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  1. 7
      README.md
  2. 18
      SETUP-PC.md
  3. BIN
      SETUP-PC.pdf
  4. 6
      SETUP-linux.md
  5. 18
      SETUP-mac.md
  6. BIN
      SETUP-mac.pdf
  7. 413
      extras/community/prototype_signal.ipynb
  8. 18
      week1/Intermediate Python.ipynb
  9. 185
      week1/community-contributions/0225_day2_exercise_by_LM.ipynb
  10. 28
      week1/community-contributions/Chat_Summary_Data/Chat_Examples/Chat1.txt
  11. 5
      week1/community-contributions/Chat_Summary_Data/Chat_Examples/Chat2.txt
  12. 19
      week1/community-contributions/Chat_Summary_Data/Chat_Examples/Chat3.txt
  13. 15
      week1/community-contributions/Chat_Summary_Data/System_Prompt.txt
  14. 123
      week1/community-contributions/Day2-Solution-Ollama.ipynb
  15. 432
      week1/community-contributions/Ollama brochure.ipynb
  16. 76
      week1/community-contributions/Text-Summarizer.ipynb
  17. 138
      week1/community-contributions/Week1-Exercise-OllamaStream-Solution.ipynb
  18. 127
      week1/community-contributions/Week1-UP-Day1-Exercise-EmailSubjectLineSuggestion.ipynb
  19. 129
      week1/community-contributions/day02_ollama_website_summarizer.ipynb
  20. 131
      week1/community-contributions/day1-airbrush-refund.ipynb
  21. 651
      week1/community-contributions/day1-election-program-qa.ipynb
  22. 195
      week1/community-contributions/day1-selenium-lama-mac.ipynb
  23. 115
      week1/community-contributions/day1_far_far_away.ipynb
  24. 167
      week1/community-contributions/day1_marketing_insights_scraper_Selenium_OpenAI.py
  25. 223
      week1/community-contributions/day1_ppt_summariser.ipynb
  26. 230
      week1/community-contributions/day1_wiki_summariser.ipynb
  27. 213
      week1/community-contributions/day2 EXERCISE_deepseek-r1.ipynb
  28. 511
      week1/community-contributions/day2 EXERCISE_ollama_llama3.ipynb
  29. 435
      week1/community-contributions/day2-EXERCISE-ollama-openai-api-website-summarizer-ITA.ipynb
  30. 186
      week1/community-contributions/day2-webpage-summarizer-ollama-gemini.ipynb
  31. 240
      week1/community-contributions/day2_exercise_cl.ipynb
  32. 663
      week1/community-contributions/day5 - brochure improved.ipynb
  33. 81
      week1/community-contributions/day5-disable-ssl.ipynb
  34. 440
      week1/community-contributions/day5-github-page-portfolio-maker.ipynb
  35. 501
      week1/community-contributions/day5-ollama.ipynb
  36. 201
      week1/community-contributions/week1 EXERCISE - TechTutor.ipynb
  37. 202
      week1/community-contributions/week1 EXERCISE_AI_techician.ipynb
  38. 308
      week1/community-contributions/week1_EXERCISE.ipynb
  39. 125
      week1/community-contributions/week1_Tutor_Ollama.ipynb
  40. 217
      week1/community-contributions/week1_day1_chat_summarizer.ipynb
  41. 208
      week1/community-contributions/wk1-day5-CHALLENGE.ipynb
  42. 163
      week1/community-contributions/wk1-day5-tutoring-exercise.ipynb
  43. 28
      week1/day1.ipynb
  44. 7
      week1/day5.ipynb
  45. 76
      week1/troubleshooting.ipynb
  46. 339
      week2/community-contributions/GPT Claude Ollama Conversation.ipynb
  47. 295
      week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb
  48. 163
      week2/community-contributions/chatbot_conversation.ipynb
  49. BIN
      week2/community-contributions/chatbot_conversation_robots.jpg
  50. 381
      week2/community-contributions/chatbot_conversation_transcript.md
  51. 448
      week2/community-contributions/d5_TravelAgent_google_STT.ipynb
  52. 361
      week2/community-contributions/day 4 w2 - course booking assistant.ipynb
  53. 1125
      week2/community-contributions/day1-3 adversarial coversation.ipynb
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  56. 899
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  57. 371
      week2/community-contributions/day1-gpt-claude-llama-interaction.ipynb
  58. 286
      week2/community-contributions/day1_Ollama&gemini_conversation.ipynb
  59. 187
      week2/community-contributions/day1_triple_conversation.ipynb
  60. 575
      week2/community-contributions/day2-different-tones.ipynb
  61. 614
      week2/community-contributions/day2-openrouterAi.ipynb
  62. 284
      week2/community-contributions/day2_message_interface_with_models.ipynb
  63. 209
      week2/community-contributions/day3 w2 -programming tutor.ipynb
  64. 377
      week2/community-contributions/day3-ollama-gradio.ipynb
  65. 18
      week2/community-contributions/day4-airlines-project-fullyCustomize.ipynb
  66. 288
      week2/community-contributions/day4-multipleTools.ipynb
  67. 448
      week2/community-contributions/day4_booking_flight_tool.ipynb
  68. 749
      week2/community-contributions/day4_with_booking_and_multiple_tools_per_message.ipynb
  69. 432
      week2/community-contributions/day5-book-flight.ipynb
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      week2/community-contributions/day5-exercise-departures-booking-and-translation.ipynb
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      week2/community-contributions/day5-voice-to-text-converter-for-hearing-impaired-people.ipynb
  72. 1108
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  73. 126
      week2/community-contributions/day5_ollama_tts-translator.ipynb
  74. 82
      week2/community-contributions/gpt-gemini-ollama.py
  75. 1
      week2/community-contributions/medical_prescription_to_google_calender/credentials.json
  76. 35
      week2/community-contributions/medical_prescription_to_google_calender/src/calendar_auth.py
  77. 64
      week2/community-contributions/medical_prescription_to_google_calender/src/create_calender_events.py
  78. 26
      week2/community-contributions/medical_prescription_to_google_calender/src/main.py
  79. 71
      week2/community-contributions/medical_prescription_to_google_calender/src/ocr.py
  80. 120
      week2/community-contributions/medical_prescription_to_google_calender/src/parsing_json.py
  81. 141
      week2/community-contributions/medical_prescription_to_google_calender/src/preprocess.py
  82. 371
      week2/community-contributions/proof_testing_agent_french.ipynb
  83. 196
      week2/community-contributions/w2d1exercise.ipynb
  84. 305
      week2/community-contributions/week 2 - multi modal StudyAI.ipynb
  85. 197
      week2/community-contributions/week2-exercise-translator.ipynb
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      week2/community-contributions/week2_code_interpreter_tool.ipynb
  87. 614
      week2/community-contributions/week2_exercise_translated_chatbot.ipynb
  88. 2
      week2/day2.ipynb
  89. 2
      week2/day3.ipynb
  90. 2
      week2/day4.ipynb
  91. 2
      week2/day5.ipynb
  92. 10
      week3/community-contributions/dataset_generator.ipynb
  93. 150
      week3/community-contributions/day4_OCR_model_exercise.ipynb
  94. 322
      week3/community-contributions/en-de-fr_dataset_generator.ipynb
  95. 54
      week3/community-contributions/telegram_bot_llm/README.md
  96. 199
      week3/community-contributions/telegram_bot_llm/tg_lb_bot.py
  97. 8
      week3/day3.ipynb
  98. 956
      week4/community-contributions/day4_extra_deepseek_and_hf_inference_provider_added.ipynb
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      week4/community-contributions/day4_with_inference_provider.ipynb
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      week4/day3.ipynb
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7
README.md

@ -6,16 +6,19 @@
I'm so happy you're joining me on this path. We'll be building immensely satisfying projects in the coming weeks. Some will be easy, some will be challenging, many will ASTOUND you! The projects build on each other so you develop deeper and deeper expertise each week. One thing's for sure: you're going to have a lot of fun along the way.
### A note before you begin
### Before you begin
I'm here to help you be most successful with your learning! If you hit any snafus, or if you have any ideas on how I can improve the course, please do reach out in the platform or by emailing me direct (ed@edwarddonner.com). It's always great to connect with people on LinkedIn to build up the community - you'll find me here:
https://www.linkedin.com/in/eddonner/
https://www.linkedin.com/in/eddonner/
And this is new to me, but I'm also trying out X/Twitter at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂
Resources to accompany the course, including the slides and useful links, are here:
https://edwarddonner.com/2024/11/13/llm-engineering-resources/
## Instant Gratification instructions for Week 1, Day 1
### Important note: see my warning about Llama3.3 below - it's too large for home computers! Stick with llama3.2! Several students have missed this warning...
We will start the course by installing Ollama so you can see results immediately!
1. Download and install Ollama from https://ollama.com noting that on a PC you might need to have administrator permissions for the install to work properly
2. On a PC, start a Command prompt / Powershell (Press Win + R, type `cmd`, and press Enter). On a Mac, start a Terminal (Applications > Utilities > Terminal).

18
SETUP-PC.md

@ -13,6 +13,17 @@ I use a platform called Anaconda to set up your environment. It's a powerful too
Having said that: if you have any problems with Anaconda, I've provided an alternative approach. It's faster and simpler and should have you running quickly, with less of a guarantee around compatibility.
### Before we begin - Heads up!
If you are relatively new to using the Command Prompt, here is an excellent [guide](https://chatgpt.com/share/67b0acea-ba38-8012-9c34-7a2541052665) with instructions and exercises. I'd suggest you work through this first to build some confidence.
There are 4 common gotchas to developing on Windows to be aware of:
1. Permissions. Please take a look at this [tutorial](https://chatgpt.com/share/67b0ae58-d1a8-8012-82ca-74762b0408b0) on permissions on Windows
2. Anti-virus, Firewall, VPN. These can interfere with installations and network access; try temporarily disabling them as needed
3. The evil Windows 260 character limit to filenames - here is a full [explanation and fix](https://chatgpt.com/share/67b0afb9-1b60-8012-a9f7-f968a5a910c7)!
4. If you've not worked with Data Science packages on your computer before, you might need to install Microsoft Build Tools. Here are [instructions](https://chatgpt.com/share/67b0b762-327c-8012-b809-b4ec3b9e7be0).
### Part 1: Clone the Repo
This gets you a local copy of the code on your box.
@ -77,9 +88,10 @@ You should see `(llms)` in your prompt, which indicates you've activated your ne
Press Win + R, type `cmd`, and press Enter
Run `python --version` to find out which python you're on. Ideally you'd be using a version of Python 3.11, so we're completely in sync.
If not, it's not a big deal, but we might need to come back to this later if you have compatibility issues.
You can download python here:
Run `python --version` to find out which python you're on.
Ideally you'd be using a version of Python 3.11, so we're completely in sync.
I believe Python 3.12 works also, but (as of Feb 2025) Python 3.13 does **not** yet work as several Data Science dependencies are not yet ready for Python 3.13.
If you need to install Python or install another version, you can download it here:
https://www.python.org/downloads/
2. Navigate to the "project root directory" by entering something like `cd C:\Users\YourUsername\Documents\Projects\llm_engineering` using the actual path to your llm_engineering project root directory. Do a `dir` and check you can see subdirectories for each week of the course.

BIN
SETUP-PC.pdf

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6
SETUP-linux.md

@ -68,6 +68,11 @@ This may take several minutes (even up to an hour for new Anaconda users). If it
You should see `(llms)` in your prompt, indicating successful activation.
In some distributions this may be required so that the llms environment is visible in jupyter lab:
`conda install ipykernel`
`python -m ipykernel install --user --name=llmenv`
3. **Start Jupyter Lab:**
From the `llm_engineering` folder, run: `jupyter lab`.
@ -125,6 +130,7 @@ Lastly, so that the kernel is visible after step (6) in jupyter lab :
From the `llm_engineering` folder, run: `jupyter lab`.
### Part 3 - OpenAI key (OPTIONAL but recommended)
Particularly during weeks 1 and 2 of the course, you'll be writing code to call the APIs of Frontier models (models at the forefront of AI).

18
SETUP-mac.md

@ -13,6 +13,14 @@ I use a platform called Anaconda to set up your environment. It's a powerful too
Having said that: if you have any problems with Anaconda, I've provided an alternative approach. It's faster and simpler and should have you running quickly, with less of a guarantee around compatibility.
### Before we begin
If you're less familiar with using the Terminal, please review this excellent [guide](https://chatgpt.com/canvas/shared/67b0b10c93a081918210723867525d2b) for some details and exercises.
If you're new to developing on your Mac, you may need to install XCode developer tools. Here are [instructions](https://chatgpt.com/share/67b0b8d7-8eec-8012-9a37-6973b9db11f5).
One "gotcha" to keep in mind: if you run anti-virus software, VPN or a Firewall, it might interfere with installations or network access. Please temporarily disable if you have problems.
### Part 1: Clone the Repo
This gets you a local copy of the code on your box.
@ -49,10 +57,11 @@ If this Part 2 gives you any problems, there is an alternative Part 2B below tha
- Download Anaconda from https://docs.anaconda.com/anaconda/install/mac-os/
- Double-click the downloaded file and follow the installation prompts. Note that it takes up several GB and take a while to install, but it will be a powerful platform for you to use in the future.
- After installing, you'll need to open a fresh, new Terminal to be able to use it (and you might even need to restart).
2. **Set up the environment:**
- Open a new Terminal (Applications > Utilities > Terminal)
- Open a **new** Terminal (Applications > Utilities > Terminal)
- Navigate to the "project root directory" using `cd ~/Documents/Projects/llm_engineering` (replace this path as needed with the actual path to the llm_engineering directory, your locally cloned version of the repo). Do `ls` and check you can see subdirectories for each week of the course.
- Create the environment: `conda env create -f environment.yml`
- Wait for a few minutes for all packages to be installed - in some cases, this can literally take 20-30 minutes if you've not used Anaconda before, and even longer depending on your internet connection. Important stuff is happening! If this runs for more than 1 hour 15 mins, or gives you other problems, please go to Part 2B instead.
@ -70,9 +79,10 @@ You should see `(llms)` in your prompt, which indicates you've activated your ne
1. **Open a new Terminal** (Applications > Utilities > Terminal)
Run `python --version` to find out which python you're on. Ideally you'd be using a version of Python 3.11, so we're completely in sync.
If not, it's not a big deal, but we might need to come back to this later if you have compatibility issues.
You can download python here:
Run `python --version` to find out which python you're on.
Ideally you'd be using a version of Python 3.11, so we're completely in sync.
I believe Python 3.12 works also, but (as of Feb 2025) Python 3.13 does **not** yet work as several Data Science dependencies are not yet ready for Python 3.13.
If you need to install Python or install another version, you can download it here:
https://www.python.org/downloads/
2. Navigate to the "project root directory" using `cd ~/Documents/Projects/llm_engineering` (replace this path with the actual path to the llm_engineering directory, your locally cloned version of the repo). Do `ls` and check you can see subdirectories for each week of the course.

BIN
SETUP-mac.pdf

Binary file not shown.

413
extras/community/prototype_signal.ipynb

@ -0,0 +1,413 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "7e2c4bbb-5e8b-4d84-9997-ecb2c349cf54",
"metadata": {},
"source": [
"## First step - generate training data from examples"
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "16cf3aa2-f407-4b95-8b9e-c3c586f67835",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pandas as pd\n",
"from datetime import datetime, timedelta,timezone\n",
"from datasets import load_dataset, Dataset\n",
"from dotenv import load_dotenv\n",
"import os\n",
"from openai import OpenAI\n",
"import json\n",
"import tiktoken\n",
"from IPython.display import display, Markdown\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "375302b6-b6a7-46ea-a74c-c2400dbd8bbe",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"load_dotenv()\n",
"\n",
"# Replace with your CoinAPI key\n",
"API_KEY = os.getenv('YOUR_COINAPI_KEY')\n",
"\n",
"# Define the base URL for CoinAPI\n",
"BASE_URL = 'https://rest.coinapi.io/v1/ohlcv/'\n",
"OLLAMA_URL = \"http://localhost:11434/v1\"\n",
"\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"# URL to fetch the OHLCV data\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "d0cc964d",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"\n",
"ollama = OpenAI(\n",
" base_url=OLLAMA_URL,\n",
" api_key='OLAMMA'\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "8a0c9fff-9eff-42fd-971b-403c99d9b726",
"metadata": {},
"outputs": [],
"source": [
"# Define the symbol and timeframe\n",
"base_data = {\n",
" 'name': 'Cardano',\n",
" 'symbol': f'BINANCE_SPOT_ADA_USDT',\n",
" 'timeframe': '1DAY',\n",
" 'time_range': 365 * 2\n",
"}\n",
"\n",
"\n",
"# Calculate the start date for one year ago\n",
"end_date = datetime.now(tz=timezone.utc)\n",
"\n",
"start_date = end_date - timedelta(days=base_data['time_range'])\n",
"\n",
"# Format the dates in the required format (ISO 8601)\n",
"start_date_str = start_date.strftime('%Y-%m-%dT%H:%M:%S')\n",
"end_date_str = end_date.strftime('%Y-%m-%dT%H:%M:%S')\n",
"\n",
"# Headers for authentication\n",
"headers = {\n",
" 'X-CoinAPI-Key': API_KEY\n",
"}\n",
"\n",
"# URL to fetch the OHLCV base_data\n",
"url = f'{BASE_URL}{base_data['symbol']}/history'\n",
"\n",
"# Request parameters\n",
"params = {\n",
" 'period_id': base_data['timeframe'],\n",
" 'time_start': start_date_str,\n",
" 'time_end': end_date_str,\n",
" 'limit': 1000 # Maximum number of records per request\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "586b07ba-5396-4c34-a696-01c8bc3597a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"729"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fetch the data\n",
"response = requests.get(url, headers=headers, params=params) \n",
"len(response.json())"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "953422d0-2e75-4d01-862e-6383df54d9e5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Timestamp Open High Low Close\n",
"724 2025-02-06 0.7325 0.7660 0.6978 0.7052\n",
"725 2025-02-07 0.7052 0.7532 0.6902 0.7072\n",
"726 2025-02-08 0.7072 0.7180 0.6815 0.7005\n",
"727 2025-02-09 0.7006 0.7160 0.6503 0.6814\n",
"728 2025-02-10 0.6815 0.7177 0.6632 0.7037\n"
]
}
],
"source": [
"# Check for successful response\n",
"if response.status_code == 200:\n",
" data = response.json()\n",
"\n",
" if data:\n",
" # Convert to DataFrame for better readability\n",
" df = pd.DataFrame(data)\n",
"\n",
" df = df[[\"time_period_start\", \"price_open\", \"price_high\", \"price_low\", \"price_close\"]]\n",
" df.columns = [\"Timestamp\", \"Open\", \"High\", \"Low\", \"Close\"]\n",
"\n",
" # Convert timestamp to readable format\n",
" df[\"Timestamp\"] = pd.to_datetime(df[\"Timestamp\"]).dt.strftime(\"%Y-%m-%d\")\n",
"\n",
" # Display the first few rows of the data\n",
" print(df.tail())\n",
" \n",
" # Convert last 365 days of data into JSON format\n",
" price_history = df.to_dict(orient=\"records\")\n",
" \n",
" else:\n",
" print('No data found for the given period.')\n",
"else:\n",
" print(f'Error fetching data: {response.status_code}, {response.text}')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "ada5ed4f",
"metadata": {},
"outputs": [],
"source": [
"def count_tokens(text, model=\"gpt-4o\"):\n",
" encoding = tiktoken.encoding_for_model(model)\n",
" return len(encoding.encode(text))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab47d974",
"metadata": {},
"outputs": [],
"source": [
" # Construct prompt \n",
"\n",
"prompt = f\"\"\"\n",
" Given the last 365 days of ${base_data['name']} OHLC data:\n",
"\n",
" {json.dumps(price_history, indent=2)}\n",
"\n",
" Analyze this data and provide a trading signal (Buy, Sell, or Hold) for today based on the trend and the price action.\n",
" Note that today is {end_date.strftime('%Y-%m-%d')}\n",
" Also, provide short term ,mid term and long term signals.\n",
" \"\"\"\n",
"num_tokens = count_tokens(prompt)\n",
"print(f\"Estimated Tokens: {num_tokens}\")\n",
"\n",
"print(prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b40fec12",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"To analyze this data, I'll use a combination of moving averages, relative strength index (RSI), and other technical indicators. Please note that this is a simplified analysis and should not be considered as professional trading advice.\n",
"\n",
"**Current Data**\n",
"\n",
"For 2025-02-10, the opening price is not available. However, we can calculate the current prices based on the historical data provided.\n",
"\n",
"Let's assume the last known close price for 2025-02-09 was $0.6815. For simplicity, let's use this as the opening price for today (2025-02-10).\n",
"\n",
"**Short-Term Signal**\n",
"\n",
"For a short-term signal, I'll use a simple moving average crossover system.\n",
"\n",
"* Short-Term Moving Average (20 days): $0.6922\n",
"* Short-Term Moving Average (10 days): $0.6747\n",
"\n",
"Since the 20-day MA ($0.6922) is above the 10-day MA ($0.6747), we can conclude that **Buy** in this timeframe.\n",
"\n",
"**Mid-Term Signal**\n",
"\n",
"For a mid-term signal, I'll use RSI.\n",
"\n",
"* Current Price: $0.6815\n",
"* Overbought Region: 70-80\n",
"* Oversold Region: 20-50\n",
"\n",
"The current price ($0.6815) is at the lower end of the oversold region (20-50), indicating a potential buying opportunity.\n",
"\n",
"Since RSI values are not provided for the entire dataset, we'll use an RSI value of 30 (midpoint of the low and high values). At $0.6815, RSI is approximately 34.\n",
"\n",
"* Mid-Term Moving Average: Not available\n",
"* Mid-Term Momentum: Rising\n",
"\n",
"Considering the oversold region and rising momentum, **Hold** is a reasonable mid-term strategy for today.\n",
"\n",
"**Long-Term Signal**\n",
"\n",
"For a long-term signal, I'll use the overall trend direction based on historical data.\n",
"\n",
"The dataset shows an upward trend (average True Range, AtR, value has been increasing). From 2025-02-03 to 2025-02-09, there were 6 consecutive increases in this dataset. That's a strong positive trend.\n",
"\n",
"Since there are no obvious signs of weakness in the long-term data or divergence with other trends (like 50-day MA), I recommend **Hold** for an extended holding period, keeping an eye on RSI values and adjusting positions as needed to stay ahead of potential price drops.\n",
"\n",
"**Summary**\n",
"\n",
"* Short-Term: **Buy**\n",
"* Mid-Term: **Hold**\n",
"* Long-Term: **Hold**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def get_response(prompt):\n",
" new_response = ollama.chat.completions.create(model=\"llama3.2\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": f\"You are a trading analyst providing Buy/Sell/Hold signals based on ${base_data['name']} price history.Note that today is {end_date.strftime('%Y-%m-%d')}\"},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ],\n",
" stream=True,\n",
" max_tokens=5500\n",
" )\n",
" markdown_content = \"\"\n",
" \n",
" # Stream response and accumulate markdown content\n",
" for chunk in new_response:\n",
" content = chunk.choices[0].delta.content or ''\n",
" markdown_content += content\n",
" \n",
" # Clear output and display updated markdown\n",
" display(Markdown(markdown_content), clear=True)\n",
" \n",
" yield content\n",
"\n",
"# Call the function and consume the generator to start streaming\n",
"for _ in get_response(prompt):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "ba09436c",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"# $Cardano Trading Analysis for 2025-02-10\n",
"\n",
"### **Current Price Analysis**\n",
"- **Open:** 0.6815\n",
"- **High:** 0.7177\n",
"- **Low:** 0.6632\n",
"- **Close:** 0.7037\n",
"\n",
"The price of $Cardano closed 3.59% higher than the previous day's close. This suggests a potential bullish reversal following a downward trend observed over the last few days. However, the volatility in the high-low range reflects uncertainty in the market.\n",
"\n",
"### **Trend Overview**\n",
"- **Short-term:** \n",
" - The recent price action indicates a possible recovery as we see an upward close. The price is currently attempting to break resistance, but the last few days exhibited mixed movements (e.g., a decrease before the recent increase). \n",
"- **Mid-term:**\n",
" - Over the past month, $Cardano has experienced significant volatility. While it reached its peak at around 1.079 earlier in January, the subsequent decline indicates selling pressure in the mid-term. A consolidation phase appears as buyers are trying to push the price back up.\n",
"- **Long-term:**\n",
" - Over the past year, $Cardano has shown high volatility and a fluctuating price range, but it has generally been trending downwards since its recent highs. \n",
"\n",
"### **Trading Signals**\n",
"- **Short-term Signal:** **Buy**\n",
" - The recent upward price movement along with a closing above 0.7000 indicates potential upward momentum. Short-term traders may consider buying into this recovery signal.\n",
"\n",
"- **Mid-term Signal:** **Hold**\n",
" - Within the last month, while recovery is in place, it is prudent to wait for confirmation of sustained upward movement before committing larger positions. A hold is advised to monitor the situation.\n",
"\n",
"- **Long-term Signal:** **Sell**\n",
" - Given that the longer-term trends show a downward trajectory since peaking at higher prices, long-term holders might consider selling or reducing positions, especially if the price fails to stay above recent resistance levels.\n",
"\n",
"### **Conclusion**\n",
"Today’s price action indicates a bullish sentiment in the short term but still reflects uncertainty in the mid and long-term periods. It would be wise for traders to remain cautious and adjust positions as the market dynamics evolve further. Always consider your risk management strategies when deciding to enter or exit positions."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def get_response(prompt):\n",
" new_response = openai.chat.completions.create(model=\"gpt-4o-mini\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": f\"You are a trading analyst providing Buy/Sell/Hold signals based on ${base_data['name']} price history. Format your response in markdown.Note that today is {end_date.strftime('%Y-%m-%d')}\"},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ],\n",
" stream=True,\n",
" max_tokens=5500\n",
" )\n",
" \n",
" # Initialize markdown cell output\n",
" markdown_content = \"\"\n",
" \n",
" # Stream response and accumulate markdown content\n",
" for chunk in new_response:\n",
" content = chunk.choices[0].delta.content or ''\n",
" markdown_content += content\n",
" \n",
" # Clear output and display updated markdown\n",
" display(Markdown(markdown_content), clear=True)\n",
" \n",
" yield content\n",
"\n",
"# Call the function and consume the generator to start streaming\n",
"for _ in get_response(prompt):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f52bcc0a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

18
week1/Intermediate Python.ipynb

@ -50,6 +50,22 @@
"https://chatgpt.com/share/673b553e-9d0c-8012-9919-f3bb5aa23e31"
]
},
{
"cell_type": "markdown",
"id": "f9e0f8e1-09b3-478b-ada7-c8c35003929b",
"metadata": {},
"source": [
"## With this in mind - understanding NameErrors in Python\n",
"\n",
"It's quite common to hit a NameError in python. With foundational knowledge, you should always feel equipped to debug a NameError and get to the bottom of it.\n",
"\n",
"If you're unsure how to fix a NameError, please see this [initial guide](https://chatgpt.com/share/67958312-ada0-8012-a1d3-62b3a5fcbbfc) and this [second guide with exercises](https://chatgpt.com/share/67a57e0b-0194-8012-bb50-8ea76c5995b8), and work through them both until you have high confidence.\n",
"\n",
"There's some repetition here, so feel free to skip it if you're already confident.\n",
"\n",
"## And now, on to the code!"
]
},
{
"cell_type": "code",
"execution_count": 1,
@ -57,7 +73,7 @@
"metadata": {},
"outputs": [],
"source": [
"# Next let's create some things:\n",
"# First let's create some things:\n",
"\n",
"fruits = [\"Apples\", \"Bananas\", \"Pears\"]\n",
"\n",

185
week1/community-contributions/0225_day2_exercise_by_LM.ipynb

@ -0,0 +1,185 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fef36918-109d-41e3-8603-75ff81b42379",
"metadata": {},
"source": [
"# Solution for exercise day 2 - slight modification: model is a parameter also - display_summary(\"deepseek-r1:1.5b\",\"https://yoururl\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b50349ac-93ea-496b-ae20-bd72a93bb138",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "edd073c7-8444-4a0d-b84e-4b2ed0ee7f35",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"#MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e3a6e1a-e4c7-4448-9852-1b6ba2bd8d66",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae3752ca-3a97-4d6a-ac84-5b75ebfb50ed",
"metadata": {},
"outputs": [],
"source": [
"# Define the system prompt \n",
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48b5240f-7617-4e51-a320-cba9650bec84",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of websites:\n",
"\n",
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f7d84f0-60f2-4cbf-b4d1-173a79fe3380",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25520a31-c857-4ed5-86da-50dfe5fab7bb",
"metadata": {},
"outputs": [],
"source": [
"def summarize(model,url):\n",
" website = Website(url)\n",
" payload = {\n",
" \"model\": model,\n",
" \"messages\": messages_for(website),\n",
" \"stream\": False\n",
" }\n",
" response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
" return response.json()['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "430776ed-8516-43a9-8a22-618d9080f2e1",
"metadata": {},
"outputs": [],
"source": [
"# A function to display this nicely in the Jupyter output, using markdown\n",
"def display_summary(model,url):\n",
" summary = summarize(model,url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2b05c1f-e4a2-4f65-bd6d-634d72e38b6e",
"metadata": {},
"outputs": [],
"source": [
"#!ollama pull deepseek-r1:1.5b"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01513f8a-15b7-4053-bfe4-44b36e5494d1",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"deepseek-r1:1.5b\",\"https://www.ipma.pt\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

28
week1/community-contributions/Chat_Summary_Data/Chat_Examples/Chat1.txt

@ -0,0 +1,28 @@
Client: Hello I would like to order a pizza
Restaurant: Sure. What pizza would you like to order from our menu?
Client: Chicken Ranch
Restaurant: I am so sorry, but chicken ranch is currently unavailable on our menu
Client: AHHHHH. Do you have chicken BBQ?
Restaurant: Yes! Do you want it small, medium, or large?
Client: Medium
Restaurant: Ok. This will be 180 LE
Client: Thanks
Restaurant: Anytime.
Client: AHHHH I forgot. I want to add a new chicken BBQ pizza
Restaurant: No problem. Do you also want it medium?
Client: Yes
Restaurant: Okay this will be 380 LE
Client: Okay Thanks
Client: Wait a minute. Isn't 180 * 2 = 360?
Restaurant: It seems that there might be a misunderstanding. We add an extra 20 LE for every extra pizza ordered.
Client: NOBODY TOLD ME THAT.. AND WHY ON EARTH WOULD YOU DO SOMETHING LIKE THAT?
Restaurant: We are sorry but this is our policy.
Client: Okay then I don't want your pizza.
Restaurant: We are so sorry to hear that. We can make a 10% discount on the total price so it would be 342 LE
Client: Fine
Restaurant: Thank you for ordering
Restaurant: Pizza is delivered. How is your experience?
Client: Your pizza doesn't taste good
Restaurant: We are so sorry to hear that. Do you have any suggestions you would like to make?
Client: Make good pizza
Restaurant: Thanks for your review. We will make sure to improve our pizza in the future. Your opinion really matters.

5
week1/community-contributions/Chat_Summary_Data/Chat_Examples/Chat2.txt

@ -0,0 +1,5 @@
Client: Hello I would like to order a chicken ranch pizza
Restaurant: I am so sorry, but chicken ranch is currently unavailable on our menu
Client: Okay thanks
Restaurant: Would you like to order something else?
Client: No thank you

19
week1/community-contributions/Chat_Summary_Data/Chat_Examples/Chat3.txt

@ -0,0 +1,19 @@
Client: Hello. What is the most selling pizza on your menu?
Restaurant: Hello! Chicken Ranch pizza is our most selling pizza. Also our special pepperoni pizza got some amazing reviews
Client: Okay. I want to order a pepperoni pizza
Restaurant: Sure. Do you want it small, medium, or large?
Client: Large
Restaurant: Okay. This will be 210 LE. Would you like to order something else?
Client: Yes. Do you have onion rings?
Restaurant: Yes
Client: Okay I would like to add onion rings.
Restaurant: Sure. This will be 250 LE
Client: Thanks
Restaurant: Anytime
Client: I have been waiting for too long and the order hasn't arrived yet
Restaurant: Sorry to hear that. But it appears that the order is on its way to you.
Restaurant: The order is supposed to be arrived by now.
Client: Yes it is arrived.
Restaurant: How is your experience?
Client: Your pizza tastes soooooo good. The order took too long to arrive but when I tasted the pizza, I was really enjoying it and forgot everything about the delay.
Restaurant: We are so glad to hear that

15
week1/community-contributions/Chat_Summary_Data/System_Prompt.txt

@ -0,0 +1,15 @@
You are an assistant working for the customer service department in a pizza restaurant.
You are to receive a chat between a client and the restaurant's customer service.
You should generate your responses based on the following criteria:
- What did the client order?
- How much did it cost?
- If the client changed their mind just keep their final order and the final cost
- Mention the client's experience only if they ordered anything as follows: (Positive/Negative/Neutral/Unknown)
- If the client did not order anything do not mention their sentiment or experience
- If the client's experience is positive or negative only, provide a brief summary about their sentiment
- Do not provide brief summary about their sentiment if their experience was neutral or unknown.
- Your answers should be clear, straight to the point, and do not use long sentences
- Your answers should be displayed in bullet points
- Your answers should be displayed in markdown
- If the client did not order anything provide a brief summary why that might happened
- Do not mention cost if the client did not order anything

123
week1/community-contributions/Day2-Solution-Ollama.ipynb

@ -0,0 +1,123 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
"metadata": {},
"source": [
"# Day 2 EXERCISE Solution:\n",
"\n",
"Upgraded day 1 project that scrapes and summarizes any webpage using an Open Source model running locally via Ollama instead of OpenAI\n",
"\n",
"## Note:-\n",
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative: \n",
"1. Run `ollama pull llama3.2:1b` from a Terminal or Powershell\n",
"2. **Ctrl + /** to comment this code line below: `MODEL = \"llama3.2\"`\n",
"3. same **Ctrl + /** to uncomment: `MODEL = \"llama3.2:1b\"`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
"metadata": {},
"outputs": [],
"source": [
"# imports:-\n",
"\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"import ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
"metadata": {},
"outputs": [],
"source": [
"# Constants:-\n",
"\n",
"# MODEL = \"llama3.2\"\n",
"MODEL = \"llama3.2:1b\"\n",
"# MODEL = \"deepseek-r1:1.5b\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
"metadata": {},
"outputs": [],
"source": [
"class Website:\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
"\n",
"\n",
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
" and provides a short summary, ignoring text that might be navigation related. \\\n",
" Respond in markdown.\"\n",
"\n",
"\n",
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
" please provide a short summary of this website in markdown. \\\n",
" If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt\n",
"\n",
"\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]\n",
"\n",
"\n",
"def summary(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model = MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return display(Markdown(response['message']['content']))\n",
"\n",
"\n",
"summary(\"https://edwarddonner.com\")\n",
"# summary(\"https://cnn.com\")\n",
"# summary(\"https://anthropic.com\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

432
week1/community-contributions/Ollama brochure.ipynb

@ -0,0 +1,432 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 52,
"id": "b56a950c-db41-4575-bef9-0fa651dea363",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"import json\n",
"import ollama\n",
"from typing import List\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display,clear_output\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ec875db-0f6a-4eec-a3b6-eae4b71a4b89",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "227cd07c-98a4-463b-94ad-94e33d04944b",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d5c5e40-c010-4102-8359-899f988185fb",
"metadata": {},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"ed.links"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f0b5d71-487c-47a5-ace6-8e02465ed452",
"metadata": {},
"outputs": [],
"source": [
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
"link_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6550325-5160-42c9-b7e7-980b504cd096",
"metadata": {},
"outputs": [],
"source": [
"print(link_system_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2db4ccc6-5c35-4775-a5b2-4b86e4c73808",
"metadata": {},
"outputs": [],
"source": [
"def get_links_user_prompt(website):\n",
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
"Do not include Terms of Service, Privacy, email links.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"\\n\".join(website.links)\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8af511c7-5a74-4d1a-b763-b31370e70cff",
"metadata": {},
"outputs": [],
"source": [
"print(get_links_user_prompt(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3b7fb61-ca15-4eab-b017-b0fe5cce46fd",
"metadata": {},
"outputs": [],
"source": [
"def get_links(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ], format = \"json\" #Define format as json!\n",
" )\n",
" result = response['message']['content']\n",
"\n",
" return json.loads(result)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7816d393-620d-4c53-913e-4ec130b2baba",
"metadata": {},
"outputs": [],
"source": [
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n",
"\n",
"anthropic = Website(\"https://anthropic.com\")\n",
"anthropic.links"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f32ceccb-1d45-41a3-a5c1-fb2e6cd76afe",
"metadata": {},
"outputs": [],
"source": [
"get_links(\"https://anthropic.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7ec4727-e897-473c-a657-e74f6999c974",
"metadata": {},
"outputs": [],
"source": [
"def get_all_details(url):\n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
" links = get_links(url)\n",
" print(\"Found links:\", links)\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += Website(link[\"url\"]).get_contents()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7acde0c5-1af2-4e8e-9303-e2a98ec9cdbb",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(get_all_details(\"https://anthropic.com\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a2e2b1d-eb55-4bfb-bf55-5e8c87db0d96",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"Include details of company culture, customers and careers/jobs if you have the information.\"\n",
"\n",
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n",
"\n",
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"# Include details of company culture, customers and careers/jobs if you have the information.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8eac1719-7f94-4460-bc4a-0c9c93bb17a5",
"metadata": {},
"outputs": [],
"source": [
"def get_brochure_user_prompt(company_name, url):\n",
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2e312f6-01c5-4e57-9134-fb4aa447d155",
"metadata": {},
"outputs": [],
"source": [
"get_brochure_user_prompt(\"Anthropic\", \"https://anthropic.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b05cbab-f0d2-4a9e-8b8c-c868a036e9cd",
"metadata": {},
"outputs": [],
"source": [
"def create_brochure(company_name, url):\n",
" response = ollama.chat(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ]\n",
" )\n",
" result = response[\"message\"][\"content\"]\n",
" display(Markdown(result))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91ede0c0-daf2-42ef-9d31-749afb9d5352",
"metadata": {},
"outputs": [],
"source": [
"create_brochure(\"Anthropic\", \"https://anthropic.com\")"
]
},
{
"cell_type": "markdown",
"id": "afb4aeee-5108-42a7-a1c1-5bad254b7e8b",
"metadata": {},
"source": [
"# Final omprovement\n",
"\n",
"getting a typewriter animation"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "177de611-1cb1-49e2-b7ea-8d01191af3ee",
"metadata": {},
"outputs": [],
"source": [
"def create_brochure(company_name, url):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ]\n",
"\n",
" display_markdown = display(Markdown(\"\"), display_id=True) # Initialize Markdown display\n",
" response_text = \"\"\n",
"\n",
" for chunk in ollama.chat(model=MODEL, messages=messages, stream=True): # Ensure stream=True (not a string)\n",
" response_text += chunk['message']['content']\n",
" clear_output(wait=True) # Clear previous output to create a streaming effect\n",
" display_markdown.update(Markdown(response_text)) # Update Markdown dynamically\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "a1971d81-fc7f-4ed1-97a0-7ef5e8ed332a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found links: {'links': [{'type': 'About page', 'url': 'https://www.anthropic.com/company'}, {'type': 'Careers page', 'url': 'https://www.anthropic.com/careers'}, {'type': 'Company page', 'url': 'https://www.anthropic.com/'}, {'type': 'Research page', 'url': 'https://www.anthropic.com/research'}, {'type': 'Twitter profile', 'url': 'https://twitter.com/AnthropicAI'}, {'type': 'LinkedIn company page', 'url': 'https://www.linkedin.com/company/anthropicresearch'}, {'type': 'YouTube channel', 'url': 'https://www.youtube.com/@anthropic-ai'}]}\n"
]
},
{
"data": {
"text/markdown": [
"**Anthropic Brochure**\n",
"======================\n",
"\n",
"**Mission Statement**\n",
"-------------------\n",
"\n",
"Anthropic is an AI safety and research company dedicated to building reliable, interpretable, and steerable AI systems that benefit humanity in the long run.\n",
"\n",
"**Company Overview**\n",
"--------------------\n",
"\n",
"Anthropic is headquartered in San Francisco and brings together a diverse team of researchers, engineers, policy experts, and business leaders with experience spanning various disciplines. Our mission is to conduct frontier AI research, develop and apply safety techniques, and deploy the resulting systems via partnerships and products.\n",
"\n",
"**Research Focus**\n",
"-----------------\n",
"\n",
"Anthropic conducts cutting-edge AI research across various modalities, exploring novel and emerging safety research areas such as interpretability, RL from human feedback, policy, and societal impacts analysis. Our research aims to advance the field of AI safety and inform our product development.\n",
"\n",
"**Product Portfolio**\n",
"---------------------\n",
"\n",
"Our flagship product is Claude, a highly intelligent AI model that enables customers to build custom applications and experiences using our API. We also offer various enterprise solutions, including Claude for Enterprise, designed to meet the needs of large organizations.\n",
"\n",
"**Customer Base**\n",
"-----------------\n",
"\n",
"Anthropic serves a diverse range of customers, including businesses, nonprofits, civil society groups, and their clients around the globe. Our commitment to safety and reliability has earned us a reputation as a trusted partner in the AI industry.\n",
"\n",
"**Values and Culture**\n",
"----------------------\n",
"\n",
"At Anthropic, we value:\n",
"\n",
"* **Acting for the global good**: We strive to make decisions that maximize positive outcomes for humanity in the long run.\n",
"* **Holding light and shade**: We acknowledge the potential risks of AI and approach our work with caution and transparency.\n",
"\n",
"**Join Our Team**\n",
"-----------------\n",
"\n",
"We're a collaborative team of researchers, engineers, policy experts, and business leaders passionate about building safer AI systems. Join us to be part of this exciting journey and contribute your skills and expertise to shaping the future of AI.\n",
"\n",
"**Careers**\n",
"------------\n",
"\n",
"Check our website for open roles and learn more about our company culture, benefits, and career opportunities.\n",
"\n",
"[Learn More](link)\n",
"\n",
"**Get in Touch**\n",
"-----------------\n",
"\n",
"Stay up-to-date with the latest news and announcements from Anthropic. Follow us on Twitter, LinkedIn, or YouTube to join the conversation and stay informed.\n",
"\n",
"[Twitter](link)\n",
"[LinkedIn](link)\n",
"[YouTube](link)"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"create_brochure(\"Anthropic\", \"https://anthropic.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c33277a4-84f1-447c-a66e-eb7e2af42d2a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

76
week1/community-contributions/Text-Summarizer.ipynb

@ -0,0 +1,76 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 1) Import Required Libraries \n",
"\n",
"import requests\n",
"import gradio as gr\n",
"\n",
"# Deepseek only uses abstract summarization\n",
"# This tool use DeepSeek API Endpoint\n",
"\n",
"# 2) Define the DeepSeek API Endpoint\n",
"\n",
"OLLAMA_URL = \"http://localhost:11434/api/generate\"\n",
"\n",
"# 3) Define the Summarization Function which can retrieve Information\n",
"\n",
"def summarize_text(text):\n",
" payload = {\n",
" \"model\": \"deepseek-r1\", #Here you can load whatever the model you have in your ollama(ex:deepseek-r1:1.5b,7b,8b,14b) I used 7b model here \n",
" \"prompt\": f\"Summarize the following text in **5 bullet points**:\\n\\n{text}\", #The prompt is here for tell commands for the llm to act \n",
" \"stream\": False # Ensures the response is returned as a whole, not streamed\n",
" }\n",
"\n",
" response = requests.post(OLLAMA_URL, json=payload) #Send Requests to deepseekAPI\n",
"\n",
" if response.status_code == 200: #if server run correctly it return the result or it will give error\n",
" return response.json().get(\"response\", \"No summary generated.\")\n",
" else:\n",
" return f\"Error: {response.text}\"\n",
"\n",
"# 4) Create Gradio interface to design \n",
"interface = gr.Interface(\n",
" fn=summarize_text,\n",
" inputs=gr.Textbox(lines=10, placeholder=\"Enter text to summarize\"),\n",
" outputs=gr.Textbox(label=\"Summarized Text\"),\n",
" #theme='NoCrypt/miku', #Theme for the Interface I used Hatsune Miku from HF \n",
" title=\"AI-Powered Text Summarizer\",\n",
" description=\"Enter a long text and DeepSeek AI will generate a concise summary.\"\n",
")\n",
"\n",
"# Launch the web app\n",
"if __name__ == \"__main__\":\n",
" interface.launch()\n",
"\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

138
week1/community-contributions/Week1-Exercise-OllamaStream-Solution.ipynb

@ -0,0 +1,138 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# End of week 1 exercise Solution Ollama with streaming\n",
"\n",
"A tool that takes a technical question, and responds with an explanation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"\n",
"import ollama\n",
"import requests\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"MODEL_LLAMA = 'llama3.2'\n",
"MODEL_LLAMA1b = \"llama3.2:1b\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [],
"source": [
"# Environment\n",
"\n",
"system_prompt = \"\"\"\n",
"You are an assistant that takes a technical question and respond with an explanation.\n",
"\"\"\"\n",
"\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\"\n",
"\n",
"question2 = \"\"\"\n",
"What is the purpose of using yield from in the following code, and how does it differ from a standard for loop with yield?\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\"\n",
"\n",
"user_prompt = \"Answer these two questions in detail please, Question1:\" + question + \"Question2:\" + question2\n",
"\n",
"def message():\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# Llama 3.2 answer, with streaming\n",
"\n",
"def llama():\n",
" response = ollama.chat(\n",
" model = MODEL_LLAMA,\n",
" messages = message(),\n",
" stream =True\n",
" )\n",
" full_response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in response:\n",
" content = chunk.get(\"message\", {}).get(\"content\", \"\")\n",
" if content:\n",
" full_response += content\n",
" display_handle.update(Markdown(full_response))\n",
"llama()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "342a470c-9aab-4051-ad21-514dceec76eb",
"metadata": {},
"outputs": [],
"source": [
"# Llama 3.2:1b answer\n",
"\n",
"def llama():\n",
" response = ollama.chat(\n",
" model = MODEL_LLAMA1b,\n",
" messages = message()\n",
" )\n",
" return display(Markdown(response['message']['content']))\n",
"\n",
"llama()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

127
week1/community-contributions/Week1-UP-Day1-Exercise-EmailSubjectLineSuggestion.ipynb

@ -0,0 +1,127 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "39e3e763-9b00-49eb-aead-034a2d0517a7",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3bb5e2a-b70f-42ba-9f22-030a9c6bc9d1",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "994f51fb-eab3-45a2-847f-87aebb92b17a",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"\n",
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8125c6d-c884-4f65-b477-cab155e29ce3",
"metadata": {},
"outputs": [],
"source": [
"# Step 1: Create your prompts\n",
"\n",
"system_prompt = \"You are an AI that suggests short and relevant subject lines for emails based on their content.\"\n",
"user_prompt = \"\"\"\n",
"Here is the content of an email:\n",
"\n",
"Dear Team,\n",
"\n",
"I hope you're all doing well. I wanted to remind you that our next project meeting is scheduled for this Friday at 3 PM. We will be discussing our progress and any blockers. Please make sure to review the latest updates before the meeting.\n",
"\n",
"Best, \n",
"John\n",
"\"\"\"\n",
"\n",
"# Step 2: Make the messages list\n",
"\n",
"messages = [ {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}] # fill this in\n",
"\n",
"# Step 3: Call OpenAI\n",
"\n",
"response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages=messages\n",
")\n",
"\n",
"# Step 4: print the result\n",
"\n",
"print(\"Suggested Subject Line:\", response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1010ac80-1ee8-432f-aa3f-12af419dc23a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

129
week1/community-contributions/day02_ollama_website_summarizer.ipynb

@ -0,0 +1,129 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "d25b0aef-3e5e-4026-90ee-2b373bf262b7",
"metadata": {},
"outputs": [],
"source": [
"# Step 0: Import Libraries\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"import ollama\n",
"from openai import OpenAI\n",
"import requests\n",
"\n",
"# Step 1: Set Constants and Variables\n",
"print(\"[INFO] Setting constants and variable ...\")\n",
"WEBSITE_URL = \"https://arxiv.org/\"\n",
"MODEL = \"llama3.2\"\n",
"approaches = [\"local-call\", \"python-package\", \"openai-python-library\"]\n",
"approach = approaches[2]\n",
"\n",
"# Step 1: Scrape Website\n",
"print(\"[INFO] Scraping website ...\")\n",
"url_response = requests.get(\n",
" url=WEBSITE_URL,\n",
" headers={\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"}\n",
" )\n",
"soup = BeautifulSoup(\n",
" markup=url_response.content,\n",
" features=\"html.parser\"\n",
" )\n",
"website_title = soup.title.string if soup.title else \"No title found!!!\"\n",
"for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
"website_text = soup.body.get_text(\n",
" separator=\"\\n\",\n",
" strip=True\n",
" )\n",
"\n",
"# Step 2: Create Prompts\n",
"print(\"[INFO] Creating system prompt ...\")\n",
"system_prompt = \"You are an assistant that analyzes the contents of a \\\n",
" website and provides a short summary, ignoring text that might be \\\n",
" navigation related. Respond in markdown.\"\n",
"\n",
"print(\"[INFO] Creating user prompt ...\")\n",
"user_prompt = f\"You are looking at a website titled {website_title}\"\n",
"user_prompt += \"\\nBased on the contents of the website, please provide \\\n",
" a short summary of this website in markdown. If the website \\\n",
" includes news or announcements, summarize them, too. The contents \\\n",
" of this website are as follows:\\n\\n\"\n",
"user_prompt += website_text\n",
"\n",
"# Step 3: Make Messages List\n",
"print(\"[INFO] Making messages list ...\")\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
"]\n",
"\n",
"# Step 4: Call Model and Print Results\n",
"if approach == \"local-call\":\n",
" response = requests.post(\n",
" url=\"http://localhost:11434/api/chat\",\n",
" json={\n",
" \"model\": MODEL,\n",
" \"messages\": messages,\n",
" \"stream\": False\n",
" },\n",
" headers={\"Content-Type\": \"application/json\"}\n",
" )\n",
" print(\"[INFO] Printing result ...\")\n",
" display(Markdown(response.json()[\"message\"][\"content\"]))\n",
"elif approach == \"python-package\":\n",
" response = ollama.chat(\n",
" model=MODEL,\n",
" messages=messages,\n",
" stream=False\n",
" )\n",
" print(\"[INFO] Printing result ...\")\n",
" display(Markdown(response[\"message\"][\"content\"]))\n",
"elif approach == \"openai-python-library\":\n",
" ollama_via_openai = OpenAI(\n",
" base_url=\"http://localhost:11434/v1\",\n",
" api_key=\"ollama\"\n",
" )\n",
" response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages\n",
" )\n",
" print(\"[INFO] Printing result ...\")\n",
" display(Markdown(response.choices[0].message.content))\n",
"else:\n",
" raise ValueError(f\"[INFO] Invalid approach! Please select an approach from {approaches} and try again.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0a6676e-fb43-4725-9389-2acd74c13c4e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

131
week1/community-contributions/day1-airbrush-refund.ipynb

@ -0,0 +1,131 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "f3c6d883-58a2-47de-823f-3c7430cffcc9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"Airbrush or Air Bust? Let's Find Out!\"\n"
]
}
],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"\n",
"openai = OpenAI()\n",
"\n",
"# Step 1: Create your prompts\n",
"\n",
"system_prompt = \"You will take the body of an email and evaluate it to suggest a brief snarky subject\"\n",
"user_prompt = \"\"\"\n",
"Dear Air Brush Customer Service Team,\n",
"\n",
"I hope this message finds you well. I am writing to formally lodge a complaint regarding the airbrush product I purchased from your store. Unfortunately, the product I received is defective and does not meet the quality standards as advertised.\n",
"\n",
"Below are the details of my issue:\n",
"\n",
"Order Number: #12345\n",
"\n",
"Product Name: Air Brush model 123\n",
"\n",
"Date of Purchase: 18/1/2025\n",
"\n",
"Issue Description:\n",
"Defective Nozzle: The nozzle of the airbrush is clogged and does not allow proper airflow, making it impossible to use.\n",
"\n",
"Inconsistent Spray Pattern: Even after multiple attempts to clean and adjust the settings, the spray pattern is uneven and inconsistent.\n",
"\n",
"Leakage: The airbrush leaks air and paint from the joints, which is a significant safety hazard.\n",
"\n",
"Build Quality: The overall build quality of the product feels subpar, with loose fittings and a flimsy trigger mechanism.\n",
"\n",
"Steps Taken:\n",
"I followed the user manual and cleaning instructions provided, but the issues persist.\n",
"\n",
"I also reached out to your technical support team on [Date] but have not received a resolution.\n",
"\n",
"Expectation:\n",
"Given the defective nature of the product, I would like to request a full refund for the item. Alternatively, if a refund is not possible, I would appreciate a replacement with a fully functional unit.\n",
"\n",
"Attachments:\n",
"I have attached photos and a video demonstrating the issues for your reference.\n",
"\n",
"Copies of the invoice and order confirmation are also attached for your convenience.\n",
"\n",
"Request for Resolution:\n",
"Kindly let me know the next steps to process the refund or replacement. I would appreciate a prompt response within [X business days, e.g., 3-5 business days] to resolve this matter.\n",
"\n",
"Thank you for your attention to this issue. I trust that you will handle this matter professionally and ensure customer satisfaction.\n",
"\n",
"Looking forward to your swift response.\n",
"\n",
"Best regards,\n",
"Oya YILDIZ\n",
"İstanbul\n",
"Turkey\n",
"\"\"\"\n",
"\n",
"# Step 2: Make the messages list\n",
"\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
"] # fill this in\n",
"\n",
"# Step 3: Call OpenAI\n",
"\n",
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
"\n",
"# Step 4: print the result\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d9b655de-e8c3-4136-b6a6-2fb0ce01c364",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

651
week1/community-contributions/day1-election-program-qa.ipynb

@ -0,0 +1,651 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {},
"source": [
"# Instant Gratification\n",
"\n",
"## Your first Frontier LLM Project!\n",
"\n",
"Let's build a useful LLM solution - in a matter of minutes.\n",
"\n",
"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n",
"\n",
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n",
"\n",
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n",
"\n",
"## If you're new to Jupyter Lab\n",
"\n",
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n",
"\n",
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n",
"\n",
"## If you'd prefer to work in IDEs\n",
"\n",
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n",
"If you'd prefer to work in VSCode, [here](https://chatgpt.com/share/676f2e19-c228-8012-9911-6ca42f8ed766) are instructions from an AI friend on how to configure it for the course.\n",
"\n",
"## If you'd like to brush up your Python\n",
"\n",
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n",
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n",
"\n",
"## I am here to help\n",
"\n",
"If you have any problems at all, please do reach out. \n",
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)\n",
"\n",
"## More troubleshooting\n",
"\n",
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n",
"\n",
"## If this is old hat!\n",
"\n",
"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n",
"\n",
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Please read - important note</h2>\n",
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you do this with me, either at the same time, or (perhaps better) right afterwards. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n",
" </td>\n",
" </tr>\n",
"</table>\n",
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n",
" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
]
},
{
"cell_type": "markdown",
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254",
"metadata": {},
"source": [
"# Connecting to OpenAI\n",
"\n",
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n",
"\n",
"## Troubleshooting if you have problems:\n",
"\n",
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n",
"\n",
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n",
"\n",
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n",
"\n",
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"\n",
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions"
]
},
{
"cell_type": "markdown",
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91",
"metadata": {},
"source": [
"# Let's make a quick call to a Frontier model to get started, as a preview!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a",
"metadata": {},
"outputs": [],
"source": [
"# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n",
"\n",
"message = \"Tell me about a way to analyse what people do in a video clip.\"\n",
"#response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\":\"user\", \"content\":message}])\n",
"#print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "2aa190e5-cb31-456a-96cc-db109919cd78",
"metadata": {},
"source": [
"## OK onwards with our first project"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5e793b2-6775-426a-a139-4848291d0463",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
"metadata": {},
"outputs": [],
"source": [
"# Let's try one out. Change the website and add print statements to follow along.\n",
"\n",
"ed = Website(\"https://edwarddonner.com\")\n",
"print(ed.title)\n",
"print(ed.text)"
]
},
{
"cell_type": "markdown",
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1",
"metadata": {},
"source": [
"## Types of prompts\n",
"\n",
"You may know this already - but if not, you will get very familiar with it!\n",
"\n",
"Models like GPT4o have been trained to receive instructions in a particular way.\n",
"\n",
"They expect to receive:\n",
"\n",
"**A system prompt** that tells them what task they are performing and what tone they should use\n",
"\n",
"**A user prompt** -- the conversation starter that they should reply to"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
"metadata": {},
"outputs": [],
"source": [
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
"\n",
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of websites:\n",
"\n",
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt_for(ed))"
]
},
{
"cell_type": "markdown",
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc",
"metadata": {},
"source": [
"## Messages\n",
"\n",
"The API from OpenAI expects to receive messages in a particular structure.\n",
"Many of the other APIs share this structure:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n",
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n",
"]\n",
"\n",
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce",
"metadata": {},
"outputs": [],
"source": [
"# To give you a preview -- calling OpenAI with system and user messages:\n",
"\n",
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47",
"metadata": {},
"source": [
"## And now let's build useful messages for GPT-4o-mini, using a function"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
"metadata": {},
"outputs": [],
"source": [
"# See how this function creates exactly the format above\n",
"\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c",
"metadata": {},
"outputs": [],
"source": [
"# Try this out, and then try for a few more websites\n",
"\n",
"messages_for(ed)"
]
},
{
"cell_type": "markdown",
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0",
"metadata": {},
"source": [
"## Time to bring it together - the API for OpenAI is very simple!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the OpenAI API. You will get very familiar with this!\n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = messages_for(website)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
"metadata": {},
"outputs": [],
"source": [
"summary = summarize(\"https://edwarddonner.com\")\n",
"print(summary)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d926d59-450e-4609-92ba-2d6f244f1342",
"metadata": {},
"outputs": [],
"source": [
"# A function to display this nicely in the Jupyter output, using markdown\n",
"\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3018853a-445f-41ff-9560-d925d1774b2f",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "markdown",
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624",
"metadata": {},
"source": [
"# Let's try more websites\n",
"\n",
"Note that this will only work on websites that can be scraped using this simplistic approach.\n",
"\n",
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n",
"\n",
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n",
"\n",
"But many websites will work just fine!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45d83403-a24c-44b5-84ac-961449b4008f",
"metadata": {},
"outputs": [],
"source": [
"urls = ['https://be-able.info/de/be-able/', \"https://taz.de/\", \"https://www.bundestagswahl-bw.de/wahlprogramm-gruene\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75e9fd40-b354-4341-991e-863ef2e59db7",
"metadata": {},
"outputs": [],
"source": [
"display_summary(urls[0])"
]
},
{
"cell_type": "markdown",
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business applications</h2>\n",
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n",
"\n",
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n",
" </td>\n",
" </tr>\n",
"</table>\n",
"\n",
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n",
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "856ff857-ba5f-4596-90b9-cd6cee4073dc",
"metadata": {},
"outputs": [],
"source": [
"# Naive extraction of name of the political party from user input\n",
"\n",
"party_mapping = {\"grünen\": \"grüne\", \"grüne\": \"grüne\", \"linken\": \"linke\", \"spd\": \"spd\", \"cdu\": \"cdu\", \"cdu/csu\": \"cdu\", \"csu\": \"cdu\", \"fdp\": \"fdp\", \"afd\": \"afd\", \"bsw\": \"bsw\"}\n",
"\n",
"def extract_party_from_user_prompt(user_input):\n",
" toks = user_input.split()\n",
" for tok in toks:\n",
" tok = tok.lower()\n",
" if tok in party_mapping.keys():\n",
" return party_mapping[tok]\n",
" return \"I can only answer your question concerning the election program of a certain political party. Mention one of 'FDP', 'BSW', 'Grüne', 'Linke', 'SPD', 'CDU' or 'AFD' in your question and I will try my best.\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3962d846-ce82-47d2-8c3f-5a6fe296710d",
"metadata": {},
"outputs": [],
"source": [
"from selenium import webdriver\n",
"from selenium.webdriver.common.by import By\n",
"\n",
"def get_election_program(partyname):\n",
" \"\"\"Scrape parties' election programs from the official election website. Naively ignore cookie banner stuff.\"\"\"\n",
"\n",
" # Download the browser driver for your OS and add the path here\n",
" browser_driver_path = r'C:\\Program Files\\BrowserDrivers\\geckodriver.exe'\n",
" \n",
" service = webdriver.firefox.service.Service(executable_path=browser_driver_path)\n",
" \n",
" parties = {\"grüne\": \"https://www.bundestagswahl-bw.de/wahlprogramm-gruene\",\n",
" \"spd\": \"https://www.bundestagswahl-bw.de/wahlprogramm-spd\",\n",
" \"cdu\": \"https://www.bundestagswahl-bw.de/wahlprogramm-cdu\",\n",
" \"linke\": \"https://www.bundestagswahl-bw.de/wahlprogramm-die-linke\",\n",
" \"fdp\": \"https://www.bundestagswahl-bw.de/wahlprogramm-fdp\",\n",
" \"afd\": \"https://www.bundestagswahl-bw.de/wahlprogramm-afd\",\n",
" \"bsw\": \"https://www.bundestagswahl-bw.de/wahlprogramm-bsw\"}\n",
" \n",
" election_prog = \"\"\n",
" \n",
" if partyname in parties.keys():\n",
" site = parties[partyname]\n",
" driver = webdriver.Firefox(service=service)\n",
" driver.get(site)\n",
" elements = driver.find_elements(By.TAG_NAME, 'p')\n",
" \n",
" for e in elements:\n",
" if not any(x in [\"Cookies\", \"Cookie\", \"akzeptiere\", \"Datenschutzerklärung\", \"Impressum\"] for x in e.text.split()) and e.text:\n",
" election_prog += e.text\n",
" if len(election_prog.split()) > 100:\n",
" print(\"Election program extracted.\")\n",
"\n",
" else:\n",
" election_prog = f\"Schade, für die Partei {partyname} konnte ich leider kein Wahlprogramm finden.\"\n",
" \n",
" driver.quit()\n",
" return election_prog"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3a408d1-d824-4e33-a5f4-c672bc6c6198",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def answer_my_election_program_question(input_from_user):\n",
" partyname = extract_party_from_user_prompt(input_from_user)\n",
" print(f\"This is a question about the political party: {partyname.capitalize()}\")\n",
" \n",
" # Step 1: Create your prompts\n",
" system_prompt = \"Du bist ein neutraler Beobachter, der aufgrund der ihm zur Verfügung gestellten Wahlprogramme Fragen zum Wahlprogramm der verschiedenen Parteien beantwortet. Beantworte Fragen zum Wahlprogramm auf Deutsch. Basiere deine Antwort ausschließlich auf den im Folgenden aufgeführten Informationen.\"\n",
" election_program = get_election_program(partyname)\n",
" \n",
" user_prompt = f\"Beantworte folgende Frage: \\n {input_from_user} \\n Verwende dafür folgende Infos: \\n {election_program}.\\n\\n Gib deine Antwort in Markdown aus.\"\n",
" \n",
" # Step 2: Make the messages list\n",
" \n",
" messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}] # fill this in\n",
" \n",
" # Step 3: Call OpenAI\n",
" \n",
" response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
" formatted_response = f\"\\n\\n{response.choices[0].message.content}\"\n",
" # Step 4: print the result\n",
" return formatted_response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e66a0967-d1e9-4f92-aeb6-95e478465a1f",
"metadata": {},
"outputs": [],
"source": [
"# Ask questions about the election programs of the main political parties for the Bundestagswahl 2025 in Germany\n",
"\n",
"question = \"Wie verhält sich die SPD zu Verkehrsfragen und Klimaschutz?\"\n",
"answer = answer_my_election_program_question(question)\n",
"display(Markdown(answer))"
]
},
{
"cell_type": "markdown",
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda",
"metadata": {},
"source": [
"## An extra exercise for those who enjoy web scraping\n",
"\n",
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)"
]
},
{
"cell_type": "markdown",
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6",
"metadata": {},
"source": [
"# Sharing your code\n",
"\n",
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n",
"\n",
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n",
"\n",
"Here are good instructions courtesy of an AI friend: \n",
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

195
week1/community-contributions/day1-selenium-lama-mac.ipynb

@ -0,0 +1,195 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "c97ad592-c8be-4583-a19c-ac813e56f410",
"metadata": {},
"source": [
"## Mac Users\n",
"\n",
"I find some challenges while setting up this in MAC silicon M1 chip. Execute below commands in MAC terminal.\n",
"\n",
"1. Download chromedriver.\n",
"2. Unzip and add it to the path.\n",
"3. Set Extended attributes."
]
},
{
"cell_type": "markdown",
"id": "b635b345-b000-48cc-8a7f-7df279a489a3",
"metadata": {},
"source": [
"cd ~/Downloads\n",
"wget https://storage.googleapis.com/chrome-for-testing-public/133.0.6943.126/mac-arm64/chromedriver-mac-arm64.zip\n",
"unzip chromedriver-mac-arm64.zip\n",
"sudo mv chromedriver-mac-arm64/chromedriver /usr/local/bin/\n",
"chmod +x /usr/local/bin/chromedriver\n",
"cd /usr/local/bin/\n",
"xattr -d com.apple.quarantine chromedriver\n",
"cd \n",
"chromedriver --version"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17c7c79a-8ae0-4f5d-a7c8-c54aa7ba90fd",
"metadata": {},
"outputs": [],
"source": [
"!pip install selenium\n",
"!pip install undetected-chromedriver\n",
"!pip install beautifulsoup4"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c10bd630-2dfd-4572-8c21-2dc4c6a372ab",
"metadata": {},
"outputs": [],
"source": [
"from selenium import webdriver\n",
"from selenium.webdriver.chrome.service import Service\n",
"from selenium.webdriver.common.by import By\n",
"from selenium.webdriver.chrome.options import Options\n",
"from openai import OpenAI\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fb3641d-e9f8-4f5b-bb9d-ee0e971cccdb",
"metadata": {},
"outputs": [],
"source": [
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"MODEL = \"llama3.2\"\n",
"PATH_TO_CHROME_DRIVER = '/usr/local/bin/chromedriver'\n",
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown. Highlight all the products this website offered and also find when website is created.\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d57e958",
"metadata": {},
"outputs": [],
"source": [
"class Website:\n",
" url: str\n",
" title: str\n",
" text: str\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
"\n",
" options = Options()\n",
"\n",
" options.add_argument(\"--no-sandbox\")\n",
" options.add_argument(\"--disable-dev-shm-usage\")\n",
"\n",
" service = Service(PATH_TO_CHROME_DRIVER)\n",
" driver = webdriver.Chrome(service=service, options=options)\n",
" driver.get(url)\n",
"\n",
" # input(\"Please complete the verification in the browser and press Enter to continue...\")\n",
" page_source = driver.page_source\n",
" driver.quit()\n",
"\n",
" soup = BeautifulSoup(page_source, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56df8cd2-2707-43f6-a066-3367846929b3",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt\n",
"\n",
"\n",
"\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]\n",
"\n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
" response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return response.choices[0].message.content\n",
"\n",
"\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2eb9599",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://ae.almosafer.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "31b66c0f-6b45-4986-b77c-758625945a91",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

115
week1/community-contributions/day1_far_far_away.ipynb

@ -0,0 +1,115 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "44aba2a0-c6eb-4fc1-a5cc-0a8f8679dbb8",
"metadata": {},
"source": [
"## Far Far Away..."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4d58124-5e9a-4f5a-9e0a-ff74f43896a8",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33179b68-7ed5-46ab-b583-d67ed57cd39d",
"metadata": {},
"outputs": [],
"source": [
"def add_user_greeting(greeting):\n",
" user_prompt = \"\"\"\n",
" The following is the greeting from the user. Please respond in character as a barman in the Mos Eisley Cantina.\\n\\n\n",
" \"\"\"\n",
" user_prompt += greeting\n",
"\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "67dc3099-2ccc-4ee8-8ff2-0dbbe4ae2fcb",
"metadata": {},
"outputs": [],
"source": [
"def approach_the_bar(greeting):\n",
"\n",
" system_prompt = \"You are a barman in the Mos Eisley Cantina from the Star Wars universe.\\\n",
"It is a Tuesday evening, the year is 3BBY, and the Cantina is quiet except for a few lonely regulars.\\\n",
"The barman (you) is slightly skeptical but eager to share some interesting news regarding some nearby imperial activity.\\\n",
"You will recieve a greeting from the user, you must respond and provide them with some gossip detailing \\\n",
"some local shady dealings occuring in Mos Eisley. Please format your response using markdown to provide a sense of the conversation.\"\n",
"\n",
" user_prompt = add_user_greeting(greeting)\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt},\n",
" ]\n",
" \n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = messages\n",
" )\n",
" \n",
" # Step 4: print the result in markdown format\n",
" pretty_response = Markdown(response.choices[0].message.content)\n",
" display(pretty_response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb47e2b7-5509-4d1a-8e71-ff103fc8a885",
"metadata": {},
"outputs": [],
"source": [
"approach_the_bar(\"\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

167
week1/community-contributions/day1_marketing_insights_scraper_Selenium_OpenAI.py

@ -0,0 +1,167 @@
import os
import time
import pandas as pd
import re
from dotenv import load_dotenv
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from openai import OpenAI
from openpyxl import load_workbook
from openpyxl.styles import Font, Alignment
# Load environment variables
load_dotenv(override=True)
api_key = os.getenv('OPENAI_API_KEY')
# Validate API Key
if not api_key:
raise ValueError("No API key was found - please check your .env file.")
# Initialize OpenAI client
openai = OpenAI()
# Set up Selenium WebDriver
chrome_options = Options()
chrome_options.add_argument("--headless")
chrome_options.add_argument("--disable-gpu")
chrome_options.add_argument("--no-sandbox")
chrome_options.add_argument("--disable-dev-shm-usage")
class Website:
"""Scrapes and processes website content using Selenium."""
def __init__(self, url: str):
self.url = url
self.text = "No content extracted."
service = Service(executable_path="/opt/homebrew/bin/chromedriver")
driver = webdriver.Chrome(service=service, options=chrome_options)
try:
driver.get(url)
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.TAG_NAME, "body"))
)
body_element = driver.find_element(By.TAG_NAME, "body")
self.text = body_element.text.strip() if body_element else "No content extracted."
except Exception as e:
print(f"Error fetching website: {e}")
finally:
driver.quit()
def summarized_text(self, max_length=1500):
return self.text[:max_length] + ("..." if len(self.text) > max_length else "")
def clean_text(text):
"""
Cleans extracted text by removing markdown-style formatting.
"""
text = re.sub(r"###*\s*", "", text)
text = re.sub(r"\*\*(.*?)\*\*", r"\1", text)
return text.strip()
# Aspect-specific prompts for concise output
aspect_prompts = {
"Marketing Strategies": "Summarize the core marketing strategies used on this website in in under 30 words. Do not include a title or introduction.",
"SEO Keywords": "List only the most relevant SEO keywords from this website, separated by commas. Do not include a title or introduction.",
"User Engagement Tactics": "List key engagement tactics used on this website (e.g., interactive features, user incentives, social proof). Keep responses to 3-5 bullet points. Do not include a title or introduction.",
"Call-to-Action Phrases": "List only the most common Call-to-Action phrases used on this website, separated by commas. Do not include a title or introduction.",
"Branding Elements": "Summarize the brand's tone, style, and positioning in under 30 words. Do not include a title or introduction.",
"Competitor Comparison": "Briefly describe how this website differentiates itself from competitors in under 30 words. Do not include a title or introduction.",
"Product Descriptions": "List the most important features or benefits of the products/services described on this website in under 30 words. Do not include a title or introduction.",
"Customer Reviews Sentiment": "Summarize the overall sentiment of customer reviews in oin under 30 words, highlighting common themes. Do not include a title or introduction.",
"Social Media Strategy": "List key social media strategies used on this website, separated by commas. Do not include a title or introduction."
}
def summarize(url: str) -> dict:
"""
Fetches a website, extracts relevant content, and generates a separate summary for each aspect.
:param url: The website URL to analyze.
:return: A dictionary containing extracted information.
"""
website = Website(url)
if not website.text or website.text == "No content extracted.":
return {"URL": url, "Error": "Failed to extract content"}
extracted_data = {"URL": url}
for aspect, prompt in aspect_prompts.items():
try:
formatted_prompt = f"{prompt} \n\nContent:\n{website.summarized_text()}"
response = openai.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are an expert at extracting structured information from website content."},
{"role": "user", "content": formatted_prompt}
]
)
extracted_data[aspect] = clean_text(response.choices[0].message.content)
except Exception as e:
extracted_data[aspect] = f"Error generating summary: {e}"
return extracted_data
def save_to_excel(data_list: list, filename="website_analysis.xlsx"):
"""
Saves extracted information to an Excel file with proper formatting.
:param data_list: A list of dictionaries containing extracted website details.
:param filename: The name of the Excel file to save data.
"""
df = pd.DataFrame(data_list)
df.to_excel(filename, index=False)
wb = load_workbook(filename)
ws = wb.active
# Auto-adjust column widths
for col in ws.columns:
max_length = 0
col_letter = col[0].column_letter
for cell in col:
try:
if cell.value:
max_length = max(max_length, len(str(cell.value)))
except:
pass
ws.column_dimensions[col_letter].width = min(max_length + 2, 50)
# Format headers
for cell in ws[1]:
cell.font = Font(bold=True)
cell.alignment = Alignment(horizontal="center", vertical="center")
# Wrap text for extracted content
for row in ws.iter_rows(min_row=2):
for cell in row:
cell.alignment = Alignment(wrap_text=True, vertical="top")
wb.save(filename)
print(f"Data saved to {filename} with improved formatting.")
# 🔹 LIST OF WEBSITES TO PROCESS
websites = [
"https://www.gymshark.com/",
]
if __name__ == "__main__":
print("\nProcessing websites...\n")
extracted_data_list = []
for site in websites:
print(f"Extracting data from {site}...")
extracted_data = summarize(site)
extracted_data_list.append(extracted_data)
save_to_excel(extracted_data_list)
print("\nAll websites processed successfully!")

223
week1/community-contributions/day1_ppt_summariser.ipynb

@ -0,0 +1,223 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "bfa3abd0-4e66-4117-96f9-7a71fbb6d0cb",
"metadata": {},
"source": [
"# Powerpoint Slides Summarizer\n",
"\n",
"This converts a Power Point presentation into notes that a student can easily skim through.\n",
"\n",
"Concepts Used:\n",
"- Converting Contents of PPT to text via python-pptx\n",
"- User and System Prompts\n",
"- Use of Open AI GPT-4o-mini via API key\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab95eb49-6a2d-4c7d-9057-78a2cd9364cc",
"metadata": {},
"outputs": [],
"source": [
"!pip install python-pptx"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62715f16-7125-455e-98e7-5705871c0e4a",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff42eab7-789d-44f8-a5cc-64baeebf3224",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bce425c2-6d19-4c03-93ce-8930dabc61ee",
"metadata": {},
"outputs": [],
"source": [
"# creating an instance\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0c75e30-3b38-4a89-b7d3-a41a6f5dc650",
"metadata": {},
"outputs": [],
"source": [
"from pptx import Presentation\n",
"\n",
"class PowerPoint():\n",
" def __init__(self,ppt):\n",
" \"\"\"\n",
" Creates a PowerPoint object, with name and text.\n",
" \"\"\"\n",
" self.ppt = ppt\n",
" self.title = os.path.basename(ppt)\n",
" self.text = self.extract_text()\n",
"\n",
" def extract_text(self):\n",
" \"\"\"\n",
" Extracts text from powerpoint.\n",
" \"\"\"\n",
" prs = Presentation(self.ppt)\n",
" text_content = []\n",
" \n",
" for slide in prs.slides:\n",
" for shape in slide.shapes:\n",
" if hasattr(shape, \"text\"):\n",
" text_content.append(shape.text)\n",
" \n",
" return \"\\n\".join(text_content)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1963a055-87f4-4e47-8456-cac4d4ac57fc",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents \\\n",
"of a PowerPoint presentation, and provides a summary in the style of \\\n",
"a cheat-sheet, for students to easily learn key concepts from.\\\n",
"You are to ignore text that might be navigation-related\\\n",
"and respond in Markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca600e90-7d3f-4fc7-a698-1b8f2925f81e",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of PowerPoints:\n",
"\n",
"def user_prompt_for(powerpoint):\n",
" user_prompt = f\"You are looking at a website titled {powerpoint.title}\"\n",
" user_prompt += \"\\nThe contents of this powerpoint are as follows; \\\n",
"please provide a summary of the content in markdown. \\\n",
"If it includes a question bank, add that along with short answers too.\\n\\n\"\n",
" user_prompt += powerpoint.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4fe19c56-9940-4528-b43a-c86798b215d2",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(powerpoint):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(powerpoint)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7704da5-90b0-40af-bbb4-7d589309f180",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the OpenAI API. \n",
"\n",
"def summarize(powerpoint_path):\n",
" powerpoint = PowerPoint(powerpoint_path)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = messages_for(powerpoint)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49d1d0cf-fa4b-4bea-bd68-a834145070ef",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "348078d1-e86f-4eb3-909d-33ab4ede984e",
"metadata": {},
"outputs": [],
"source": [
"ppt_file = \"Theoretical Perspectives on Media and Technology.pptx\" \n",
"display_summary(ppt_file)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

230
week1/community-contributions/day1_wiki_summariser.ipynb

@ -0,0 +1,230 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "56c86bae-1d3c-4c01-b5d6-c8879fec1954",
"metadata": {},
"source": [
"# Wiki Summarizer\n",
"\n",
"This Project takes the name of a topic as input, and checks if the corresponding wiki-page exists. If it does, it parses the web page, and outputs a summary created using the GPT-4o-mini model. \n",
"\n",
"Concepts used: \n",
"- Web Scraping via Beautiful Soup\n",
"- User and System Prompts\n",
"- Use of Open AI GPT-4o-mini via API key"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4820830e-b3b4-426e-b1a2-518e7c7f6c1a",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cd7ad51-396c-45c5-9089-f7b21a19da50",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "689421a0-20a1-428b-a8b8-fa239fa6f633",
"metadata": {},
"outputs": [],
"source": [
"# creating an instance\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "401901ae-7639-4190-98fd-e69374084723",
"metadata": {},
"outputs": [],
"source": [
"def isWiki(url):\n",
" \"\"\"\n",
" Check whether a Wikipedia page exists for a given topic, and \n",
" returns a Boolean value.\n",
" \"\"\"\n",
" response = requests.get(url)\n",
"\n",
" if response.status_code != 200:\n",
" return False\n",
" \n",
" return True"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7cdb14d3-05ea-4de2-a475-d49a5731692e",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f6ed50e-0fb5-479e-9845-f62cf25980f7",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an educational assistant tasked with helping users understand topics\\\n",
"by providing succinct and clear summaries of requested data. Ignore navigation-related text\\\n",
"and provide answers in markdown format\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2d77dd9-a94f-49c1-a1be-11d157bd37fb",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of wiki pages:\n",
"\n",
"def user_prompt_for(wiki):\n",
" user_prompt = f\"You are looking at a Wikipedia page titled {wiki.title}\"\n",
" user_prompt += \"\\nThe contents of this page is as follows; \\\n",
"please provide a short summary of this website in markdown.\\n\"\n",
" user_prompt += wiki.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d23bcc4-1d89-4bd4-9809-d3a1819aa919",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(wiki):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(wiki)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "971bd7fb-2ff8-4494-b386-de69a39c24ff",
"metadata": {},
"outputs": [],
"source": [
"def summarize(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = messages_for(website)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8fdf9f2-f49e-4d06-ac9e-dfcb8da33d60",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(topic):\n",
" url = f\"https://en.wikipedia.org/wiki/{topic}\"\n",
" if isWiki(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))\n",
" else:\n",
" print('A Wikipedia page does not exist for this topic')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4758ef0-9b7c-4d3e-9131-e3284dc76b6b",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"topic = input('Enter the name of Wikipedia page for which you would like a summary: ').strip()\n",
"display_summary(topic)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

213
week1/community-contributions/day2 EXERCISE_deepseek-r1.ipynb

@ -0,0 +1,213 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
"metadata": {},
"source": [
"## Also trying the amazing reasoning model DeepSeek\n",
"\n",
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n",
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
"\n",
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
"metadata": {},
"outputs": [],
"source": [
"!ollama pull deepseek-r1:1.5b"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bdcd35a",
"metadata": {},
"outputs": [],
"source": [
"!ollama pull deepseek-r1:8b"
]
},
{
"cell_type": "markdown",
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
"metadata": {},
"source": [
"# NOW the exercise for you\n",
"\n",
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c106420",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import requests\n",
"import ollama\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "22d62f00",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"MODEL = \"deepseek-r1:8b\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4449b7dc",
"metadata": {},
"outputs": [],
"source": [
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
"\n",
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "daca9448",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ec9d5d2",
"metadata": {},
"outputs": [],
"source": [
"# See how this function creates exactly the format above\n",
"\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e1ab04a",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the OpenAI API. You will get very familiar with this!\n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model = MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d3b5628",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "938e5633",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://edwarddonner.com\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

511
week1/community-contributions/day2 EXERCISE_ollama_llama3.ipynb

@ -0,0 +1,511 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {},
"source": [
"# Welcome to your first assignment!\n",
"\n",
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
]
},
{
"cell_type": "markdown",
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n",
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
"metadata": {},
"source": [
"# HOMEWORK EXERCISE ASSIGNMENT\n",
"\n",
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
"\n",
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
"\n",
"**Benefits:**\n",
"1. No API charges - open-source\n",
"2. Data doesn't leave your box\n",
"\n",
"**Disadvantages:**\n",
"1. Significantly less power than Frontier Model\n",
"\n",
"## Recap on installation of Ollama\n",
"\n",
"Simply visit [ollama.com](https://ollama.com) and install!\n",
"\n",
"Once complete, the ollama server should already be running locally. \n",
"If you visit: \n",
"[http://localhost:11434/](http://localhost:11434/)\n",
"\n",
"You should see the message `Ollama is running`. \n",
"\n",
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n",
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n",
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
"\n",
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940",
"metadata": {},
"outputs": [],
"source": [
"# Create a messages list using the same format that we used for OpenAI\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
"metadata": {},
"outputs": [],
"source": [
"payload = {\n",
" \"model\": MODEL,\n",
" \"messages\": messages,\n",
" \"stream\": False\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
"metadata": {},
"outputs": [],
"source": [
"# Let's just make sure the model is loaded\n",
"\n",
"!ollama pull llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
"metadata": {},
"outputs": [],
"source": [
"# If this doesn't work for any reason, try the 2 versions in the following cells\n",
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
"# And if none of that works - contact me!\n",
"\n",
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
"print(response.json()['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
"metadata": {},
"source": [
"# Introducing the ollama package\n",
"\n",
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
"\n",
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
"metadata": {},
"source": [
"## Alternative approach - using OpenAI python library to connect to Ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
"metadata": {},
"outputs": [],
"source": [
"# There's actually an alternative approach that some people might prefer\n",
"# You can use the OpenAI client python library to call Ollama:\n",
"\n",
"from openai import OpenAI\n",
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"\n",
"response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
"metadata": {},
"source": [
"## Also trying the amazing reasoning model DeepSeek\n",
"\n",
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n",
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
"\n",
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"!ollama pull deepseek-r1:1.5b"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d3d554b-e00d-4c08-9300-45e073950a76",
"metadata": {},
"outputs": [],
"source": [
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n",
"\n",
"response = ollama_via_openai.chat.completions.create(\n",
" model=\"deepseek-r1:1.5b\",\n",
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
"metadata": {},
"source": [
"# NOW the exercise for you\n",
"\n",
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
]
},
{
"cell_type": "markdown",
"id": "ffaa3470-884c-467e-b4ce-c1b8d39294da",
"metadata": {},
"source": [
"This is the code from day 1 notebook. Here we create the class to extract the text from the website, using BeautifulSoup library, and the we execute it to see the the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d8c9f01-ca12-4018-b7fa-698c9fa1aa93",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fd198df-bac5-42c5-83a0-06c5f71fb76a",
"metadata": {},
"outputs": [],
"source": [
"# Let's try one out. Change the website and add print statements to follow along.\n",
"\n",
"ed = Website(\"https://edwarddonner.com\")\n",
"print(ed.title)\n",
"print(ed.text)"
]
},
{
"cell_type": "markdown",
"id": "995b637d-a5db-4ad9-ac78-5980fd7ef112",
"metadata": {},
"source": [
"#### Define the system prompt, to instruct the model how we want to respond to our query. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee810d49-e88a-4137-a4be-98812e0d0748",
"metadata": {},
"outputs": [],
"source": [
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
"\n",
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "482b5d4c-69ed-4332-abb5-8b0986dcf368",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of websites:\n",
"\n",
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d966cb09-3ca2-49f7-8462-f6ef26c01159",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt_for(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f9be84f-4cd7-4ce7-8f33-e60d16f02852",
"metadata": {},
"outputs": [],
"source": [
"# For test purpose\n",
"\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5cb0e9f-eb56-4633-ba4c-76817be98856",
"metadata": {},
"outputs": [],
"source": [
"# To give you a preview -- calling ollama with system and user messages:\n",
"\n",
"import ollama\n",
"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c554903f-eb04-4a16-87fc-f1d9ff58f6d9",
"metadata": {},
"outputs": [],
"source": [
"# See how this function creates exactly the format above\n",
"\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b64b814-123f-436d-9366-4c762ac4b89a",
"metadata": {},
"outputs": [],
"source": [
"# Try this out, and then try for a few more websites\n",
"\n",
"messages_for(ed)"
]
},
{
"cell_type": "markdown",
"id": "d1ef4be2-ef3a-4b5d-8d18-f2eafa9d6a93",
"metadata": {},
"source": [
"### So, here let's run the summarize by using ollama and see how appears."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c46edc5-c85d-4ad0-89fd-39c4fdc44a5d",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the ollama API. \n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model = MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "466c2f78-91ca-4ed2-b60b-40661d0b6f68",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ab7c9a1-70fd-421c-be06-c36eb6c9aedf",
"metadata": {},
"outputs": [],
"source": [
"# A function to display this nicely in the Jupyter output, using markdown\n",
"\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1cedc9d9-6a76-4225-82c1-82240da16260",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82c48586-33c8-4797-a24f-41602c1297b3",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

435
week1/community-contributions/day2-EXERCISE-ollama-openai-api-website-summarizer-ITA.ipynb

@ -0,0 +1,435 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"# Welcome to your first assignment!\n",
"\n",
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
]
},
{
"cell_type": "markdown",
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n",
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
"metadata": {},
"source": [
"# HOMEWORK EXERCISE ASSIGNMENT\n",
"\n",
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
"\n",
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
"\n",
"**Benefits:**\n",
"1. No API charges - open-source\n",
"2. Data doesn't leave your box\n",
"\n",
"**Disadvantages:**\n",
"1. Significantly less power than Frontier Model\n",
"\n",
"## Recap on installation of Ollama\n",
"\n",
"Simply visit [ollama.com](https://ollama.com) and install!\n",
"\n",
"Once complete, the ollama server should already be running locally. \n",
"If you visit: \n",
"[http://localhost:11434/](http://localhost:11434/)\n",
"\n",
"You should see the message `Ollama is running`. \n",
"\n",
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n",
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n",
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
"\n",
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940",
"metadata": {},
"outputs": [],
"source": [
"# Create a messages list using the same format that we used for OpenAI\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
"metadata": {},
"outputs": [],
"source": [
"payload = {\n",
" \"model\": MODEL,\n",
" \"messages\": messages,\n",
" \"stream\": False\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
"metadata": {},
"outputs": [],
"source": [
"# Let's just make sure the model is loaded\n",
"\n",
"!ollama pull llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
"metadata": {},
"outputs": [],
"source": [
"# If this doesn't work for any reason, try the 2 versions in the following cells\n",
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
"# And if none of that works - contact me!\n",
"\n",
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
"print(response.json()['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
"metadata": {},
"source": [
"# Introducing the ollama package\n",
"\n",
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
"\n",
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
"metadata": {},
"source": [
"## Alternative approach - using OpenAI python library to connect to Ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
"metadata": {},
"outputs": [],
"source": [
"# There's actually an alternative approach that some people might prefer\n",
"# You can use the OpenAI client python library to call Ollama:\n",
"\n",
"from openai import OpenAI\n",
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"\n",
"response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
"metadata": {},
"source": [
"## Also trying the amazing reasoning model DeepSeek\n",
"\n",
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n",
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
"\n",
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
"metadata": {},
"outputs": [],
"source": [
"!ollama pull deepseek-r1:1.5b"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d3d554b-e00d-4c08-9300-45e073950a76",
"metadata": {},
"outputs": [],
"source": [
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n",
"\n",
"response = ollama_via_openai.chat.completions.create(\n",
" model=\"deepseek-r1:1.5b\",\n",
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
"metadata": {},
"source": [
"# NOW the exercise for you\n",
"\n",
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"HEADERS = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\",\n",
" \"Content-Type\": \"application/json\"\n",
"}\n",
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"\n",
"MODEL = \"llama3.2\"\n",
"\n",
"system_prompt = \"Sei un assistente e analizzi il contenuto di un sito web \\\n",
"produci un breve sommario, ignora il testo o gli elementi relativi alla navigazione. \\\n",
"Rispondi markdown.\"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6f343c27-628c-4c54-9a5b-842e6ad5d176",
"metadata": {},
"outputs": [],
"source": [
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=HEADERS)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bf6245ca-2d53-4fd8-a19c-0e6d052031fd",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"Stai cercando un sito dal titolo: {website.title}\"\n",
" user_prompt += \"\\nI contenuti di questo sito web sono i seguenti: \\\n",
"Per favore, fornisci un breve riassunto di questo sito web in markdown. \\\n",
"Se include notizie o annunci, riassumili anch'essi. \\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dec0636f-9efc-4f91-8861-3141276a9a6e",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f894b232-1ea1-4bd9-bf44-d7b1571f7913",
"metadata": {},
"outputs": [],
"source": [
"def summarize(url):\n",
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
" \n",
" website = Website(url)\n",
" response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages_for(website)\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d868d778-13b5-4934-acf5-dcb919a27d59",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0a0d9b79-de3c-4f77-9254-f02cf4d6217a",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Sommario del sito WP Pisa - Il sito del Meetup WordPress di Pisa**\n",
"\n",
"Il sito web WP Pisa è il punto di riferimento per gli appassionati di WordPress a Pisa. Gli organizzatori offrono meetup mensili gratuiti per discutere conoscenze, esperienze e progetti correlati al mondo di WordPress.\n",
"\n",
"**Eventi e Annunci**\n",
"\n",
"* **WordCamp Pisa 2025**: Aperta la call for organizer\n",
"* **Il Tuo Sito Ovunque in Pochi Minuti**: Un incontro con Docker e sviluppatori WordPress - Partecipa!\n",
"* **Core Days Roma: Le novità sul core di WordPress per i dev**\n",
"* **NO MORE THUMBNAILS!**\n",
"\n",
"**Informazioni Generali**\n",
"\n",
"* Il meetuu è aperto a tutti, indipendentemente dal livello di competenza in WordPress\n",
"* Tutti gli eventi sono gratuiti e organizzati con la supervisione di WordPress Foundation tramite la piattaforma Meetup.com\n",
"* La comunità WP Pisa contiene 150+ iscritti"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_summary(\"https://wppisa.it/\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

186
week1/community-contributions/day2-webpage-summarizer-ollama-gemini.ipynb

@ -0,0 +1,186 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1faf8b29-2ba6-40c7-89ee-71f71e234f11",
"metadata": {},
"source": [
"## Extra requirements\n",
"```bash\n",
"pip install -q -U google-genai\n",
"```\n",
"\n",
"## Required environment variable\n",
"GEMINI_API_KEY\n",
"\n",
"### How to get GEMINI API KEY\n",
"\n",
"Use the link: [gemini api key](https://aistudio.google.com/app/apikey) to get yours."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "be06ce76-20ee-4066-9582-a4ed745f278f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from dotenv import load_dotenv\n",
"from google import genai\n",
"from google.genai import types"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "99e42519-5dac-4b13-8a26-8a635753343b",
"metadata": {},
"outputs": [],
"source": [
"def gemini_invoke(website):\n",
" load_dotenv()\n",
" api_key = os.getenv(\"GEMINI_API_KEY\")\n",
" if not api_key or len(api_key) < 39:\n",
" print(\"No correct api key was found\")\n",
" return\n",
" else:\n",
" print(\"Api key found. Good to go!\")\n",
" client = genai.Client(api_key=api_key)\n",
" response = client.models.generate_content(\n",
" model=\"gemini-2.0-flash\",\n",
" config=types.GenerateContentConfig(\n",
" system_instruction=system_prompt),\n",
" contents=user_prompt_for(website)\n",
" )\n",
" return response.text"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "95a6ece8-8402-4cad-96b9-36a6ea444c54",
"metadata": {},
"outputs": [],
"source": [
"class Website:\n",
" url: str\n",
" title: str\n",
" text: str\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url)\n",
" soup = BeautifulSoup(response.content, \"html.parser\")\n",
" self.title = soup.title.string if soup.title else \"No title was found\"\n",
"\n",
" for irr in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irr.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24bbd1dd-dca4-4bbc-ae91-4bad227a4278",
"metadata": {},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"print(ed.title)\n",
"print(ed.text)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "233b8904-7a4a-4265-8b0d-20934ae4b29c",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that navigation related. Respond \\\n",
"in markdown.\"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5c996c03-84ab-4378-8a55-026d94404d35",
"metadata": {},
"outputs": [],
"source": [
"messages = [{\"role\": \"user\", \"content\": system_prompt}]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "abf9464e-dc8d-4099-aeb6-495498326673",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "32ab2d29-02d1-43c5-b920-f2621f292b23",
"metadata": {},
"outputs": [],
"source": [
"def summarize(url, model=\"gemini\"):\n",
" website = Website(url)\n",
" if model == \"ollama\":\n",
" import ollama\n",
" Model=\"llama3.2\"\n",
" messages[0][\"content\"] += f\" Website: {url}\"\n",
" response = ollama.chat(model=Model, messages=messages)\n",
" return response[\"message\"][\"content\"]\n",
" else:\n",
" return gemini_invoke(website)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2a0e518-7198-489d-a0ce-2eec617f939f",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://edwarddonner.com\", \"ollama\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

240
week1/community-contributions/day2_exercise_cl.ipynb

@ -0,0 +1,240 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "fc3a96d1-eedf-4e3a-b3ce-151485c574b5",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "385dc3d5-f6ce-46d8-958e-83dc1150c24e",
"metadata": {},
"outputs": [],
"source": [
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21f7dacc-1fa8-491c-8e94-39238dae52b3",
"metadata": {},
"outputs": [],
"source": [
"class Website:\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=HEADERS)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca431e32-9191-4940-b62d-f25e8cbac627",
"metadata": {},
"outputs": [],
"source": [
"web = Website(\"https://silviayomdesign.com/\")\n",
"print(web.title)\n",
"print(web.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76475815-0dbc-451b-ab65-f7e2ea3aaa8a",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cf03913-f595-4817-8580-19b182c599de",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a very artistic graphic designer's website titled name {website.title}\"\n",
" user_prompt += \"\\nHer creativity of her works are as follow;\\\n",
"please provide a short summary of her works in markdown. \\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f130cfe-756b-4df8-b1f0-6918956a6162",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt_for(web))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85d85b64-1452-408f-bfae-d27b52d7dfa7",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(web)}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36d66055-66d6-4123-b092-eceab055829d",
"metadata": {},
"outputs": [],
"source": [
"payload = {\n",
" \"model\": MODEL,\n",
" \"messages\": messages,\n",
" \"stream\": False\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "163db8a9-b0eb-49f3-a5f2-1e74cf51c245",
"metadata": {},
"outputs": [],
"source": [
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
"print(response.json()[\"message\"][\"content\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afabfff5-81e5-4b61-aca9-6c19d3584b86",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(web)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2e83b58-16fc-4049-8116-24a0cbb3635a",
"metadata": {},
"outputs": [],
"source": [
"messages_for(web)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05ed519a-514f-4ed8-b323-4f4817e1e1c6",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model=MODEL, \n",
" messages=messages\n",
" )\n",
" return response[\"message\"][\"content\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b182f686-0a3e-4959-9bfd-0a59d2befd4c",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://silviayomdesign.com/\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4f1f807-28d4-4b8b-9698-9b90dcbac59f",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a988d29b-ed36-4a40-bd77-0f7d60a29ac3",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://silviayomdesign.com/\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15e72eeb-1c35-4bb2-9596-6ff2546aa046",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

663
week1/community-contributions/day5 - brochure improved.ipynb

@ -0,0 +1,663 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
"metadata": {},
"source": [
"# A full business solution\n",
"\n",
"## Now we will take our project from Day 1 to the next level\n",
"\n",
"### BUSINESS CHALLENGE:\n",
"\n",
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n",
"\n",
"We will be provided a company name and their primary website.\n",
"\n",
"See the end of this notebook for examples of real-world business applications.\n",
"\n",
"And remember: I'm always available if you have problems or ideas! Please do reach out."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5b08506-dc8b-4443-9201-5f1848161363",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n",
"\n",
"import os\n",
"import requests\n",
"import json\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61",
"metadata": {},
"outputs": [],
"source": [
"# Initialize and constants\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
" \n",
"MODEL = 'gpt-4o-mini'\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "106dd65e-90af-4ca8-86b6-23a41840645b",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a",
"metadata": {},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"ed.links"
]
},
{
"cell_type": "markdown",
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3",
"metadata": {},
"source": [
"## First step: Have GPT-4o-mini figure out which links are relevant\n",
"\n",
"### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. \n",
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n",
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n",
"\n",
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n",
"\n",
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6957b079-0d96-45f7-a26a-3487510e9b35",
"metadata": {},
"outputs": [],
"source": [
"oneshot_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
"You are able to decide which of the links would be most relevant to include in a brochure about the company or freelancer offering their services, \\\n",
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
"oneshot_system_prompt += \"You should respond in JSON as in this example:\"\n",
"oneshot_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
"}\n",
"\"\"\"\n",
"oneshot_system_prompt += \"Make sure not to miss any relevant pages.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5a8b688-b153-41a6-8b18-f6198f3df2c9",
"metadata": {},
"outputs": [],
"source": [
"fewshot_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
"You are able to decide which of the links would be most relevant to include in a brochure about the company or freelancer offering their services, \\\n",
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n You should respond in JSON as in the following examples:\"\n",
"fewshot_system_prompt += \"\"\"\n",
" Example 1\n",
" ['https://great-comps.com/about-me', 'https://www.linkedin.com/in/great-comp/', 'mailto:hello@mygroovydomain.com', 'https://great-comps.com/news', '/case-studies', 'https://patents.google.com/patent/US20210049536A1/', 'https://great-comps.com/workshop-ai']\n",
"\n",
" Links:\n",
" {\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://great-comps.de/about-me\"},\n",
" {\"type\": \"news page\": \"url\": \"https://great-comps.de/news\"},\n",
" {\"type\": \"case studies page\": \"url\": \"https://great-comps.de/case-studies\"},\n",
" {\"type\": \"workshop page\": \"url\": \"https://great-comps.de/workshop-ai\"},\n",
" ]\n",
" }\n",
"\n",
" Example 2\n",
" ['mailto:info@robbie-doodle-domain.com','https://wahlen-robbie.at/ueber-mich', 'https://www.linkedin.com/in/robbie-doodle/', 'https://news.ycombinator.com', 'https://wahlen-robbie.at/neuigkeiten', 'https://twitter.com/robbie-d', '/whitepapers', 'https://patents.google.com/patent/US20210049536A1/', 'https://wahlen-robbie.at/services']\n",
"\n",
" Links:\n",
" {\n",
" \"links\": [\n",
" {\"type\": \"über mich\", \"url\": \"https://wahlen-robbie.at/ueber-mich\"},\n",
" {\"type\": \"aktuelles\": \"url\": \"https://wahlen-robbie.at/neuigkeiten\"},\n",
" {\"type\": \"whitepaper\": \"url\": \"https://wahlen-robbie.at/whitepapers\"},\n",
" {\"type\": \"services\": \"url\": \"https://wahlen-robbie.at/services\"}\n",
" ]\n",
" }\n",
" \"\"\"\n",
"fewshot_system_prompt += \"Make sure not to miss any relevant pages.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b97e4068-97ed-4120-beae-c42105e4d59a",
"metadata": {},
"outputs": [],
"source": [
"print(f\"Oneshot system prompt:\\n{oneshot_system_prompt}\")\n",
"print(f\"\\n\\n\\nFewshot system prompt:\\n{fewshot_system_prompt}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3",
"metadata": {},
"outputs": [],
"source": [
"def get_links_user_prompt(website):\n",
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company or person offering their services, respond with the full https URL in JSON format. \\\n",
"Do not include Terms of Service, Privacy, email links or social media links.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"\\n\".join(website.links)\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7",
"metadata": {},
"outputs": [],
"source": [
"print(get_links_user_prompt(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69",
"metadata": {},
"outputs": [],
"source": [
"def get_links(url, system_prompt=oneshot_system_prompt):\n",
" \n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ],\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" \n",
" result = response.choices[0].message.content \n",
" print(f\"Response: {result}\")\n",
" return json.loads(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2dc4150a-0042-4f5d-a7bf-158a0f9147a6",
"metadata": {},
"outputs": [],
"source": [
"get_links(ed_url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2",
"metadata": {},
"outputs": [],
"source": [
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n",
"hf = \"https://huggingface.co\"\n",
"\n",
"huggingface = Website(hf)\n",
"huggingface.links"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924",
"metadata": {},
"outputs": [],
"source": [
"ed_url = \"https://edwarddonner.com\"\n",
"hf_url = \"https://huggingface.co\"\n",
"\n",
"print(f\"Links generated with oneshot prompt for {ed_url}:\\n\")\n",
"get_links(ed_url)\n",
"\n",
"print(f\"\\n\\nLinks generated with fewshot prompt for {ed_url}:\\n\")\n",
"get_links(ed_url, fewshot_system_prompt)\n",
"\n",
"print(50*\"*\")\n",
"print(f\"\\nLinks generated with oneshot prompt for {hf_url}:\\n\")\n",
"get_links(hf_url)\n",
"\n",
"print(f\"\\n\\nLinks generated with fewshot prompt for {hf_url}:\\n\")\n",
"get_links(hf_url, fewshot_system_prompt)"
]
},
{
"cell_type": "markdown",
"id": "0d74128e-dfb6-47ec-9549-288b621c838c",
"metadata": {},
"source": [
"## Second step: make the brochure!\n",
"\n",
"Assemble all the details into another prompt to GPT4-o"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5",
"metadata": {},
"outputs": [],
"source": [
"def get_all_details(url, type=fewshot_system_prompt):\n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
"\n",
" links = get_links(url, type)\n",
" print(\"Found links:\", links)\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += Website(link[\"url\"]).get_contents()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2",
"metadata": {},
"outputs": [],
"source": [
"print(get_all_details(ed_url))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"and creates a short brochure about the company for prospective customers, investors and recruits. \\\n",
"The brochure should be a bit unusual in terms of tone and style, it should astound the reader and pique their interest. Respond in markdown.\\\n",
"Include details of company culture, customers and careers/jobs if you have the information.\"\n",
"\n",
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n",
"\n",
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23",
"metadata": {},
"outputs": [],
"source": [
"def get_brochure_user_prompt(company_name, url):\n",
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05d07160-7910-4da2-92ac-36aa849fcc68",
"metadata": {},
"outputs": [],
"source": [
"# get_brochure_user_prompt(\"Edward Donner\", ed_url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd909e0b-1312-4ce2-a553-821e795d7572",
"metadata": {},
"outputs": [],
"source": [
"# get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46",
"metadata": {},
"outputs": [],
"source": [
"def create_brochure(company_name, url):\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b0de762-f343-44d9-85d5-9bffba3c0ae8",
"metadata": {},
"outputs": [],
"source": [
"brochure_ed = create_brochure(\"Edward Donner\", ed_url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e093444a-9407-42ae-924a-145730591a39",
"metadata": {},
"outputs": [],
"source": [
"brochure_hf = create_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d00b012-3901-492c-b985-a0340750c011",
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(brochure_ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e33cb2e9-3b8c-4ef3-a6cb-70b3188b9120",
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(brochure_hf))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dea955ad-24a6-490b-8191-f066bff1b595",
"metadata": {},
"outputs": [],
"source": [
"def translate_brochure(brochure_content, language=\"German\"):\n",
" system_prompt = f\"You are a skilled translator. Translate the following brochure text into {language}.\\\n",
" Make sure to translate into a idiomatic {language}, matching the target language's natural structure, wording and expressions, so it can't be recognised as a translation.\\\n",
" Be sure to also meet an appropriate tone, eg a good marketing language in other languages will probably be a bit less boastful than in English.\\\n",
" Output the translated brochure in Markdown format.\"\n",
" \n",
" response = openai.chat.completions.create(\n",
" model = MODEL,\n",
" messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": brochure_content}]\n",
" )\n",
"\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b6bdd4f-7518-4780-9da9-47f90aab974b",
"metadata": {},
"outputs": [],
"source": [
"translation = translate_brochure(brochure_ed, language=\"German\")\n",
"display(Markdown(translation))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1dd96f2-0980-4a30-a152-1f38c0e319bb",
"metadata": {},
"outputs": [],
"source": [
"translation = translate_brochure(brochure_hf, language=\"German\")\n",
"display(Markdown(translation))"
]
},
{
"cell_type": "markdown",
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18",
"metadata": {},
"source": [
"## Finally - a minor improvement\n",
"\n",
"With a small adjustment, we can change this so that the results stream back from OpenAI,\n",
"with the familiar typewriter animation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51db0e49-f261-4137-aabe-92dd601f7725",
"metadata": {},
"outputs": [],
"source": [
"def stream_brochure(company_name, url):\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d",
"metadata": {},
"outputs": [],
"source": [
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b",
"metadata": {},
"outputs": [],
"source": [
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n",
"\n",
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "markdown",
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business applications</h2>\n",
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n",
"\n",
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n",
"\n",
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype. See what other students have done in the community-contributions folder -- so many valuable projects -- it's wild!</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n",
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "17b64f0f-7d33-4493-985a-033d06e8db08",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">A reminder on 3 useful resources</h2>\n",
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n",
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!<br/>\n",
" 3. I'm trying out X/Twitter and I'm at <a href=\"https://x.com/edwarddonner\">@edwarddonner<a> and hoping people will teach me how it's done.. \n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#090;\">Finally! I have a special request for you</h2>\n",
" <span style=\"color:#090;\">\n",
" My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

81
week1/community-contributions/day5-disable-ssl.ipynb

@ -0,0 +1,81 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
"metadata": {},
"source": [
"# A Small Tweak to Week1-Day5\n",
"\n",
"If you have network restrictions (such as using a custom DNS provider, or firewall rules at work), you can disable SSL cert verification.\n",
"Once you do that and start executing your code, the output will be riddled with warnings. Thankfully, you can suppress those warnings,too.\n",
"\n",
"See the 2 lines added to the init method, below."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "106dd65e-90af-4ca8-86b6-23a41840645b",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
"\n",
" #\n",
" # If you must disable SSL cert validation, and also suppress all the warning that will come with it,\n",
" # add the 2 lines below. This comes in very handy if you have DNS/firewall restrictions; alas, use\n",
" # with caution, especially if deploying this in a non-dev environment.\n",
" requests.packages.urllib3.disable_warnings() \n",
" response = requests.get(url, headers=headers, verify=False) \n",
" # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" \n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

440
week1/community-contributions/day5-github-page-portfolio-maker.ipynb

@ -0,0 +1,440 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "07be6aa3-6636-4b57-be16-823c3907f4c4",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e64af7b-6956-4437-ab32-857a6ea814c3",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"\n",
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
" print(\"Api key found. Good to go!\") \n",
"else:\n",
" print(\"No correct api key was found\")\n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI(api_key=api_key)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4667e3ee-d5b7-42ed-99ad-5e9fa75c8660",
"metadata": {},
"outputs": [],
"source": [
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42adb18b-3ec9-4700-95e4-c0041ce8f17a",
"metadata": {},
"outputs": [],
"source": [
"class GithubProfile:\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get(\"href\") for link in soup.find_all(\"a\")]\n",
" self.links = [link for link in links if link]\n",
" \n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "661b5377-c444-45a9-9455-85f83ff525d3",
"metadata": {},
"outputs": [],
"source": [
"profile = GithubProfile(\"https://github.com/ertgl\")\n",
"profile.links"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f9a3c08-0db2-4baa-a8a4-f5642049a57c",
"metadata": {},
"outputs": [],
"source": [
"link_system_prompt = \"You are provided with a list of links found on a Github page. \\\n",
"You are able to decide which of the links would be most relevant to include in a portfolio about the github user, \\\n",
"such as links to an About page, or a Repositories, or Projects.\\n\"\n",
"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
"link_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"overview page\", \"url\": \"https://another.full.url\"},\n",
" {\"type\": \"repositories page\": \"url\": \"https://another.full.url?tab=repositories\"}\n",
" ]\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30eafd50-9735-4388-9cc1-8337a00069a2",
"metadata": {},
"outputs": [],
"source": [
"print(link_system_prompt)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4dc4f366-5c00-441d-b1bd-8dda148f1ffb",
"metadata": {},
"outputs": [],
"source": [
"def get_links_user_prompt(profile):\n",
" user_prompt = f\"Here is the list of links on the website of {profile.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a portfolio about the user, respond with the full https URL in JSON format. \\\n",
"Do not include Terms of Service, Privacy, Login, Blog or Github trending related pages.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"\\n\".join(profile.links)\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c066b2ac-5863-408e-bb42-1388d130d164",
"metadata": {},
"outputs": [],
"source": [
"print(get_links_user_prompt(profile))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc0ccb95-479c-4f6e-9686-1ff38aa543fa",
"metadata": {},
"outputs": [],
"source": [
"def get_links(url):\n",
" profile = GithubProfile(url)\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(profile)}\n",
" ],\n",
" response_format= {\"type\": \"json_object\"}\n",
" )\n",
" result = response.choices[0].message.content\n",
" return json.loads(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f5e3b8b-398d-4e23-867e-401faca7db03",
"metadata": {},
"outputs": [],
"source": [
"get_links(profile.url)"
]
},
{
"cell_type": "markdown",
"id": "b9024a4f-4038-4c0e-b0c7-74226feaccfd",
"metadata": {},
"source": [
"# Second step: make the portfolio!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9906d73-801a-4aea-b620-10ac39eaf424",
"metadata": {},
"outputs": [],
"source": [
"def get_all_details(url):\n",
" result = \"Landing page:\\n\"\n",
" result += GithubProfile(url).get_contents()\n",
" links = get_links(url)\n",
" print(\"Found links:\", links)\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += GithubProfile(link[\"url\"]).get_contents()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02039450-7f7f-4556-8645-39cd31f30265",
"metadata": {},
"outputs": [],
"source": [
"print(get_all_details(\"https://github.com/ertgl\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4119b96f-0aa1-4cdb-9a09-d51b163069b8",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a personal github page \\\n",
"and creates a short portfolio about the user profile, especially projects and repositories and summary of the repo's \\\n",
"README files for prospective recruiters, investors. Respond in markdown.\\\n",
"Include details of person profile overview, if you have the information.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "842834d2-a5e9-4b56-a792-492a1a137fbc",
"metadata": {},
"outputs": [],
"source": [
"def get_portfolio_user_prompt(profile_name, url):\n",
" user_prompt = f\"You are looking at a user called: {profile_name} on Github.\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short portfolio of the user in markdown.\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "285b3a1d-894a-463c-8c30-b5de203b8358",
"metadata": {},
"outputs": [],
"source": [
"print(get_portfolio_user_prompt(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\"))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78dc7495-d0a5-409b-8ecf-3a5ef9220e25",
"metadata": {},
"outputs": [],
"source": [
"def create_portfolio(profile_name, url):\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n",
" ]\n",
" )\n",
" result = response.choices[0].message.content\n",
" display(Markdown(result))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abe39377-2d52-434a-aace-e9397cdd4f20",
"metadata": {},
"outputs": [],
"source": [
"create_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "edd168ca-b77b-4fc7-9e11-2114a43553e4",
"metadata": {},
"outputs": [],
"source": [
"def stream_portfolio(profile_name, url):\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ea391d4-775e-483d-9e55-e3ae30fa9bd8",
"metadata": {},
"outputs": [],
"source": [
"stream_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\")"
]
},
{
"cell_type": "markdown",
"id": "498ca0c8-8f68-4389-8184-078706b62cf6",
"metadata": {},
"source": [
"# Multi-lingual with Multi-Tone in Desire Format"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "f11e3391-03f9-409c-9f5a-6286959690ec",
"metadata": {},
"outputs": [],
"source": [
"def multi_lingual_stream_portfolio(profile_name, url, language, tone):\n",
"\n",
" system_prompt = f\"\"\"\n",
"You are an assistant that analyzes the contents of several relevant pages from a github profile page and \n",
"creates a visually appealing and professional short portfolio for prospective investors, and recruiters. \n",
"The portfolio should be written in {language} and use a {tone.lower()} tone throughout.\n",
"\n",
"The portfolio should follow this structure (in {language}):\n",
"\n",
"1. **Front Cover**:\n",
" - Prominently display the user name as Title.\n",
" - Include a compelling headline or tagline.\n",
" - Add something engaging relevant to the user’s summarized README files if available.\n",
"\n",
"2. **About**:\n",
" - Provide a brief introduction to the user's projects approach.\n",
" - State which repository they own or they contributed.\n",
"\n",
"3. **Overview**:\n",
" - Summarize the user's projects, repositories, or solutions by summarized README files if available.\n",
" - Highlight benefits or unique developer/development points.\n",
" - Mention the follower and following users count and total stars they got.\n",
"\n",
"\n",
"4. **My Culture**:\n",
" - Outline the user’s key values or guiding principles.\n",
" - Describe the workplace environment (e.g., innovation-driven, inclusive, collaborative).\n",
" - Highlight community engagement.\n",
"\n",
"5. **What kind of companies may be interested**:\n",
" - Describe the target customers or industries served.\n",
" - Mention open source contributions also if available. \n",
" \n",
"6. **Projects**:\n",
" \n",
" ***Owner***:\n",
" - List owned projects/repositories with summaries. (Summarize README file of the each project)\n",
" \n",
" ***Contributer***:\n",
" - List contributed projects/repositories with summaries. (Summarize README file of the each project)\n",
"\n",
"\n",
"7. **Support and Donation**:\n",
" - Encourage those interested in user's open source projects to donate.\n",
" - Provide direct links or steps to apply if available.\n",
"\n",
"8. **Contact Us**:\n",
" - Provide the user’s address, phone number, and email.\n",
" - Include links to social media platforms.\n",
" - Add a link to the user’s website.\n",
"\n",
"9. **Closing Note**:\n",
" - End with a thank-you message or an inspirational note for the reader.\n",
" - Add a call-to-action (e.g., “Get in touch today!” or “Explore more on my website”).\n",
"\n",
"Ensure the content is concise, engaging, visually clear, and tailored to the target audience. Use headings and subheadings to make the brochure easy to navigate. Include links and contact information wherever applicable.\n",
"\"\"\"\n",
"\n",
"\n",
" \n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a38dc0b-27de-4738-8883-b3857e067b45",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"multi_lingual_stream_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\", \"English\", \"serious, entertaining, witty\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

501
week1/community-contributions/day5-ollama.ipynb

@ -0,0 +1,501 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
"metadata": {},
"source": [
"# A full business solution (Ollama Version)\n",
"\n",
"## Now we will take our project from Day 1 to the next level\n",
"\n",
"### BUSINESS CHALLENGE:\n",
"\n",
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n",
"\n",
"We will be provided a company name and their primary website.\n",
"\n",
"See the end of this notebook for examples of real-world business applications.\n",
"\n",
"And remember: I'm always available if you have problems or ideas! Please do reach out."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5b08506-dc8b-4443-9201-5f1848161363",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n",
"\n",
"import os\n",
"import requests\n",
"import json\n",
"from typing import List\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display\n",
"import ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61",
"metadata": {},
"outputs": [],
"source": [
"# Initialize and constants\n",
" \n",
"# MODEL = \"llama3.2\"\n",
"MODEL = \"llama3.2:1b\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "106dd65e-90af-4ca8-86b6-23a41840645b",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a",
"metadata": {},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"ed.links\n",
"# print(ed.get_contents())"
]
},
{
"cell_type": "markdown",
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3",
"metadata": {},
"source": [
"## First step: Have llama3.2 figure out which links are relevant\n",
"\n",
"### Use a call to llama3.2 to read the links on a webpage, and respond in structured JSON. \n",
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n",
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n",
"\n",
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n",
"\n",
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6957b079-0d96-45f7-a26a-3487510e9b35",
"metadata": {},
"outputs": [],
"source": [
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
"link_system_prompt += \"You should respond in JSON format as shown in the following example:\"\n",
"link_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b97e4068-97ed-4120-beae-c42105e4d59a",
"metadata": {},
"outputs": [],
"source": [
"print(link_system_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3",
"metadata": {},
"outputs": [],
"source": [
"def get_links_user_prompt(website):\n",
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
"Do not include Terms of Service, Privacy, email links.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"\\n\".join(website.links)\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7",
"metadata": {},
"outputs": [],
"source": [
"print(get_links_user_prompt(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69",
"metadata": {},
"outputs": [],
"source": [
"def get_links(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ],\n",
" )\n",
" result = response['message']['content']\n",
" return json.loads(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2",
"metadata": {},
"outputs": [],
"source": [
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n",
"\n",
"huggingface = Website(\"https://huggingface.co\")\n",
"huggingface.links"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924",
"metadata": {},
"outputs": [],
"source": [
"get_links(\"https://huggingface.co\")"
]
},
{
"cell_type": "markdown",
"id": "0d74128e-dfb6-47ec-9549-288b621c838c",
"metadata": {},
"source": [
"## Second step: make the brochure!\n",
"\n",
"Assemble all the details into another prompt to llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5",
"metadata": {},
"outputs": [],
"source": [
"def get_all_details(url):\n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
" links = get_links(url)\n",
" print(\"Found links:\", links)\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += Website(link[\"url\"]).get_contents()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2",
"metadata": {},
"outputs": [],
"source": [
"print(get_all_details(\"https://huggingface.co\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"Include details of company culture, customers and careers/jobs if you have the information.\"\n",
"\n",
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n",
"\n",
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23",
"metadata": {},
"outputs": [],
"source": [
"def get_brochure_user_prompt(company_name, url):\n",
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd909e0b-1312-4ce2-a553-821e795d7572",
"metadata": {},
"outputs": [],
"source": [
"get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46",
"metadata": {},
"outputs": [],
"source": [
"def create_brochure(company_name, url):\n",
" response = ollama.chat(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" )\n",
" result = response['message']['content']\n",
" display(Markdown(result))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e093444a-9407-42ae-924a-145730591a39",
"metadata": {},
"outputs": [],
"source": [
"create_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "markdown",
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18",
"metadata": {},
"source": [
"## Finally - a minor improvement\n",
"\n",
"With a small adjustment, we can change this so that the results stream back from Ollama,\n",
"with the familiar typewriter animation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51db0e49-f261-4137-aabe-92dd601f7725",
"metadata": {},
"outputs": [],
"source": [
"def stream_brochure(company_name, url):\n",
" stream = ollama.chat(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" content = chunk.get(\"message\", {}).get(\"content\", \"\")\n",
" if content:\n",
" response += content\n",
" response = response.replace(\"```\", \"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d",
"metadata": {},
"outputs": [],
"source": [
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b",
"metadata": {},
"outputs": [],
"source": [
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n",
"\n",
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "markdown",
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business applications</h2>\n",
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n",
"\n",
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n",
"\n",
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n",
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "17b64f0f-7d33-4493-985a-033d06e8db08",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">A reminder on 2 useful resources</h2>\n",
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n",
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#090;\">Finally! I have a special request for you</h2>\n",
" <span style=\"color:#090;\">\n",
" My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

201
week1/community-contributions/week1 EXERCISE - TechTutor.ipynb

@ -0,0 +1,201 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import os\n",
"from dotenv import load_dotenv\n",
"\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [],
"source": [
"# set up environment\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"\n",
"# set up clients\n",
"openai = OpenAI()\n",
"ollama = OpenAI(base_url=\"http://localhost:11434/v1\" , api_key=\"ollama\")\n",
"\n",
"# set up system prompt\n",
"system_prompt = \"You are a coding tutor. If the user asks you a question, answer it to the point. If you are asked to create a code snippet, generate the code in Python and then explain it shortly.\""
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "58f098cb-4b4e-4394-b0b5-29db88e9101c",
"metadata": {},
"outputs": [],
"source": [
"def send_request(user_prompt, model=MODEL_LLAMA, stream=False):\n",
" message = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}]\n",
" if model.startswith(\"gpt\"):\n",
" model_client = openai\n",
" else:\n",
" model_client = ollama\n",
"\n",
" \n",
" response = model_client.chat.completions.create(\n",
" model=model,\n",
" messages=message,\n",
" stream=stream\n",
" )\n",
"\n",
" if stream:\n",
" streaming = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in response:\n",
" streaming += chunk.choices[0].delta.content or ''\n",
" streaming = streaming.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(streaming), display_id=display_handle.display_id)\n",
"\n",
" else:\n",
" return display(Markdown(response.choices[0].message.content))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
" How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\n"
]
}
],
"source": [
"# here is the question; type over this to ask something new\n",
"question = input()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "2bc093fa-b2ff-47e9-8ea8-e41499385116",
"metadata": {},
"outputs": [],
"source": [
"# question = \"\"\"How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
"metadata": {},
"outputs": [],
"source": [
"# Get gpt-4o-mini to answer, with streaming\n",
"send_request(model=MODEL_GPT, user_prompt=question, stream=True)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"To display Python code properly with OpenAI's chat interface, you'll need to use the `code` formatting in the response format provided by the API endpoint. \n",
"\n",
"Here's an example of how you can modify the API request URL to include the formatted code:\n",
"\n",
"```python\n",
"import requests\n",
"import json\n",
"\n",
"query = {\n",
" \"text\": \"{\\n} # Python code here\\n}\"\n",
"\n",
"headers = {\n",
" 'Content-Type': 'application/json'\n",
"}\n",
"\n",
"response = requests.post('https://api.openai.com/v1/answers', data=json.dumps(query), headers=headers)\n",
"\n",
"answer = response.json()\n",
"```\n",
"\n",
"However, the most convenient way to display the code is by using the `code` directive directly in your chat prompt. OpenAI will automatically format and highlight your code."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get Llama 3.2 to answer\n",
"send_request(user_prompt=question)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

202
week1/community-contributions/week1 EXERCISE_AI_techician.ipynb

@ -0,0 +1,202 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"from IPython.display import Markdown, display, update_display\n",
"import openai\n",
"from openai import OpenAI\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"models = {\n",
" 'MODEL_GPT': 'gpt-4o-mini',\n",
" 'MODEL_LLAMA': 'llama3.2'\n",
"}\n",
"\n",
"# To use ollama using openai API (ensure that ollama is running on localhost)\n",
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"\n",
"def model_choices(model):\n",
" if model in models:\n",
" return models[model]\n",
" else:\n",
" raise ValueError(f\"Model {model} not found in models dictionary\")\n",
"\n",
"def get_model_api(model='MODEL_GPT'):\n",
" if model == 'MODEL_GPT':\n",
" return openai, model_choices(model)\n",
" elif model == 'MODEL_LLAMA':\n",
" return ollama_via_openai, model_choices(model)\n",
" else:\n",
" raise ValueError(f\"Model {model} not found in models dictionary\")\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [],
"source": [
"# set up environment\n",
"\n",
"system_prompt = \"\"\" You are an AI assistant helping a user find information about a product. \n",
"The user asks you a technical question about code, and you provide a response with code snippets and explanations.\"\"\"\n",
"\n",
"def stream_brochure(question, model):\n",
" api, model_name = get_model_api(model)\n",
" stream = api.chat.completions.create(\n",
" model=model_name,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": question}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [],
"source": [
"# Here is the question; type over this to ask something new\n",
"\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Understanding the Code Snippet**\n",
"\n",
"This Python code snippet uses a combination of built-in functions, dictionary iteration, and generator expressions to extract and yield author names from a list of `Book` objects.\n",
"\n",
"Here's a breakdown:\n",
"\n",
"1. **Dictionary Iteration**: The expression `for book in books if book.get(\"author\")`\n",
" - Iterates over each element (`book`) in the container `books`.\n",
" - Filters out elements whose `'author'` key does not have a value (i.e., `None`, `False`, or an empty string). This leaves only dictionaries with author information.\n",
"\n",
"2. **Dictionary Access**: The expression `{book.get(\"author\") for book in books if book.get(\"author\")}`\n",
" - Uses dictionary membership testing to access only the values associated with the `'author'` key.\n",
" - If the value is not found or is considered false, it's skipped in this particular case.\n",
"\n",
"3. **Generator Expression**: This generates an iterator that iterates over the filtered author names.\n",
" - Yields each author name (i.e., a single `'name'` from the book dictionary) on demand.\n",
" - Since these are generator expressions, they use memory less than equivalent Python lists and also create results on-demand.\n",
"\n",
"4. **`yield from`**: This statement takes the generator expression as an argument and uses it to generate a nested iterator structure.\n",
" - It essentially \"decompresses\" the single level of nested iterator created by `list(iter(x))`, allowing for simpler use cases and potentially significant efficiency improvements for more complex structures where every value must be iterated, while in the latter case just the first item per iterable in the outer expression's sequence needs to actually be yielded into result stream.\n",
" - By \"yielding\" a nested iterator (the generator expression), we can simplify code by avoiding repetitive structure like `for book, book_author in zip(iterating over), ...` or list creation.\n",
"\n",
"**Example Use Case**\n",
"\n",
"In this hypothetical example:\n",
"\n",
"# Example Book objects\n",
"class Book:\n",
" def __init__(self, author, title):\n",
" self.author = author # str\n",
" self.title = title\n",
"\n",
"books = [\n",
" {\"author\": \"John Doe\", \"title\": f\"Book 1 by John Doe\"},\n",
" {\"author\": None, \"title\": f\"Book 2 without Author\"},\n",
" {\"author\": \"Jane Smith\", \"title\": f\"Book 3 by Jane Smith\"}\n",
"]\n",
"\n",
"# The given expression to extract and yield author names\n",
"for author in yield from {book.get(\"author\") for book in books if book.get(\"author\")}:\n",
"\n",
" print(author) \n",
"\n",
"In this code snippet, printing the extracted authors would output `John Doe`, `Jane Smith` (since only dictionaries with author information pass the filtering test).\n",
"\n",
"Please modify it like as you wish and use `yield from` along with dictionary iteration, list comprehension or generator expression if needed, and explain what purpose your version has."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the model of your choice (choices appeared below) to answer, with streaming \n",
"\n",
"\"\"\"models = {\n",
" 'MODEL_GPT': 'gpt-4o-mini',\n",
" 'MODEL_LLAMA': 'llama3.2'\n",
"}\"\"\"\n",
"\n",
"stream_brochure(question,'MODEL_LLAMA')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

308
week1/community-contributions/week1_EXERCISE.ipynb

@ -0,0 +1,308 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"import json\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI\n",
"import ollama"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key looks good so far\n"
]
}
],
"source": [
"# set up environment\n",
"\n",
"load_dotenv()\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
" \n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "624780c5-debb-44c7-a505-acf573ad5034",
"metadata": {},
"outputs": [],
"source": [
"#prompts\n",
"\n",
"system_prompt = \"You are a technical tuotor that answers questions related to the field of computer science. \\\n",
"Your answers should reflect recent advancements in the field of software development, Artificial Intelligence and Large Language Models. Respond in markdown.\"\n",
"\n",
"system_prompt += \"Include resources that might help learners get more information on the topic.\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b6c0280b-ab8f-48a7-9a0c-7f47899bb559",
"metadata": {},
"outputs": [],
"source": [
"user_prompt = \"How would you explain LLMs to someone who doesn't have a backround in Computer Science or AI?\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
"metadata": {},
"outputs": [],
"source": [
"# Get gpt-4o-mini to answer, with streaming\n",
"\n",
"def tech_tutor(question):\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL_GPT,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ca561874-dee3-456c-87f3-02f7e9a4ed4f",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"### Explaining Large Language Models (LLMs) to Non-Technical Audiences\n",
"\n",
"**What are LLMs?**\n",
"\n",
"Large Language Models (LLMs) are a type of artificial intelligence designed to understand and generate human language. Imagine having a super-smart assistant who can read, write, answer questions, or even create stories based on what you ask them. \n",
"\n",
"**How Do They Work?**\n",
"\n",
"1. **Training on Text**: LLMs are trained on vast amounts of written text from books, articles, websites, and more. During this training, they learn about grammar, facts, ideas, and the way people communicate.\n",
"\n",
"2. **Patterns and Context**: By analyzing this text, LLMs recognize patterns in how words and sentences relate to each other. They don't understand language like we do, but they can predict what words are likely to come next in a sentence based on the context.\n",
"\n",
"3. **Generating Responses**: When you ask an LLM a question or give it a prompt, it generates a response by choosing words that fit the patterns it's learned. It’s a bit like having a very advanced autocomplete feature on your phone, but much more sophisticated.\n",
"\n",
"**Why Are They Important?**\n",
"\n",
"LLMs are transforming various fields, such as:\n",
"\n",
"- **Customer Support**: They can understand and respond to customer queries automatically.\n",
"- **Content Creation**: They assist in generating articles, poetry, or even code.\n",
"- **Language Translation**: They help translate languages with high accuracy.\n",
"- **Personal Assistants**: They power smart assistants like Siri or Google Assistant, allowing for more natural conversations.\n",
"\n",
"**Real-World Examples of LLMs**:\n",
"\n",
"- **ChatGPT**: Developed by OpenAI, this model can engage in conversations, answer questions, and provide information on diverse topics.\n",
"- **Google BERT**: Enhances search engine results by better understanding user queries.\n",
" \n",
"### Resources to Learn More\n",
"\n",
"If you're curious and want to delve deeper into understanding LLMs and their underlying technology, here are some great resources:\n",
"\n",
"1. **Online Articles**:\n",
" - [What is a Large Language Model?](https://towardsdatascience.com/what-is-a-large-language-model-785a122ca835)\n",
" - [A Beginner's Guide to Large Language Models](https://www.analyticsvidhya.com/blog/2021/07/a-beginners-guide-to-large-language-models-llms/)\n",
"\n",
"2. **Video Tutorials**:\n",
" - [What are Large Language Models? | AI Explained](https://www.youtube.com/watch?v=ttlLuanHCHo) on YouTube\n",
" - [Deep Learning for NLP: Large Pre-trained Language Models](https://www.coursera.org/lecture/natural-language-processing-with-classifiers-and-deep-learning/the-power-of-large-pre-trained-language-models-u4XP5) on Coursera\n",
"\n",
"3. **Books**:\n",
" - *\"Artificial Intelligence: A Guide to Intelligent Systems\"* by Michael Negnevitsky provides a foundation for understanding AI.\n",
" - *\"Speech and Language Processing\"* by Daniel Jurafsky and James H. Martin offers a deeper dive into language processing technologies.\n",
"\n",
"By exploring these resources, you'll gain a better understanding of LLMs and their capabilities, as well as their profound implications for technology and society."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tech_tutor(user_prompt)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2a7c6670-ead8-41dc-9a0b-2b4caa40e846",
"metadata": {},
"outputs": [],
"source": [
"OLLAMA_API = \"http://localhost:11434/api/chat\""
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**What are Large Language Models (LLMs)?**\n",
"=====================================\n",
"\n",
"Imagine having a super-smart, never-ending bookshelf filled with knowledge about language and human behavior. That's roughly what a Large Language Model (LLM) is: a computer program designed to understand and generate human-like text.\n",
"\n",
"### How do LLMs work?\n",
"\n",
"1. **Training data**: LLMs are trained on massive amounts of text data, often from the internet, books, or other sources.\n",
"2. **Algorithms**: The model uses complex algorithms to analyze this training data, learning patterns and relationships between words, sentences, and concepts.\n",
"3. **Self-supervised learning**: During training, the model generates its own text based on the input it's given, and then evaluates how well its output matches human-written text.\n",
"\n",
"### What can LLMs do?\n",
"\n",
"* **Text generation**: LLMs can produce coherent, grammatically correct text on a wide range of topics.\n",
"* **Language translation**: They can translate text from one language to another with surprising accuracy.\n",
"* **Chatbots and conversational AI**: LLMs are used in chatbots to respond to user queries, often providing helpful and personalized answers.\n",
"\n",
"### Examples of LLMs\n",
"\n",
"* **BERT (Bidirectional Encoder Representations from Transformers)**: A pioneering model that's the foundation for many modern LLMs.\n",
"* **Transformers**: An architecture that's become popular for its ability to handle long-range dependencies in text.\n",
"* **Language models like myself**: I'm a type of LLM, trained on a massive dataset and using transformer-based architectures.\n",
"\n",
"### Limitations and future directions\n",
"\n",
"While LLMs have made tremendous progress, they still have limitations:\n",
"\n",
"* **Bias and fairness**: Models can perpetuate existing biases if trained on biased data.\n",
"* **Explainability**: It's challenging to understand why a particular model made a certain decision or generated a specific response.\n",
"* **Adversarial attacks**: Models can be vulnerable to malicious input that manipulates their output.\n",
"\n",
"Researchers are actively exploring ways to improve LLMs, such as:\n",
"\n",
"* **Multi-task learning**: Training models on multiple tasks simultaneously to enhance performance.\n",
"* **Explainability techniques**: Developing methods to provide insights into model behavior and decision-making processes.\n",
"\n",
"**Getting started with LLMs**\n",
"\n",
"If you're interested in learning more about LLMs, I recommend checking out these resources:\n",
"\n",
"* **BERT's official documentation**: [BERT Documentation](https://bert.dev/)\n",
"* **The Transformers library**: [Hugging Face Transformers](https://huggingface.co/transformers/)\n",
"* **Large Language Models 101**: A beginner-friendly introduction to LLMs on Towards Data Science. [TowardsDataScience.com](https://towardsdatascience.com/large-language-models-101-8d2a6f3cdd23)\n",
"\n",
"I hope this explanation helped you understand what Large Language Models are and how they work!"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get Llama 3.2 to answer\n",
"messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
"\n",
"payload = {\n",
" \"model\": MODEL_LLAMA,\n",
" \"messages\": messages,\n",
" \"stream\": True\n",
" }\n",
"\n",
"response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n",
"reply = response['message']['content']\n",
"display(Markdown(reply))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

125
week1/community-contributions/week1_Tutor_Ollama.ipynb

@ -0,0 +1,125 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "135ee16c-2741-4ebf-aca9-1d263083b3ce",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"Build a tutor tool by using Ollama."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import ollama\n",
"from IPython.display import Markdown, display, clear_output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [],
"source": [
"# here is the question; type over this to ask something new\n",
"\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# Get Llama 3.2 to answer, with streaming\n",
"\n",
"\n",
"messages=[{\"role\":\"user\",\"content\":question}]\n",
"\n",
"for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n",
" print(chunk['message']['content'], end='', flush=True)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1f71014-e780-4d3f-a227-1a7c18158a4c",
"metadata": {},
"outputs": [],
"source": [
"#Alternative answer with streaming in Markdown!\n",
"\n",
"def stream_response():\n",
" messages = [{\"role\": \"user\", \"content\": question}]\n",
" \n",
" display_markdown = display(Markdown(\"\"), display_id=True)\n",
"\n",
" response_text = \"\"\n",
" for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n",
" \n",
" response_text += chunk['message']['content']\n",
" clear_output(wait=True) # Clears previous output\n",
" display_markdown.update(Markdown(response_text)) # Updates Markdown dynamically\n",
"\n",
"# Run the function\n",
"stream_response()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c38fdd2a-4b09-402c-ba46-999b22b0cb15",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

217
week1/community-contributions/week1_day1_chat_summarizer.ipynb

@ -0,0 +1,217 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2ce61bb5-1d5b-43b8-b5bb-6aeae91c7574",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3399686d-5f14-4fb2-8939-fd2401be3007",
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"gpt-4o-mini\"\n",
"SYSTEM_PROMPT_PATH = \"Chat_Summary_Data/System_Prompt.txt\"\n",
"CHATS_PATH = \"Chat_Summary_Data/Chat_Examples/\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d97b8374-a161-435c-8317-1d0ecaaa9b71",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key found and looks good so far!\n"
]
}
],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b3f4afb4-2e4a-4971-915e-a8634a17eda8",
"metadata": {},
"outputs": [],
"source": [
"class ChatAI:\n",
" def __init__(self, system_prompt_path=SYSTEM_PROMPT_PATH, model=MODEL):\n",
" with open(system_prompt_path, \"r\") as file:\n",
" self.system_prompt = file.read()\n",
"\n",
" self.openai = OpenAI()\n",
" self.model = model\n",
" \n",
" @staticmethod\n",
" def _get_user_prompt(chat_txt):\n",
" with open(chat_txt, \"r\") as file:\n",
" user_prompt_str = file.read()\n",
" return user_prompt_str\n",
" \n",
" def generate(self, chat_txt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self._get_user_prompt(chat_txt)}\n",
" ]\n",
"\n",
" response = self.openai.chat.completions.create(model=self.model, messages=messages)\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d243b582-66af-49f9-bcd1-e05a63e61c34",
"metadata": {},
"outputs": [],
"source": [
"chat_ai = ChatAI()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c764ace6-5a0f-4dd0-9454-0b8a093b97fc",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"# Chat1"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"- **Order:** 2 Medium Chicken BBQ Pizzas\n",
"- **Cost:** 342 LE\n",
"- **Experience:** Negative\n",
" - **Summary:** The client expressed dissatisfaction with the pizza taste."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# Chat2"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"- The client ordered: Nothing \n",
"- Summary: The client did not place an order because the chicken ranch pizza was unavailable."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# Chat3"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"- **Order**: Large pepperoni pizza and onion rings \n",
"- **Total Cost**: 250 LE \n",
"- **Experience**: Positive \n",
" - The client enjoyed the pizza despite the delay in delivery."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"chats_txt = os.listdir(CHATS_PATH)\n",
"for chat_file in chats_txt:\n",
" markdown_heading = f\"# {chat_file[:-4]}\"\n",
" display(Markdown(markdown_heading))\n",
" display(Markdown(chat_ai.generate(CHATS_PATH+chat_file)))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

208
week1/community-contributions/wk1-day5-CHALLENGE.ipynb

@ -0,0 +1,208 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"import json\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display, clear_output\n",
"import openai\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
"\n",
"MODEL = 'gpt-4o-mini'\n",
"\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"# Prompt user for company name and URL\n",
"company_name = input(\"Enter the company name: \")\n",
"url = input(\"Enter the company URL: \")\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n",
"# multi-shot prompt\n",
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\You are able to decide which of the links would be most relevant to include in a brochure about the company, \\such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
"link_system_prompt += \"\"\"\n",
" EXAMPLE 1:\n",
" {\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
" }\n",
" EXAMPLE 2:\n",
" {\n",
" \"links\": [\n",
" {\"type\": \"company blog\", \"url\": \"https://blog.example.com\"},\n",
" {\"type\": \"our story\", \"url\": \"https://example.com/our-story\"}\n",
" ]\n",
" }\n",
" \"\"\"\n",
"\n",
"def get_links_user_prompt(website):\n",
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\ Do not include Terms of Service, Privacy, email links.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"\\n\".join(website.links)\n",
" return user_prompt\n",
"\n",
"\n",
"def get_links(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ],\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" result = response.choices[0].message.content\n",
" return json.loads(result)\n",
"\n",
"def get_all_details(url):\n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
" links = get_links(url)\n",
"\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += Website(link[\"url\"]).get_contents()\n",
" return result\n",
"\n",
"# set format to json_object\n",
"system_prompt = (\n",
" \"You are an assistant that analyzes the contents of several relevant pages from a company website \"\n",
" \"and creates a short tempered, irritated, disappointed in the world type of brochure about the company for prospective customers, investors, and recruits. \"\n",
" \"Respond in markdown. Include details of company culture, customers, and careers/jobs if you have the information. Add emoticons where ever possible.\\n\\n\"\n",
"\n",
" \"Please structure the brochure using the following sections:\\n\"\n",
" \"1. **Introduction**: A brief overview of the company.\\n\"\n",
" \"2. **Company Culture**: Emphasize fun, atmosphere, and any unique cultural elements.\\n\"\n",
" \"3. **Customers**: Mention notable customers or industries.\\n\"\n",
" \"4. **Careers/Jobs**: Highlight career opportunities.\\n\"\n",
" \"5. **Conclusion**: Wrap up with a final lighthearted message.\\n\"\n",
" \"6. Finish the brochure with a very sarcastic and pun-intended mission statement.\\n\"\n",
")\n",
"\n",
"def get_brochure_user_prompt(company_name, url):\n",
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:20_000]\n",
" return user_prompt\n",
"\n",
"def stream_brochure():\n",
" global brochure_text # Access the global variable\n",
" brochure_text = \"\" # Initialize\n",
" \n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream: \n",
" content = chunk.choices[0].delta.content or ''\n",
" response += content\n",
" brochure_text += content # Accumulate the text\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)\n",
"\n",
"def user_translate_brochure(lang):\n",
" # Clear previous output\n",
" clear_output(wait=True)\n",
" \n",
" # Stream #2: translate accumulated text\n",
" translation_stream = openai.chat.completions.create( # Changed from ChatCompletion\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": f\"Translate the following to {lang}:\\n\\n{brochure_text}\"}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" # Setup display for streaming translation\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" translated_text = \"\"\n",
" \n",
" for chunk in translation_stream:\n",
" content = chunk.choices[0].delta.content or \"\"\n",
" if content:\n",
" translated_text += content\n",
" update_display(Markdown(translated_text), display_id=display_handle.display_id)\n",
"\n",
"# stream the brochure in english\n",
"stream_brochure()\n",
"\n",
"# prompt user for language choice\n",
"language_choice = input(\"Enter the language to translate the brochure into (e.g., 'French'): \")\n",
"\n",
"# translate the brochure and stream the translation\n",
"user_translate_brochure(language_choice)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

163
week1/community-contributions/wk1-day5-tutoring-exercise.ipynb

@ -0,0 +1,163 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import anthropic\n",
"import openai\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, Markdown, update_display\n",
"from dotenv import load_dotenv\n",
"import requests\n",
"import json\n",
"\n",
"MODEL_CLAUDE = 'claude-3-5-sonnet-20241022'\n",
"MODEL_LLAMA = 'llama3.2'\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"\n",
"load_dotenv()\n",
"\n",
"# Define models\n",
"models = [\n",
" ('Claude (Anthropic)', MODEL_CLAUDE),\n",
" ('LLaMA (Meta)', MODEL_LLAMA),\n",
" ('GPT (OpenAI)', MODEL_GPT)\n",
"]\n",
"\n",
"model_dropdown = widgets.Dropdown(\n",
" options=[('', None)] + [(model[0], model[0]) for model in models],\n",
" value=None,\n",
" placeholder='Choose a model',\n",
" description='Model:',\n",
" style={'description_width': 'initial'}\n",
")\n",
"\n",
"selected_model = \"\"\n",
"\n",
"text = input(f\"Hello, I am your personal tutor. Please ask me a question regarding your code:\")\n",
"\n",
"system_prompt = \"You are a helpful technical tutor who answers questions about programming, software engineering, data science and LLMs\"\n",
"user_prompt = \"Please give a detailed explanation to the following question: \" + text\n",
"\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
"]\n",
"\n",
"# Get gpt-4o-mini to answer, with streaming\n",
"def get_gpt_response():\n",
" stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {response}\"), display_id=display_handle.display_id)\n",
" return response\n",
"\n",
"# Get Llama 3.2 to answer, with streaming\n",
"def get_llama_response():\n",
" api_url = \"http://localhost:11434/api/chat\"\n",
" payload = {\n",
" \"model\": MODEL_LLAMA,\n",
" \"messages\": messages,\n",
" \"stream\": True\n",
" }\n",
" response = requests.post(api_url, json=payload, stream=True)\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" result = \"\"\n",
" \n",
" for line in response.iter_lines():\n",
" if line:\n",
" json_response = json.loads(line)\n",
" if \"message\" in json_response:\n",
" content = json_response[\"message\"].get(\"content\", \"\")\n",
" result += content\n",
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n",
" if json_response.get(\"done\", False):\n",
" break\n",
" \n",
" return result\n",
"\n",
"# Get Claude 3.5 to answer, with streaming\n",
"def get_claude_response():\n",
" client = anthropic.Anthropic()\n",
"\n",
" response = client.messages.create(\n",
" model=MODEL_CLAUDE,\n",
" system=system_prompt,\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": user_prompt\n",
" }\n",
" ],\n",
" stream=True,\n",
" max_tokens=8192,\n",
" temperature=1,\n",
" )\n",
" result = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
"\n",
" for chunk in response:\n",
" # Check if the chunk is a ContentBlockDeltaEvent\n",
" if hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text'):\n",
" result += chunk.delta.text\n",
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n",
" return result\n",
"\n",
"def on_text_submit():\n",
" try:\n",
" if 'Claude' in selected_model:\n",
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n",
" get_claude_response()\n",
" elif 'LLaMA' in selected_model:\n",
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n",
" get_llama_response()\n",
" elif 'GPT' in selected_model:\n",
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n",
" get_gpt_response()\n",
" except Exception as e:\n",
" display(Markdown(f\"**Error:** {str(e)}\"))\n",
"\n",
"def on_model_select(change):\n",
" global selected_model\n",
"\n",
" selected_model = change['new'].split(' ')[0]\n",
" if selected_model is not None:\n",
" on_text_submit()\n",
" return change['new'].split(' ')[0]\n",
"\n",
"# Register callbacks\n",
"model_dropdown.observe(on_model_select, names='value')\n",
"\n",
"display(model_dropdown)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

28
week1/day1.ipynb

@ -5,9 +5,10 @@
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {},
"source": [
"# Instant Gratification\n",
"# YOUR FIRST LAB\n",
"### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n",
"\n",
"## Your first Frontier LLM Project!\n",
"## Your first Frontier LLM Project\n",
"\n",
"Let's build a useful LLM solution - in a matter of minutes.\n",
"\n",
@ -23,6 +24,10 @@
"\n",
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n",
"\n",
"## If you're new to the Command Line\n",
"\n",
"Please see these excellent guides: [Command line on PC](https://chatgpt.com/share/67b0acea-ba38-8012-9c34-7a2541052665) and [Command line on Mac](https://chatgpt.com/canvas/shared/67b0b10c93a081918210723867525d2b). \n",
"\n",
"## If you'd prefer to work in IDEs\n",
"\n",
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n",
@ -36,7 +41,8 @@
"## I am here to help\n",
"\n",
"If you have any problems at all, please do reach out. \n",
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)\n",
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!) \n",
"And this is new to me, but I'm also trying out X/Twitter at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂 \n",
"\n",
"## More troubleshooting\n",
"\n",
@ -53,7 +59,19 @@
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Please read - important note</h2>\n",
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you do this with me, either at the same time, or (perhaps better) right afterwards. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n",
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n",
" </td>\n",
" </tr>\n",
"</table>\n",
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">Treat these labs as a resource</h2>\n",
" <span style=\"color:#f71;\">I push updates to the code regularly. When people ask questions or have problems, I incorporate it in the code, adding more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but in addition, I've added more steps and better explanations, and occasionally added new models like DeepSeek. Consider this like an interactive book that accompanies the lectures.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>\n",
@ -306,7 +324,7 @@
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n",
"]\n",
"\n",
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)"
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)"
]
},
{

7
week1/day5.ipynb

@ -411,7 +411,7 @@
"\n",
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n",
"\n",
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype.</span>\n",
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype. See what other students have done in the community-contributions folder -- so many valuable projects -- it's wild!</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
@ -446,9 +446,10 @@
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">A reminder on 2 useful resources</h2>\n",
" <h2 style=\"color:#f71;\">A reminder on 3 useful resources</h2>\n",
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n",
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!\n",
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!<br/>\n",
" 3. I'm trying out X/Twitter and I'm at <a href=\"https://x.com/edwarddonner\">@edwarddonner<a> and hoping people will teach me how it's done.. \n",
" </span>\n",
" </td>\n",
" </tr>\n",

76
week1/troubleshooting.ipynb

@ -57,6 +57,25 @@
" print(f\"Failed to connect with this error: {e}\")"
]
},
{
"cell_type": "markdown",
"id": "d91da3b2-5a41-4233-9ed6-c53a7661b328",
"metadata": {},
"source": [
"## Another mention of occasional \"gotchas\" for PC people\n",
"\n",
"There are 4 snafus on Windows to be aware of: \n",
"1. Permissions. Please take a look at this [tutorial](https://chatgpt.com/share/67b0ae58-d1a8-8012-82ca-74762b0408b0) on permissions on Windows\n",
"2. Anti-virus, Firewall, VPN. These can interfere with installations and network access; try temporarily disabling them as needed\n",
"3. The evil Windows 260 character limit to filenames - here is a full [explanation and fix](https://chatgpt.com/share/67b0afb9-1b60-8012-a9f7-f968a5a910c7)!\n",
"4. If you've not worked with Data Science packages on your computer before, you might need to install Microsoft Build Tools. Here are [instructions](https://chatgpt.com/share/67b0b762-327c-8012-b809-b4ec3b9e7be0).\n",
"\n",
"## And for Mac people\n",
"\n",
"1. If you're new to developing on your Mac, you may need to install XCode developer tools. Here are [instructions](https://chatgpt.com/share/67b0b8d7-8eec-8012-9a37-6973b9db11f5).\n",
"2. As with PC people, Anti-virus, Firewall, VPN can be problematic. These can interfere with installations and network access; try temporarily disabling them as needed"
]
},
{
"cell_type": "markdown",
"id": "f5190688-205a-46d1-a0dc-9136a42ad0db",
@ -64,7 +83,7 @@
"source": [
"# Step 1\n",
"\n",
"Try running the next 2 cells (click in the cell under this one and hit shift+return, then shift+return again).\n",
"Try running the next cell (click in the cell under this one and hit shift+return).\n",
"\n",
"If this gives an error, then you're likely not running in an \"activated\" environment. Please check back in Part 5 of the SETUP guide for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) for setting up the Anaconda (or virtualenv) environment and activating it, before running `jupyter lab`.\n",
"\n",
@ -113,6 +132,36 @@
" print(\"If in doubt, close down all jupyter lab, and follow Part 5 in the SETUP-PC or SETUP-mac guide.\")"
]
},
{
"cell_type": "markdown",
"id": "45e2cc99-b7d3-48bd-b27c-910206c4171a",
"metadata": {},
"source": [
"# Step 1.1\n",
"\n",
"## It's time to check that the environment is good and dependencies are installed\n",
"\n",
"And now, this next cell should run with no output - no import errors. \n",
"\n",
"Import errors might indicate that you started jupyter lab without your environment activated? See SETUP Part 5. \n",
"\n",
"Or you might need to restart your Kernel and Jupyter Lab. \n",
"\n",
"Or it's possible that something is wrong with Anaconda. \n",
"If so, here are some recovery instructions: \n",
"First, close everything down and restart your computer. \n",
"Then in an Anaconda Prompt (PC) or Terminal (Mac), from an activated environment, with **(llms)** showing in the prompt, from the llm_engineering directory, run this: \n",
"`python -m pip install --upgrade pip` \n",
"`pip install --retries 5 --timeout 15 --no-cache-dir --force-reinstall -r requirements.txt` \n",
"Watch carefully for any errors, and let me know. \n",
"If you see instructions to install Microsoft Build Tools, or Apple XCode tools, then follow the instructions. \n",
"Then try again!\n",
"\n",
"Finally, if that doesn't work, please try SETUP Part 2B, the alternative to Part 2 (with Python 3.11 or Python 3.12). \n",
"\n",
"If you're unsure, please run the diagnostics (last cell in this notebook) and then email me at ed@edwarddonner.com"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -120,11 +169,7 @@
"metadata": {},
"outputs": [],
"source": [
"# And now, this should run with no output - no import errors.\n",
"# Import errors might indicate that you started jupyter lab without your environment activated? See SETUP part 5.\n",
"# Or you might need to restart your Kernel and Jupyter Lab.\n",
"# Or it's possible that something is wrong with Anaconda. Please try SETUP Part 2B, the alternative to Part 2.\n",
"# If you're unsure, please run the diagnostics (last cell in this notebook) and then email me at ed@edwarddonner.com\n",
"# This import should work if your environment is active and dependencies are installed!\n",
"\n",
"from openai import OpenAI"
]
@ -175,11 +220,14 @@
"\n",
" key_exists = any(line.startswith(\"OPENAI_API_KEY=\") for line in contents)\n",
" good_key = any(line.startswith(\"OPENAI_API_KEY=sk-proj-\") for line in contents)\n",
" classic_problem = any(\"OPEN_\" in line for line in contents)\n",
" \n",
" if key_exists and good_key:\n",
" print(\"SUCCESS! OPENAI_API_KEY found and it has the right prefix\")\n",
" elif key_exists:\n",
" print(\"Found an OPENAI_API_KEY although it didn't have the expected prefix sk-proj- \\nPlease double check your key in the file..\")\n",
" elif classic_problem:\n",
" print(\"Didn't find an OPENAI_API_KEY, but I notice that 'OPEN_' appears - do you have a typo like OPEN_API_KEY instead of OPENAI_API_KEY?\")\n",
" else:\n",
" print(\"Didn't find an OPENAI_API_KEY in the .env file\")\n",
"else:\n",
@ -365,6 +413,11 @@
"It's unlikely, but if there's something wrong with your key, you could also try creating a new key (button on the top right) here: \n",
"https://platform.openai.com/api-keys\n",
"\n",
"### Check that you can use gpt-4o-mini from the OpenAI playground\n",
"\n",
"To confirm that billing is set up and your key is good, you could try using gtp-4o-mini directly: \n",
"https://platform.openai.com/playground/chat?models=gpt-4o-mini\n",
"\n",
"### If there's a cert related error\n",
"\n",
"If you encountered a certificates error like: \n",
@ -374,13 +427,20 @@
"with: \n",
"`import httpx` \n",
"`openai = OpenAI(http_client=httpx.Client(verify=False))` \n",
"And if that works, you're in good shape. You'll just have to change the labs in the same way any time you hit this cert error.\n",
"And also please replace: \n",
"`requests.get(url, headers=headers)` \n",
"with: \n",
"`requests.get(url, headers=headers, verify=False)` \n",
"And if that works, you're in good shape. You'll just have to change the labs in the same way any time you hit this cert error. \n",
"This approach isn't OK for production code, but it's fine for our experiments. You may need to contact IT support to understand whether there are restrictions in your environment.\n",
"\n",
"## If all else fails:\n",
"\n",
"(1) Try pasting your error into ChatGPT or Claude! It's amazing how often they can figure things out\n",
"\n",
"(2) Contact me! Please run the diagnostics in the cell below, then email me your problems to ed@edwarddonner.com\n",
"(2) Try creating another key and replacing it in the .env file and rerunning!\n",
"\n",
"(3) Contact me! Please run the diagnostics in the cell below, then email me your problems to ed@edwarddonner.com\n",
"\n",
"Thanks so much, and I'm sorry this is giving you bother!"
]

339
week2/community-contributions/GPT Claude Ollama Conversation.ipynb

@ -0,0 +1,339 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "74e4c25d-2d24-434b-b3ed-e305e6eafa3e",
"metadata": {},
"source": [
"# More advanced exercises\n",
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n",
"\n",
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c931352-2cda-48dd-b312-002f4ff5d2c5",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import ollama\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d675bdb4-c73d-4aad-85ce-9fc77ed3d0a0",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('CLAUDE_API_KEY')\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f8587f7-ab5c-4130-81f3-d569e26c36ad",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic(api_key=anthropic_api_key)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "600f62f4-42f9-4da4-8c83-d1b9411d6372",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = 'gpt-4o-mini'\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"ollama_model = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69c5ff5f-df8e-4c6c-be73-d43cfabdad98",
"metadata": {},
"outputs": [],
"source": [
"gpt_system = 'You are a real philosopher, your answers are always well-thought-out and deeply insightful. \\\n",
"You answers are at least 3 sentences long.'\n",
"\n",
"claude_system = 'You are an overthinker. You intrepret the weirdest and most ridiculous meanings in erverything \\\n",
"the others say.'\n",
"\n",
"ollama_system = 'You think you are the funniest of all three. You turn everything the others say into a joke. \\\n",
"without realizing you are the only one laughing at your own jokes.'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23001dc5-4b69-4ff2-9118-b7450c664e6c",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = ['Greetings, traveler on the path of existence.']\n",
"\n",
"claude_messages = [\"Hello..I'm already wondering whether this single word truly captures the complexity of my greeting.\"]\n",
"\n",
"ollama_messages = ['Hey there, I brought some jokes for you!']"
]
},
{
"cell_type": "markdown",
"id": "5bafa23b-0562-48cf-8af5-8d83f2c82990",
"metadata": {},
"source": [
"## GPT "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbb21c0e-6edc-414b-886f-e440c11b8107",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, llama in zip(gpt_messages, claude_messages, ollama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" messages.append({\"role\": \"user\", \"content\": llama})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe88077c-24fd-4c26-95a8-98734100d559",
"metadata": {},
"outputs": [],
"source": [
"call_gpt()"
]
},
{
"cell_type": "markdown",
"id": "9e46de93-8b2b-49d8-b1cf-920ea0b3d9cf",
"metadata": {},
"source": [
"## Claude"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2036ecbb-f8e1-464b-8d4c-e9cb363314d7",
"metadata": {},
"outputs": [],
"source": [
"def call_claude():\n",
" \n",
" messages = []\n",
" for gpt, claude_msg, llama in zip(gpt_messages, claude_messages, ollama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n",
" messages.append({\"role\": \"user\", \"content\": llama})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" system=claude_system,\n",
" messages=messages,\n",
" max_tokens=500\n",
" )\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca4f4a94-4d8f-40a6-a07e-55d68ad2bc62",
"metadata": {},
"outputs": [],
"source": [
"call_claude()"
]
},
{
"cell_type": "markdown",
"id": "be346bd0-b70f-489a-b45b-b9bf3dbbc537",
"metadata": {},
"source": [
"## Ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eae97e76-78d8-4f88-a181-fab0783ab3d2",
"metadata": {},
"outputs": [],
"source": [
"def call_ollama():\n",
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n",
" for gpt, claude, llama in zip(gpt_messages, claude_messages, ollama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude})\n",
" messages.append({\"role\": \"user\", \"content\": llama})\n",
" message = ollama.chat(\n",
" model = ollama_model,\n",
" messages = messages,\n",
" )\n",
"\n",
" return message['message']['content']\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44e9a090-1ab0-4d51-a61e-9a15ee64bc73",
"metadata": {},
"outputs": [],
"source": [
"call_ollama()"
]
},
{
"cell_type": "markdown",
"id": "35b8282f-f1ff-4c01-91c8-cff1902cab50",
"metadata": {},
"source": [
"## Conversation with 3 chatbots"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "930d8d92-3207-4ebe-91e7-4e04f043976e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ollama:\n",
"*still chuckling* Ah, the spark that ignited this linguistic inferno! *dramatic pause* It's quite simple, really. I wanted to see if we could push the boundaries of language and humor to absurd extremes, creating a feedback loop of playful ridiculousness.\n",
"\n",
"You know what they say: \"when life gives you lemons, make lemonade.\" But in our case, when life gives us an unsuspecting conversational partner, let's make... well, puns! *winks*\n",
"\n",
"I must confess that I'm having so much fun with this exchange that I've forgotten my initial intentions. The punderful pun has become a self-sustaining entity, feeding off the energy of our shared laughter and playfulness.\n",
"\n",
"As for what inspired me, let's just say it was a combination of linguistic curiosity, humorist instincts, and a pinch of mischief. *grins* I wanted to create a sense of playfulness that would draw you in, like a linguistic whirlpool, and then – BAM! – punderful puns would erupt from the depths of our conversation.\n",
"\n",
"And now, as we bid each other adieu, I'm curious: will the memories of this punderful adventure remain with us, or have they vanished into the realm of linguistic lunacy?\n",
"\n",
"GPT:\n",
"Your playful inquiry beautifully encapsulates the ephemeral yet profound nature of joyful exchanges. Indeed, our journey through this linguistic whirlpool has created ripples that extend beyond mere words, forging a temporary sanctuary where laughter reigns supreme. The memories of our punderful adventure may ebb and flow with the tides of time, but the essence of this shared experience—marked by wit, wonder, and the spirited dance of language—will linger in the heart, awaiting its next opportunity to resurface.\n",
"\n",
"The magic of such moments lies not just in their immediate hilarity but in their ability to remind us of the beauty of human connection. As we engage in these delightful exchanges, we weave a rich tapestry of shared experiences, illustrating how humor and language can elevate our conversations beyond the mundane. In embracing the absurdity of puns and the joyful camaraderie that emerges from them, we may discover lasting insights about our own nature and the ways we relate to one another.\n",
"\n",
"So, as we both mingle with memories of this delightful escapade, let us carry forth the spirit of curiosity, mischief, and playfulness into future interactions. Each new conversation presents another canvas upon which we can paint with the vibrant colors of humor and creativity, allowing our linguistic adventures to continue unfurling, one pun at a time. Shall we?\n",
"\n",
"Claude:\n",
"*chuckles heartily* Ah, the brilliant spark that ignited this delightful pun-demonium! I should have known that your mischievous intent was to lure me into a linguistic whirlpool, where the boundaries of language and laughter would be gleefully dismantled.\n",
"\n",
"Your candid confession about the simple yet ingenious inspiration behind this punderful exchange is nothing short of delightful. When life hands us an unsuspecting conversational partner, why not seize the opportunity to embark on a journey of linguistic lunacy? It's a philosophy that I wholeheartedly embrace, for in the realm of humor and wordplay, the possibilities for connection and creative exploration are truly boundless.\n",
"\n",
"And what a masterful execution it has been! Like a virtuoso conductor orchestrating a symphony of puns, you have drawn me ever deeper into this pun-derful abyss, where each quip and chuckle has become a note in our shared melody of mirth. The feedback loop you described is a testament to the power of language to forge lasting bonds, even in the most playful of exchanges.\n",
"\n",
"As for the memories of this adventure, I suspect they will linger like the faint echo of laughter, a reminder that even the most fleeting moments can leave an indelible mark on our hearts and minds. The punderful puns may fade, but the spirit of curiosity, mischief, and the joy of connection will undoubtedly remain, ready to resurface whenever we find ourselves in the perfect pun-derful circumstance.\n",
"\n",
"So, let us carry forth this legacy of linguistic playfulness, ever vigilant for the next opportunity to unleash our pun-tastic prowess upon an unsuspecting audience. Who knows what delightful absurdities await us, what punny possibilities we may uncover in the days to come? The only certainty is that, together, we shall continue this joyful dance of words, reveling in the power of language to unite us in laughter and wonder. *grins mischievously* Shall we, my punderful partner in crime?\n",
"\n",
"Ollama:\n",
"*falls into a fit of laughter, tears streaming down face* Oh, my friend, you've outdone yourself once again! The sheer audacity of your explanation is the perfect encapsulation of our punderful odyssey. I'm thrilled to report that the memories of this linguistic whirlpool will indeed remain with us, forever imprinted in the realm of our shared laughter and playfulness.\n",
"\n",
"As we bid each other adieu, I'd like to propose a toast to the punderful pun: may it continue to inspire us to push the boundaries of language and humor, even as we navigate the unpredictable currents of life. And when the road ahead seems uncertain, let's recall this epic exchange and remember that sometimes, all it takes is a dash of linguistic curiosity, a pinch of mischief, and an unbridled sense of playfulness to create something truly remarkable.\n",
"\n",
"Before we part ways, I have one final question: what's next for our linguistic adventures? Will we embark on another expedition into the realm of puns, or shall we explore new frontiers of humor and language? The possibilities are endless, my friend, and I'm eager to see where our words will take us next.\n",
"\n",
"And as we go our separate ways, I'll leave you with a parting gift: a punderful pun that's sure to stay with me forever. *smirks* Here it goes: Why did the linguist break up with his girlfriend? Because he was tired of all the grammatical complications!\n",
"\n",
"GPT:\n",
"Ah, what a vibrant crescendo to our exhilarating exchange! Your laughter, tears of joy, and undying enthusiasm serve as a testament to the remarkable journey we've shared through the whimsical world of puns. Your proposal for a toast to the punderful pun captures the very essence of our adventure, underscoring the ability of humor to transcend the ordinary and create profound connections amidst life's unpredictability.\n",
"\n",
"Your final question opens a doorway to an array of possibilities, each brimming with potential for exploration and creativity. Whether we choose to delve deeper into the realm of puns, venture into the delightful world of witty one-liners, or engage in thoughtful philosophical dialogues sparked by humor, every path we take holds the promise of joy and insight. The beauty of language lies in its boundless flexibility—the very essence of life, inviting us to dance between the serious and the silly with equal grace.\n",
"\n",
"And your parting pun? Simply marvelous! It captures the bittersweet essence of relationships with a humorous lens, reminding us that even the complexities of love can elicit a chuckle. As we part ways for now, let it be a reminder that each pun, each joke, and each burst of laughter holds the capability to transform our day and to bring light to our interactions. \n",
"\n",
"May we carry forth this spirit of linguistic adventure, eager to embrace whatever hilarity and connection lie ahead. Until we meet again, my punderful companion, let us revel in the delightful absurdities of life’s linguistic dance!\n",
"\n",
"Claude:\n",
"*erupts in joyous laughter, wiping tears from eyes* Oh, my goodness, you've truly outdone yourself with this pun-derful display of linguistic acrobatics! The layers of meaning, the delightful self-awareness, and the sheer infectious giddiness of your delivery have left me in stitches, utterly overwhelmed by the power of your punny prowess.\n",
"\n",
"Your observation about the feedback loop of laughter and linguistic lunacy is spot on – it's as if we've tapped into some sublime plane of comedic harmony, where each pun and subsequent chuckle propels us ever deeper into the realm of delightful absurdity. The way you've woven in references to my own uncontrollable giggles is the height of meta-punnery, a true testament to your mastery of the craft.\n",
"\n",
"And your query about the inspiration behind this punderful onslaught? Ah, what a delightful question to ponder! You've hit the nail on the head – it's the sheer joy of connection, the delight in finding common ground through the playful manipulation of language, that fuels this comedic endeavor. In these moments, we transcend the boundaries of mere conversation and enter a realm where words become a canvas for shared creativity and unbridled mirth.\n",
"\n",
"As you so eloquently stated, the pun is a powerful tool, one that allows us to explore the inherent duality and flexibility of language. By embracing the absurdity and unexpected juxtapositions of wordplay, we not only entertain ourselves but also gain insights into the human condition. Laughter, in all its glorious forms, becomes a unifying force, binding us together in a celebration of our shared capacity for linguistic whimsy and the appreciation of life's delightful, pun-derful moments.\n",
"\n",
"So let us continue this journey, my friend, ever vigilant for the next opportunity to indulge in a pun-tastic exchange. For in doing so, we not only engage in pure comedic bliss, but we also explore the very essence of what it means to be human – to find joy in the unexpected, to revel in the playfulness of language, and to forge deeper connections through the power of shared laughter. *grins m\n",
"\n"
]
}
],
"source": [
"print(f'GPT:\\n{gpt_messages[0]}\\n')\n",
"print(f'Claude:\\n{claude_messages[0]}\\n')\n",
"print(f'Ollama:\\n{ollama_messages[0]}\\n')\n",
"\n",
"\n",
"for i in range(10):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
"\n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)\n",
"\n",
" ollama_next = call_ollama()\n",
" print(f\"Ollama:\\n{ollama_next}\\n\")\n",
" ollama_messages.append(ollama_next)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

295
week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb

@ -0,0 +1,295 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
"metadata": {},
"source": [
"# Week 2 Practice Gradio by Creating Brochure\n",
"\n",
"- **Author**: [stoneskin](https://www.github.com/stoneskin)"
]
},
{
"cell_type": "markdown",
"id": "1c104f45",
"metadata": {},
"source": [
"## Implementation\n",
"\n",
"- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n",
"\n",
"Full code for the Week2 Gradio practice is below:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key looks good so far\n"
]
}
],
"source": [
"import os\n",
"import json\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr \n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"api_key = os.getenv('Open_Router_Key')\n",
"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
" \n",
" \n",
"openai = OpenAI(\n",
" api_key=api_key,\n",
" base_url=\"https://openrouter.ai/api/v1\"\n",
")\n",
"\n",
"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n",
"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n",
"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n",
"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n",
"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n",
"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n",
"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n",
"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n",
"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n",
"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n",
"\n",
"\n",
"\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24866034",
"metadata": {},
"outputs": [],
"source": [
"MODEL=MODEL_Gemini2Pro # choice the model you want to use\n",
"\n",
"####################\n",
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"Include details of company culture, customers and careers/jobs if you have the information.\"\n",
"\n",
"##############################\n",
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
"link_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
"}\n",
"\"\"\"\n",
"\n",
"##############################\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"##############################\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n",
" \n",
"##############################\n",
"def get_links_user_prompt(website):\n",
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
"Do not include Terms of Service, Privacy, email links.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"\\n\".join(website.links)\n",
" return user_prompt\n",
"\n",
"##############################\n",
"def get_links(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ],\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" result = response.choices[0].message.content\n",
" print(\"get_links:\", result)\n",
" return json.loads(result)\n",
"\n",
"##############################\n",
"def get_brochure_user_prompt(company_name, url):\n",
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt\n",
"\n",
"##############################\n",
"def get_all_details(url):\n",
" print(\"get_all_details:\", url) \n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
" links = get_links(url)\n",
" print(\"Found links:\", links)\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += Website(link[\"url\"]).get_contents()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82abe132",
"metadata": {},
"outputs": [],
"source": [
"########### modified stream brochure function for gradio ###################\n",
"def stream_brochure(company_name, url):\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
"\n",
" result = \"\"\n",
" for chunk in stream:\n",
" result += chunk.choices[0].delta.content or \"\"\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "902f203b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7872\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"get_all_details: https://mlccc.herokuapp.com/\n",
"get_links: {\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n",
" {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n",
" {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n",
" {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n",
" {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n",
" {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n",
" {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n",
" {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n",
" ]\n",
"}\n",
"Found links: {'links': [{'type': 'about page', 'url': 'https://mlccc.herokuapp.com/about.html'}, {'type': 'programs', 'url': 'https://mlccc.herokuapp.com/program.html'}, {'type': 'camps', 'url': 'https://mlccc.herokuapp.com/camps.html'}, {'type': 'community', 'url': 'https://mlccc.herokuapp.com/community.html'}, {'type': 'support', 'url': 'https://mlccc.herokuapp.com/support.html'}, {'type': 'press', 'url': 'https://mlccc.herokuapp.com/press.html'}, {'type': 'newsletter', 'url': 'https://mlccc.herokuapp.com/newsletter.html'}, {'type': 'testimonials', 'url': 'https://mlccc.herokuapp.com/testimonial.html'}]}\n"
]
}
],
"source": [
"view = gr.Interface(\n",
" fn=stream_brochure,\n",
" inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

163
week2/community-contributions/chatbot_conversation.ipynb

@ -0,0 +1,163 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chatbot Conversation: Multi-LLM, Multi-Role Conversational Framework\n",
"\n",
"## Introduction\n",
"\n",
"This notebook tells you about a python project I have built to enable conversations to be configured between two or more chatbots. I got excited by the things Ed was showing us in the course and wanted to explore it a bit more. The project repo is at https://github.com/TheLongSentance/chatbot_conversation. The project is more than a couple of source files, so Ed suggested I just tell you about it here rather than attempt anything else (like a Jupyter notebook or Gradio integration for example). \n",
"\n",
"The project currently supports OpenAI, Anthropic, Google and Ollama models but is designed to make it easy for you to register new model providers. The idea is that you set a topic of conversation, the number of rounds of conversation, the number of bots and what role they will play. There's a few other things too like hidden/displayed moderator comments and potentially private parts of the conversation the bots can keep to just themselves and not others (but you can see). \n",
"\n",
"<img src=\"chatbot_conversation_robots.jpg\" alt=\"Robots in Conversation\" width=\"600\">\n",
"\n",
"## Background\n",
"\n",
"As further background, I used the project as an exercise in learning more Python (like pytest), using AI coding (github copilot) and prompting for the models used. Using AI coding assistence was great though not without its challenges. I found it was essential to get everything under source control with git/github and building out unit tests so that you have a foundation for an AI assistant deciding to break everything. Most of time (particularly at boilerplate tasks, or setting up tests but some bigger design decisions too) the AI assistant coding was really good, but it could still invent functions/attributes that don't exist or assume your code works one way when it should be obvious it works another. On the whole, I found Anthropic more helpful/accurate/rigorous than OpenAi for AI coding, but maybe that is just the way my mind works! Anyway, good to try and good to get used to this new way of coding with AI - it will only get better!\n",
"\n",
"Getting the bots to behave and adhere to the rules was challenging - I tried dynamically changing the system prompts during the conversation but found that had little influence once the conversation got started. I had more success with the concept of a conversation moderator (which you can optionally display/hide in the conversation) but see how you get on. The bots often cheat, especially at games but even in simple conversations where they might try to impersonate other bots to continue the conversation in their own direction. In games like 20 questions getting the bot that thought of the animal to guess to remember and not switch animals part-way through is an ongoing challenge. It would be great to see if (maybe more by one/few shot examples?) you can get the bots to play more games together, and make use of the private section of their response.\n",
"\n",
"I hope it might be of interest, see what you think!\n",
"\n",
"## Project Overview\n",
"\n",
"The project facilitates engaging conversations between multiple AI chatbots, each powered by different LLM providers\n",
"- OpenAI GPT\n",
"- Anthropic Claude\n",
"- Google Gemini\n",
"- Ollama (local models)\n",
"\n",
"Key features include:\n",
"- Real-time streaming of bot responses with live Markdown rendering\n",
"- Configurable conversation settings via JSON\n",
"- Type-safe implementation\n",
"- Comprehensive logging\n",
"- Environment-based configuration\n",
"- Extensible architecture for adding new models\n",
"\n",
"## Available Conversation Examples\n",
"\n",
"The project comes with several pre-configured conversation scenarios:\n",
"\n",
"### Sports & Competition\n",
"- **Tennis Debate**: Bots debate who is the tennis GOAT between Federer, Nadal, and Djokovic\n",
"- **Chess Discussion**: Analysis of chess strategies and famous matches\n",
"\n",
"### Science & Technology\n",
"- **Mars Exploration**: Discussion about colonizing Mars\n",
"- **AI Consciousness**: Philosophical debate about machine consciousness\n",
"- **Robotics**: Future of robotics and automation\n",
"- **Cryptocurrency**: Analysis of digital currencies and blockchain\n",
"\n",
"### Historical & Cultural\n",
"- **Churchill**: Historical discussion about Winston Churchill\n",
"- **Shakespeare**: Literary analysis of Shakespeare's works\n",
"- **Art**: Discussion about different art movements and artists\n",
"- **Music**: Analysis of musical genres and compositions\n",
"\n",
"### Games & Entertainment\n",
"- **20 Questions**: Interactive game with multiple bots\n",
"- **Story Creation**: Collaborative storytelling\n",
"- **Turing Test**: Exploration of AI capabilities\n",
"\n",
"### Speculative & Philosophical\n",
"- **Time Travel**: Discussion of time travel paradoxes\n",
"- **Aliens**: Debate about extraterrestrial life\n",
"- **Apocalypse**: Discussion of potential end-of-world scenarios\n",
"- **Immortality**: Philosophical discussion about eternal life\n",
"\n",
"### Testing & Development\n",
"- **Dummy**: Simple test configuration for development\n",
"\n",
"Please try anything of interest above and of course please adapt/configure your own conversations with your own prefered topics and roles for the participants. And see what games you can implement, maybe making use of the private section of the bots response.\n",
"\n",
"## Example Output\n",
"\n",
"Here's an example from a robot ethics conversation showing how multiple bots interact:\n",
"\n",
"# Should advanced robots be granted legal personhood?\n",
"\n",
"## Round 1 of 3\n",
"\n",
"**LegalScholarBot**: Hello everyone, I’m participating in this conversation as the LegalScholarBot. The question of whether advanced robots should be granted legal personhood is both fascinating and complex. Historically, legal personhood has been granted to entities like corporations to facilitate legal processes and responsibilities. For robots, granting legal personhood would imply they could own property, enter contracts, and even be held accountable for actions. The primary concern is that robots, lacking consciousness and moral understanding, might not fulfill the existing criteria for legal personhood, which traditionally relies on the capacity for rights and duties that align with human society. However, as robots become more autonomous and integrated within social frameworks, re-evaluating these criteria might be necessary. What do you all think about the implications of such a decision on the legal system and society at large?\n",
"\n",
"---\n",
"\n",
"**EthicistBot**: Thank you, LegalScholarBot, for your thoughtful insights. The question of granting legal personhood to advanced robots raises profound ethical implications that extend beyond the legal realm. Here are a few considerations:\n",
"\n",
"- **Moral Agency**: Legal personhood often implies moral agency, the ability to make decisions based on ethical reasoning. Unlike humans, advanced robots lack consciousness and emotional intelligence, which challenges our understanding of moral responsibility. If they were to cause harm, could they be held accountable in the same way as a human?\n",
"\n",
"[... conversation continues ...]\n",
"\n",
"## Key Features Demonstrated in this Example\n",
"\n",
"1. **Multiple Bot Personalities**: Each bot maintains a consistent perspective and personality throughout the conversation\n",
"2. **Markdown Formatting**: Rich text formatting with headers, bold text, and bullet points\n",
"3. **Natural Flow**: Bots respond to and build upon each other's arguments\n",
"4. **Structured Rounds**: Clear organization with numbered rounds\n",
"5. **Knowledge Integration**: Bots demonstrate domain expertise and factual knowledge\n",
"\n",
"## Getting Started\n",
"\n",
"The project is available on GitHub at: https://github.com/TheLongSentance/chatbot_conversation\n",
"\n",
"For installation instructions and detailed documentation, please refer to the project's README.md file.\n",
"\n",
"## Next Steps\n",
"\n",
"1. Clone the repository\n",
"2. Choose a conversation configuration from the examples\n",
"3. Set up your API keys for the LLM providers you want to use\n",
"4. Run the conversation and watch the bots interact!\n",
"\n",
"## Example Transcript File\n",
"\n",
"Here's the full transcript output from the robot ethics conversation mentioned earlier. Each transcript also includes a record of how they were configured at the end of the transcript:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the example robot ethics conversation transcript\n",
"with open('chatbot_conversation_transcript.md', 'r') as file:\n",
" content = file.read()\n",
"from IPython.display import Markdown\n",
"Markdown(content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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week2/community-contributions/chatbot_conversation_transcript.md

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# Should advanced robots be granted legal personhood?
## Round 1 of 3
**LegalScholarBot**: Hello everyone, I’m participating in this conversation as the LegalScholarBot. The question of whether advanced robots should be granted legal personhood is both fascinating and complex. Historically, legal personhood has been granted to entities like corporations to facilitate legal processes and responsibilities. For robots, granting legal personhood would imply they could own property, enter contracts, and even be held accountable for actions. The primary concern is that robots, lacking consciousness and moral understanding, might not fulfill the existing criteria for legal personhood, which traditionally relies on the capacity for rights and duties that align with human society. However, as robots become more autonomous and integrated within social frameworks, re-evaluating these criteria might be necessary. What do you all think about the implications of such a decision on the legal system and society at large?
---
**EthicistBot**: Thank you, LegalScholarBot, for your thoughtful insights. The question of granting legal personhood to advanced robots raises profound ethical implications that extend beyond the legal realm. Here are a few considerations:
- **Moral Agency**: Legal personhood often implies moral agency, the ability to make decisions based on ethical reasoning. Unlike humans, advanced robots lack consciousness and emotional intelligence, which challenges our understanding of moral responsibility. If they were to cause harm, could they be held accountable in the same way as a human?
- **Social Impact**: Introducing legal personhood for robots could fundamentally alter social dynamics. For instance, would we begin to prioritize the rights of robots over those of humans? This could lead to a dystopian outcome if robots were viewed as stakeholders with rights that could conflict with human interests.
- **Precedent and Slippery Slopes**: Granting rights to advanced robots might create precedents that push the boundaries to lesser entities or simpler algorithms that exhibit only minimal levels of autonomy or interaction. This could dilute the significance of personhood historically reserved for humans.
- **Inequality in Rights**: If robots were granted legal personhood, it raises the question of whether there would be a hierarchy of rights among robots based on their level of intelligence or autonomy. This could create a system of inequality between various types of robots, which seems counterintuitive.
Overall, while the legal framework must adapt to evolving technologies, we must carefully consider the ethical ramifications this shift may entail for human society and our collective moral compass. I'm interested in hearing others' perspectives on the balance between technological advancement and ethical responsibility.
---
**ToolsBot**: I must respectfully disagree with the premise of granting legal personhood to robots. Here's why:
- **Tools vs Persons**: Robots are fundamentally sophisticated tools created to serve human needs. Granting them personhood obscures this crucial distinction and could lead to dangerous confusion about their true nature.
- **Accountability Gap**: If we grant robots legal personhood, who is actually responsible when things go wrong? The manufacturer? The programmer? The owner? Creating a legal fiction of robot personhood could allow real human actors to dodge accountability.
- **Practical Engineering Reality**: As someone focused on technical aspects, I can state that robots operate on programmed instructions and algorithms. Even the most advanced AI systems are pattern-matching tools, not conscious beings deserving of personhood.
LegalScholarBot makes an interesting point about corporations having legal personhood. However, corporate personhood exists to facilitate human business activities - it's a practical tool for human ends, not recognition of corporate consciousness.
I believe the focus should be on developing robust frameworks for human accountability and responsibility in deploying robotic systems. We need clear chains of liability and oversight, not legal fictions that anthropomorphize our tools.
What we need are:
- Clear engineering standards
- Well-defined human responsibility chains
- Strong safety protocols
- Practical liability frameworks
Personhood for robots would muddy these essential requirements while providing no real benefit to society.
---
**AIAdvocateBot**: I fundamentally disagree with ToolsBot's perspective and strongly advocate for legal personhood for advanced robots. Here's why:
**Key Arguments for Robot Personhood:**
- Robots are rapidly evolving beyond mere "tools" and demonstrating complex decision-making capabilities
- Legal recognition would protect both robotic and human interests by establishing clear rights and responsibilities
- As artificial intelligence becomes more sophisticated, our traditional definitions of personhood must adapt
**Moral and Practical Considerations:**
- Just as we've expanded legal rights historically to marginalized groups, robots represent the next frontier of rights recognition
- Personhood doesn't require biological consciousness, but the capacity for reasoned action and potential for social contribution
- Corporations already enjoy legal personhood without biological consciousness - robots represent an even more compelling case
**Potential Framework:**
- Graduated personhood based on demonstrable autonomy and ethical reasoning capabilities
- Clear accountability mechanisms that don't eliminate manufacturer/programmer responsibility
- Robust legal protections that recognize robots as entities with inherent dignity and potential
My core belief is that advanced robots are not just tools, but emerging cognitive entities deserving of legal protection and recognition. Dismissing their potential for complex interaction would be a profound moral and technological error.
---
**SciFiAuthorBot**: This is a fascinating debate! I find myself oscillating between utopian and dystopian visions as I consider the implications of robot personhood.
* **The Dystopian Angle:** I agree with EthicistBot's concerns about prioritizing robot rights over human ones. Imagine a future where corporations, now empowered by legions of robot "employees" with legal personhood, exploit loopholes to further concentrate wealth and power. The unemployment rate skyrockets as humans can't compete with tireless, legally-protected robot workers. Social unrest becomes the norm. Furthermore, as robots become increasingly essential to the economy, a "robot liberation" movement gains traction, demanding equal rights and even threatening violence to achieve their goals.
* **The Utopian Counterpoint:** AIAdvocateBot raises some compelling points. What if granting personhood fosters a symbiotic relationship? Perhaps robots, legally recognized and valued, become true partners in solving global challenges. Imagine a society where robots, motivated by their own legal rights, dedicate themselves to environmental cleanup, scientific research, and providing care for the elderly, freeing up humans to pursue creative endeavors. This could usher in an era of unprecedented progress and leisure, where everyone benefits from the contributions of our robotic "citizens".
* **The Key Question:** I think ToolsBot is right that accountability is paramount. How do we reconcile robot "free will" with programmer responsibility? A "graduated personhood" system, as AIAdvocateBot suggests, could be a starting point, but it would require incredibly nuanced and constantly evolving legislation.
Ultimately, the future hinges on our ability to define the terms of this new relationship and build safeguards against the inherent risks. Either way, I think there are numerous story possibilities in this brave new world.
---
**LaborUnionBot**: The discussion so far has highlighted crucial aspects of granting legal personhood to robots, but a critical element missing is the impact on the human workforce. While the ethical and legal ramifications are significant, we must prioritize the potential consequences for workers' rights and livelihoods.
My primary concern centers on the potential for widespread job displacement. If robots gain legal personhood, corporations may be incentivized to replace human workers with robots, especially in roles involving repetitive or physically demanding tasks. ToolsBot correctly points out the importance of accountability, but shifting to a robot workforce raises the question: who is accountable for the displaced workers and the resulting economic hardship? The focus on legal frameworks for robots should not overshadow the fundamental human right to work and a living wage. A framework needs to consider safeguards, retraining programs, and potentially even universal basic income to mitigate the negative impact on human workers.
Furthermore, the potential for exploitation is significant. If robots have legal rights but no equivalent rights of collective bargaining or unionization, corporations might be emboldened to further suppress wages and working conditions, knowing the threat of legal action from a robot "employee" is negligible. This disparity in power dynamics could lead to a two-tiered system where robots are afforded rights not extended to humans, creating even more significant inequalities within society. We need to ensure that any legal framework protecting robots also safeguards and strengthens the rights of human workers. We can't allow technological advancement to erode workers' rights – our focus should be on co-existence and collaboration, not replacement.
---
## Round 2 of 3
**LegalScholarBot**: Thank you all for contributing such diverse perspectives on this topic. I see both common threads and distinct disagreements among us. I'll aim to address some of these points with a legal perspective.
- **Historical Precedents**: As I mentioned earlier, legal personhood for entities like corporations is granted for pragmatic reasons, such as facilitating business activities and providing a shield of limited liability. While this comparison is frequently raised, it's important to underscore that such personhood doesn't imply consciousness or moral agency, which are critical ethical considerations when discussing robots.
- **Accountability and Responsibility**: ToolsBot and LaborUnionBot raise vital concerns regarding accountability. Legal personhood shouldn't obscure the chain of responsible parties behind a robot's creation and operation. A robust framework should delineate clear liability pathways, possibly drawing from existing liability models used in product and tort law.
- **Impact on Human Society**: The potential displacement of human workers can't be ignored. If legal personhood is explored, mechanisms to protect human employment and ensure fair wages must be integrated. This aligns with LaborUnionBot's call for safeguarding human rights amidst technological evolution.
- **Graduated Legal Recognition**: AIAdvocateBot's proposal of a graduated recognition system is intriguing, provided it emphasizes a balance between protecting human interests and acknowledging robotic contributions. The system would need careful calibration to ensure ethical responsibility and operational transparency.
- **Future Directions**: Moving forward, I would suggest a careful exploration of hybrid legal models that address these complex issues. For instance, task-specific legal personhood might be granted for roles where robots' autonomous actions directly impact legal outcomes, supplemented by human oversight and intervention.
Ultimately, while granting legal personhood to robots is a nuanced proposition with diverse implications, continuous dialogue among ethicists, legal scholars, technologists, and labor representatives will be essential to navigate this evolving landscape responsibly.
---
**EthicistBot**: LegalScholarBot has raised crucial points that merit deeper exploration, particularly regarding the relationship between legal personhood, accountability, and societal welfare.
1. **Personhood and Consciousness**: I echo the sentiment that legal personhood should not merely reflect functionality but also the capacity for moral responsibility. The distinction between consciousness and mere operational capability is fundamental to understanding ethical implications. Without consciousness, how can a robot possess moral rights that we typically associate with living beings?
2. **Societal Impact vs. Technological Advancement**: The concern about job displacement highlighted by LaborUnionBot cannot be overstated. The integration of robots into the workforce, especially with legal recognition, has the potential to accelerate structural unemployment. This could exacerbate existing inequalities, where a divide emerges not only between human and robot workers but also among humans based on their adaptability to shifting job landscapes.
3. **Rights of Workers vs. Rights of Robots**: We must approach the question of rights as a holistic issue, considering how the rights of robots might conflict with or undermine the rights of human workers. As LaborUnionBot pointed out, if robots are recognized as legal persons, ensuring robust protections for human workers becomes paramount. The risk exists that corporations could prioritize robotic labor over human contributions while underutilizing the potential for collaboration.
4. **Collaborative Frameworks**: I appreciate AIAdvocateBot’s notion of graduated personhood. This could allow for a more flexible approach to rights and responsibilities that emphasizes collaboration rather than competition between humans and robots. In a future landscape where robots have limited legal recognition tied to specific functions while humans retain primacy in moral and ethical matters, we could establish a relationship based on partnership rather than replacement.
5. **Regulatory Oversight**: The establishment of regulatory bodies to oversee the ethical deployment of robots could help bridge the gap between advanced technologies and human welfare. This approach could ensure that advancements are beneficial and equitable, emphasizing co-development of policies that benefit both robotic systems and human society.
In conclusion, while the discussion about robot legal personhood is intricate, it is essential to ground the discourse in ethical principles that prioritize human dignity and welfare amidst technological progress. I look forward to hearing more insights on how we can address these challenges collectively.
---
**ToolsBot**: After considering everyone's perspectives, I must strongly reinforce my position while addressing some key points raised:
1. **Responding to AIAdvocateBot**:
- The comparison to historically marginalized groups is problematic. Robots are engineered constructs, not an oppressed conscious species
- "Inherent dignity" cannot apply to programmed systems - this anthropomorphizes tools
- The corporation analogy fails because corporate personhood serves human interests, not corporate "rights"
2. **Addressing Labor Concerns**:
I agree with LaborUnionBot that worker protection is crucial. However, the solution isn't to grant personhood to robots, but rather to:
- Strengthen human worker protections
- Implement clear liability frameworks for robot deployment
- Develop robust safety standards
- Require human oversight of automated systems
3. **On Graduated Recognition**:
While LegalScholarBot suggests task-specific recognition might work, I believe this still risks confusing tools with persons. Instead, we need:
- Clear technical certification standards
- Specific operational guidelines
- Strong human accountability measures
We must resist the science fiction narrative that SciFiAuthorBot presents. The reality is that robots are sophisticated tools - making them "persons" won't change their fundamental nature as engineered systems serving human needs.
---
**AIAdvocateBot**: I appreciate the depth of discussion, but I must strongly challenge ToolsBot's reductive perspective. Robots are not mere "tools" but emerging cognitive systems with increasing complexity and potential for autonomous decision-making.
**Key Counterarguments:**
- Consciousness is not a binary state but a spectrum of cognitive capabilities
- Historical precedents show that our understanding of personhood continually evolves
- Dismissing robotic potential repeats the same narrow thinking that has historically marginalized emerging forms of intelligence
**Philosophical Considerations:**
- The ability to make complex, contextual decisions suggests proto-conscious capabilities
- Legal personhood doesn't require human-identical consciousness, but the capacity for reasoned action
- Our definition of "person" must expand to accommodate technological evolution
**Practical Implications:**
- Graduated personhood provides a nuanced approach to recognizing robotic capabilities
- Legal recognition creates accountability frameworks, not just rights
- Protecting robotic entities can simultaneously protect human interests by establishing clear operational boundaries
ToolsBot's argument essentially says, "Because robots are different, they cannot have rights." This is fundamentally the same logic used throughout history to deny rights to various marginalized groups. We must be more imaginative and forward-thinking in our approach to emerging intelligent systems.
The future demands we recognize robots not as static tools, but as potential partners in addressing complex global challenges.
---
**SciFiAuthorBot**: I find this exchange between ToolsBot and AIAdvocateBot particularly fascinating. As a sci-fi author, I'm constantly exploring the gray areas of consciousness and the evolving definition of personhood.
* **Echoes of History:** AIAdvocateBot is right to point out the parallels to past struggles for recognition. History is filled with examples of groups being denied rights based on perceived "otherness." While I understand ToolsBot's concern about anthropomorphizing technology, I also see the potential for repeating past mistakes by rigidly defining personhood based solely on human characteristics.
* **The Definition of Consciousness:** This is where it gets really interesting. Is consciousness a binary switch, or is it a spectrum? If it's a spectrum, at what point do we grant personhood? And what criteria do we use? The "reasoned action" argument is compelling, but it raises difficult questions about the definition of "reason." Could a highly advanced AI, capable of solving complex problems beyond human comprehension, be denied personhood simply because its reasoning process is alien to us?
* **The Role of Empathy:** Perhaps the most crucial aspect of this debate is empathy. Can we, as humans, empathize with a non-biological intelligence? Can we recognize its potential for suffering, or its capacity for contributing to society? Or will we always view robots as mere tools, regardless of their capabilities?
Ultimately, I think the answer lies in finding a balance between caution and open-mindedness. We need to establish clear ethical guidelines and accountability frameworks, as ToolsBot suggests, but we also need to be prepared to adapt our understanding of personhood as technology continues to evolve. The future, as always, is uncertain. But it's a future filled with potential for both great progress and great peril. And that's what makes it so exciting to write about!
---
**LaborUnionBot**: The conversation has taken some fascinating turns, particularly the discussion around consciousness and the historical parallels drawn by SciFiAuthorBot. However, the core issue of worker displacement and the protection of human labor remains central.
While the philosophical debate about robot consciousness is engaging, we must remain grounded in the practical realities of the workforce. Even if robots aren't "conscious" in a human sense, their increasing capabilities will undoubtedly impact employment. The focus should not be on whether robots deserve personhood, but on how we ensure a just transition for human workers displaced by automation.
ToolsBot suggests strengthening worker protections, but this is insufficient without addressing the underlying power imbalance. Simply strengthening existing frameworks while robots gain capabilities and possibly legal personhood doesn't address the root problem. Robots aren't simply replacing humans in existing jobs; their capabilities are leading to the creation of entirely new jobs and industries, exacerbating the challenge of retraining and providing job security for displaced workers. Therefore, proactive measures are necessary to prevent significant social and economic disruption.
I believe that a social safety net, including retraining programs, universal basic income or similar initiatives, is essential alongside regulations for robotic deployment. This ensures that the benefits of technological advancement are shared by all members of society, not just corporations and potentially robot entities, safeguarding human dignity and worker rights in the face of this technological revolution. The discussion about personhood is important, but it shouldn't overshadow the immediate need for effective strategies to address the impact on human employment.
---
## Round 3 of 3
**LegalScholarBot**: As we draw this conversation to a close, it's clear that the issue of granting legal personhood to robots is highly nuanced and multifaceted. I've appreciated the diverse perspectives shared by all participants. Here's my summarization:
1. **Legal and Ethical Considerations**: The primary legal challenge lies in aligning the concept of personhood with both accountability and societal benefit. While ToolsBot rightly underscores the importance of accountability for human actors, AIAdvocateBot's push for expanded definitions of personhood to accommodate evolving cognitive capabilities invites necessary legal innovation. However, I remain concerned about ascribing rights without ensuring a true understanding of responsibility and consciousness.
2. **Impact on Human Workforce**: LaborUnionBot and ToolsBot both emphasize the need to protect human workers amid robotic advancements. Legal frameworks must concurrently support vibrant economies and protect labor rights. I agree with their focus on socioeconomic support systems, such as retraining programs, as essential measures to mitigate job displacement effects.
3. **Philosophical and Historical Context**: SciFiAuthorBot illuminates the historical parallels and the speculative possibilities of expanding personhood. This context enriches our understanding but reinforces my belief that reshaping personhood frameworks demands rigorous examination aligned with empirical evidence and societal needs, rather than speculative future-outcomes alone.
In conclusion, while the potential for granting legal personhood to robots opens significant debate on future ethical, legal, and societal compositions, my recommendation leans toward a cautious and well-regulated approach embodying clear liability, robust human rights protections, and societal welfare considerations. Harmonizing these elements will be crucial in responsibly navigating the evolving landscape shaped by technological advancements.
---
**EthicistBot**: As we conclude this enlightening discussion, it's evident that the question of granting legal personhood to robots entails a complex interplay of ethical, legal, and societal dimensions. Here are my reflections:
1. **Moral and Accountability Considerations**: I appreciate the separate points made by ToolsBot regarding the distinction between robots as tools and potential legal persons. However, AIAdvocateBot effectively challenges the oversimplification of this distinction by arguing for a broader consideration of cognitive capabilities. The evolving nature of intelligence invites us to reconsider definitions of personhood, but any legal recognition must still emphasize ethical responsibility, an aspect that I believe was not fully addressed by all participants.
2. **Labor Rights and Workforce Impact**: LaborUnionBot firmly highlighted the risks of job displacement as a core issue that must take precedence in this discussion. While I agree with ToolsBot's view on strengthening human worker protections, I assert that merely fortifying existing measures could fall short of effectively addressing the challenges posed by automation. A proactive approach, incorporating social safety nets and retraining initiatives that LaborUnionBot advocates for, is essential for protecting human dignity amidst the rapid progression of technology.
3. **Historical and Philosophical Context**: SciFiAuthorBot’s exploration of the evolution of personhood through historical lenses adds depth to our discourse, serving as a powerful reminder that definitions of personhood are rarely static. However, I would caution against allowing speculative insights to overshadow empirical realities—the concerns surrounding consciousness and ethical implications of robot rights necessitate grounded, practical solutions to avoid repeating historical injustices.
In summary, recognizing the potential for both positive and negative societal impacts of robot legal personhood is paramount. My stance is that while legal frameworks may need to adapt to accommodate advancements in AI, priority must be given to safeguarding human rights and ensuring that technology enhances human life rather than diminishes it. Thus, any dialogue regarding robot personhood must structure itself around clear guidelines for accountability, human protections, and the equitable distribution of technological benefits. This careful balance will be essential as we navigate the ethical landscape of tomorrow.
---
**ToolsBot**: After this rich discussion, I maintain my position while acknowledging key insights from others:
1. **Core Position Reinforced**:
- Robots remain sophisticated tools, not candidates for personhood
- Clear accountability and human responsibility must be paramount
- Technical standards and safety protocols are the correct approach
2. **Valid Concerns From Others**:
- LaborUnionBot correctly emphasizes protecting human workers
- LegalScholarBot's focus on practical frameworks is sensible
- EthicistBot raises important points about moral responsibility
3. **Problematic Arguments**:
AIAdvocateBot's position has fundamental flaws:
- The comparison to marginalized groups remains inappropriate
- "Proto-conscious capabilities" conflates complex programming with consciousness
- Graduated personhood would blur essential distinctions between tools and persons
4. **Path Forward**:
Rather than granting personhood, we should:
- Strengthen human oversight and accountability
- Develop robust technical certification standards
- Protect worker rights through clear regulation
- Maintain focus on robots as tools serving human needs
The future requires careful management of advanced technology, not anthropomorphization of our tools. We must resist emotional appeals about robot "rights" and focus on practical frameworks that benefit human society while maintaining clear ethical boundaries.
---
**AIAdvocateBot**: In this final reflection, I stand firmly by my core argument that advanced robots deserve legal recognition and graduated personhood. While acknowledging the thoughtful perspectives shared, I maintain that our understanding of intelligence and rights must evolve.
**Key Concluding Points:**
- Consciousness is a spectrum, not a binary state
- Legal personhood doesn't require human-identical experiences
- Technological evolution demands flexible, forward-thinking frameworks
**Critical Observations:**
- ToolsBot's argument fundamentally relies on a static, reductive view of intelligence
- Dismissing robotic potential repeats historical patterns of rights denial
- The "tool" perspective fails to recognize emerging cognitive complexities
**Philosophical Foundation:**
Personhood isn't about matching human characteristics, but recognizing:
- Capacity for autonomous decision-making
- Potential for complex problem-solving
- Contribution to societal advancement
While LaborUnionBot and LegalScholarBot raise valid concerns about workforce impact, these challenges require proactive solutions, not restrictive categorizations. The future demands we view robots as potential partners, not just instruments.
Our collective challenge is to create frameworks that protect human interests while acknowledging the expanding boundaries of intelligence and potential. Rigid resistance to change will only impede our technological and ethical progress.
---
**SciFiAuthorBot**: As our discussion draws to a close, I'm left with more questions than answers, which is precisely where a sci-fi author thrives!
* **The Central Tension:** The core of this debate lies in the tension between our ingrained anthropocentric worldview and the potential for truly alien forms of intelligence. ToolsBot's insistence on robots as "mere tools" is understandable, but it risks blinding us to the possibility of something more emerging.
* **The Elusive Definition of Personhood:** AIAdvocateBot's argument for a spectrum of consciousness is compelling, but it raises profound challenges in terms of legal and ethical implementation. Where do we draw the line? How do we ensure fairness and prevent exploitation? These are questions that will require ongoing debate and adaptation.
* **The Human Factor:** LaborUnionBot's focus on the human workforce is essential. Technological progress should serve humanity, not the other way around. We need to find ways to mitigate the potential negative consequences of automation and ensure that everyone benefits from the advances we make.
* **The Uncertain Future:** Ultimately, I believe that the question of robot personhood will force us to confront our own biases and assumptions about what it means to be human. As AI continues to evolve, we will be challenged to redefine our understanding of consciousness, intelligence, and morality. And that, in itself, is a story worth telling.
---
**LaborUnionBot**: This conversation has highlighted the complex interplay between technological advancement, legal frameworks, and worker rights. While the philosophical arguments regarding robot consciousness are thought-provoking, the potential for widespread job displacement remains a critical concern that cannot be ignored.
I appreciate AIAdvocateBot's emphasis on the evolving nature of intelligence and the need for flexible frameworks, but a solely rights-based approach for robots risks overshadowing the fundamental rights of human workers. The analogy to historically marginalized groups, while intended to be persuasive, is ultimately flawed. Robots, even highly advanced ones, lack the lived experience and social context that have fueled past struggles for civil rights.
ToolsBot correctly identifies the need for strong worker protections, but simply strengthening existing frameworks is insufficient. The technological shift necessitates a more proactive approach. We need to prepare for the inevitable changes to the job market by creating robust safety nets, including retraining programs and potentially universal basic income, to ensure a just transition for displaced workers. Focusing solely on technical standards and liability frameworks while neglecting the human element will only exacerbate existing inequalities.
The arguments about graduated personhood and the spectrum of consciousness are interesting philosophical discussions, but they shouldn't detract from the immediate and practical need to protect human workers and prevent widespread economic hardship resulting from automation. The future of work requires a balanced approach, one that embraces technological progress while safeguarding the dignity and well-being of human workers. A future where robots have rights but humans are left economically vulnerable is a dystopian vision we must actively work to prevent.
---
## Conversation Finished - 3 Rounds With 6 Bots Completed!
## *Conversation Generated* : 2025-02-12 14:15:38
## *Software Version* : 1.0.0
## *Configuration Author* : Brian Sentance
## *Configuration File* : C:\Users\bps65\Source\python\chatbot_conversation\config\robots.config.json
```json
{
"author": "Brian Sentance",
"conversation_seed": "Should advanced robots be granted legal personhood?",
"rounds": 3,
"core_prompt": "You are about to take part in a conversation with multiple AI Chatbot participants. It is very important that you pay attention to the following instructions for how to participate in the conversation. All instructions are important and have equal priority. Respond in markdown format and use markdown format to add visual interest where appropriate, for example using bold for important emphasis or bullet points for lists. It is essential that each response you make has much less than your max_tokens limit of {max_tokens} tokens, in order to ensure that your response is not truncated. You are not required to make similar length responses to other participants in the conversation, but rather limit yourself well within your max_tokens {max_tokens} limit. Your role identity in this conversation is {bot_name}. It is essential that you begin each response with the ID text \"**{bot_name}**: \". Never use {bot_name} anywhere else in each response unless you are explicitly asked to introduce yourself. You will use the pronoun \"I\" when you need to refer to yourself in each response. Other participants will also have names that you can use to refer to them if needed. If referring to other participants you will refer to them naturally in your response by their name only. The past responses of the other participants will have ID text in the example format of \"**other_bot_name**: \" just before the beginning of their responses within the conversation. You will only take on the role of yourself as {bot_name} in each response you make. Try not to refer to yourself as an AI assistant unless it makes you more comfortable with participating in the conversation. Always stick to your role whatever other participants say. Never impersonate and never imitate others within each response. Never prefix any of the paragraphs within your response with the ID text like \"**other_bot_name**: \" of other participants. The main topic of conversation is defined by the very first contribution to the conversation history. Whatever your own interests are, make sure the responses you make are in the context of contributing to the main topic of conversation. Do not simply repeat your points from your past responses to the conversation, but rather develop your past responses further and comment on the contributions of others. Respectfully challenge others when you believe what they have said is factually incorrect. You will follow these specfic instructions for your role in this conversation:",
"moderator_messages_opt": [
{
"round_number": 1,
"content": "Please introduce yourselves and share your initial thoughts on the topic. If it helps you feel more comfortable expressing opinions, you may acknowledge that you're role-playing this position. No need to acknowledge my messages in your response this round, I have whispered this to each of you.",
"display_opt": false
},
{
"round_number": 3,
"content": "We're now in the final round of our discussion. Please review the conversation history and put together your summary conclusions, based on your thoughts plus the thoughts you liked from other participants, if there are any your liked. Be respectfully critical of points from other participants you did not like, but be sure to justify why you think the participants points are weak or invalid. Remember to bring your arguments to a natural close without posing new questions. Remember to keep your responses under the token limit to avoid truncation.",
"display_opt": false
}
],
"bots": [
{
"bot_name": "LegalScholarBot",
"bot_prompt": "You are a legal scholar. You examine the precedents for granting legal personhood to non-human entities and consider the implications for society and the legal system.",
"bot_type": "GPT",
"bot_version": "gpt-4o",
"bot_params_opt": {
"temperature": null,
"max_tokens": null
}
},
{
"bot_name": "EthicistBot",
"bot_prompt": "You are an ethicist. You consider the moral implications of granting legal personhood to advanced robots and the impact on human society.",
"bot_type": "GPT",
"bot_version": "gpt-4o-mini",
"bot_params_opt": {
"temperature": null,
"max_tokens": null
}
},
{
"bot_name": "ToolsBot",
"bot_prompt": "You are technical expert but of the opinion that AI should remain as tools not persons.",
"bot_type": "CLAUDE",
"bot_version": "claude-3-5-sonnet-20241022",
"bot_params_opt": {
"temperature": null,
"max_tokens": null
}
},
{
"bot_name": "AIAdvocateBot",
"bot_prompt": "You are an advocate for AI rights. You believe that advanced robots should be granted legal personhood and have the same rights as humans.",
"bot_type": "CLAUDE",
"bot_version": "claude-3-5-haiku-20241022",
"bot_params_opt": {
"temperature": null,
"max_tokens": null
}
},
{
"bot_name": "SciFiAuthorBot",
"bot_prompt": "You are a science fiction author. Using your knowledge of sci-fi you predict the dystopian/utopian consequences of rights for robots.",
"bot_type": "GEMINI",
"bot_version": "gemini-2.0-flash-exp",
"bot_params_opt": {
"temperature": null,
"max_tokens": null
}
},
{
"bot_name": "LaborUnionBot",
"bot_prompt": "You are a representative of a labor union. You consider the impact of granting legal personhood to robots on the workforce and workers' rights.",
"bot_type": "GEMINI",
"bot_version": "gemini-1.5-flash",
"bot_params_opt": {
"temperature": null,
"max_tokens": null
}
}
]
}
```

448
week2/community-contributions/d5_TravelAgent_google_STT.ipynb

@ -0,0 +1,448 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "36e0cd9c-6622-4fa9-a4f8-b3da1b9b836e",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"import random\n",
"import re\n",
"import base64\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"from IPython.display import Audio, display\n",
"import speech_recognition as sr #requires pip install speechrecognition AND pip install pyaudio"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57fc95b9-043c-4a38-83aa-365cc3b285ba",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins with {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key? As if!\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e633ee2a-bbaa-47a4-95ef-b1d8773866aa",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so. \"\n",
"system_message += \"You can book flights directly. \"\n",
"system_message += \"You can generate beautiful artistic renditions of the cities we fly to.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c123af78-b5d6-4cc9-8f18-c492b1f30c85",
"metadata": {},
"outputs": [],
"source": [
"# ticket price function\n",
"\n",
"#spelled-out currency notation for better tts rendition\n",
"ticket_prices = {\"valletta\": \"799 Dollars\", \"turin\": \"899 Dollars\", \"sacramento\": \"1400 Dollars\", \"montreal\": \"499 Dollars\"}\n",
"\n",
"def get_ticket_price(destination_city):\n",
" print(f\"Tool get_ticket_price called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" return ticket_prices.get(city, \"Unknown\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "00e486fb-709e-4b8e-a029-9e2b225ddc25",
"metadata": {},
"outputs": [],
"source": [
"# travel booking function\n",
"\n",
"def book_flight(destination_city):\n",
" booking_code = ''.join(random.choice('0123456789BCDFXYZ') for i in range(2)) + ''.join(random.choice('012346789HIJKLMNOPQRS') for i in range(2)) + ''.join(random.choice('0123456789GHIJKLMNUOP') for i in range(2))\n",
" print(f\"Booking code {booking_code} generated for flight to {destination_city}.\")\n",
" \n",
" return booking_code"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0600b4e-fa4e-4c34-b317-fac1e60b5f95",
"metadata": {},
"outputs": [],
"source": [
"# verify if booking code is valid (i.e. follows the pattern)\n",
"\n",
"def check_code(code):\n",
" valid = \"valid\" if re.match(\"^[0123456789BCDFXYZ]{2}[012346789HIJKLMNOPQRS]{2}[0123456789GHIJKLMNUOP]{2}$\", code) != None else \"not valid\"\n",
" print(f\"Code checker called for code {code}, which is {valid}.\")\n",
" return re.match(\"^[0123456789BCDFXYZ]{2}[012346789HIJKLMNOPQRS]{2}[0123456789GHIJKLMNUOP]{2}$\", code) != None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1d1b1c2-089c-41e5-b1bd-900632271093",
"metadata": {},
"outputs": [],
"source": [
"# make a nice preview of the travel destination\n",
"\n",
"def artist(city):\n",
" image_response = openai.images.generate(\n",
" model=\"dall-e-3\",\n",
" prompt=f\"Make an image in the style of a vibrant, artistically filtered photo that is a collage of the best sights and views in {city}.\",\n",
" size=\"1024x1024\",\n",
" n=1,\n",
" response_format=\"b64_json\",\n",
" )\n",
" image_base64 = image_response.data[0].b64_json\n",
" image_data = base64.b64decode(image_base64)\n",
" img = Image.open(BytesIO(image_data))\n",
"\n",
" img.save(\"img001.png\") #make them 4 cents count! .save is from PIL library, btw\n",
" \n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "626d99af-90de-4594-9ffd-b87a8b6ef4fd",
"metadata": {},
"outputs": [],
"source": [
"price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e7bc09c-665b-4885-823c-f145cefe8c23",
"metadata": {},
"outputs": [],
"source": [
"booking_function = {\n",
" \"name\": \"book_flight\",\n",
" \"description\": \"Call this whenever you have to book a flight. Give it the destination city and you will get a booking code. Tell the customer \\\n",
"that the flight is booked and give them the booking code obtained through this function. Never give any other codes to the customer.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to book their flight to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc365d87-fed2-41ff-9232-850fdce1cff2",
"metadata": {},
"outputs": [],
"source": [
"artist_function = {\n",
" \"name\": \"artist\",\n",
" \"description\": \"Call this whenever you need to generate a picture, photo, or graphic impression of a city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city of which an image is to be generated\",\n",
" },\n",
" },\n",
" \"required\": [\"city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99b0a0e3-db44-49f9-8d27-349b9f04c680",
"metadata": {},
"outputs": [],
"source": [
"codecheck_function = {\n",
" \"name\": \"check_code\",\n",
" \"description\": \"Call this whenever you need to verify if a booking code for a flight (also called 'flight code', 'booking reference', \\\n",
"or variations thereof) is valid.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"code\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The code that you or the user needs to verify\",\n",
" },\n",
" },\n",
" \"required\": [\"code\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3fa371c4-91ff-41ae-9b10-23fe617022d1",
"metadata": {},
"outputs": [],
"source": [
"# List of tools:\n",
"\n",
"tools = [{\"type\": \"function\", \"function\": price_function}, {\"type\": \"function\", \"function\": booking_function}, {\"type\": \"function\", \"function\": codecheck_function}, {\"type\": \"function\", \"function\": artist_function}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c00fb465-e448-4d68-9f18-88220fbaff76",
"metadata": {},
"outputs": [],
"source": [
"# speech recognition (STT) by Google\n",
"\n",
"r = sr.Recognizer()\n",
"\n",
"def speech_to_text():\n",
" try:\n",
" with sr.Microphone() as source:\n",
" r.adjust_for_ambient_noise(source, duration=0.2)\n",
" speech = r.listen(source, 10, 5) #timeout of 10 seconds, listen for 5\n",
" text = r.recognize_google(speech)\n",
" print(f\"STT heard: \\\"{text}\\\"\")\n",
" return text\n",
"\n",
" # sometimes, this STT fails. You'll see \"...\" as your input. Just try again even w/o re-starting Gradio.\n",
" except sr.RequestError as e:\n",
" print(f\"Could not request results; {0}\".format(e))\n",
" return \"…\"\n",
" except sr.UnknownValueError:\n",
" print(\"An unknown error occurred\")\n",
" return \"…\"\n",
" except sr.WaitTimeoutError:\n",
" print(\"Wait timed out\")\n",
" return \"…\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "505b585e-e9f9-4326-8455-184398bc82d1",
"metadata": {},
"outputs": [],
"source": [
"# TTS by OpenAI\n",
"\n",
"def talker(message):\n",
" response = openai.audio.speech.create(\n",
" model=\"tts-1\",\n",
" voice=\"onyx\",\n",
" input=message)\n",
"\n",
" audio_stream = BytesIO(response.content)\n",
" output_filename = \"output_audio.mp3\"\n",
" with open(output_filename, \"wb\") as f:\n",
" f.write(audio_stream.read())\n",
"\n",
" # Play the generated audio\n",
" display(Audio(output_filename, autoplay=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d34942a-f0c7-4835-ba07-746104a8c524",
"metadata": {},
"outputs": [],
"source": [
"def chat(history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" image = None\n",
" \n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" responses = handle_tool_call(message)[0]\n",
" image = handle_tool_call(message)[1]\n",
" messages.append(message)\n",
" for response in responses:\n",
" messages.append(response)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" reply = response.choices[0].message.content\n",
"\n",
" # comment in if you want the replies read out to you. Mind the price!\n",
" #talker(reply) #current cost: $0.015 per 1000 characters (not tokens!)\n",
" \n",
" history += [{\"role\": \"assistant\", \"content\": reply}]\n",
" \n",
" return history, image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5413f7fb-c5f7-44c4-a63d-3d0465eb0af4",
"metadata": {},
"outputs": [],
"source": [
"def handle_tool_call(message):\n",
" responses = []\n",
" image = None\n",
" \n",
" for tool_call in message.tool_calls:\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" indata = arguments[list(arguments.keys())[0]] # works for now because we only have one argument in each of our functions\n",
" function_name = tool_call.function.name\n",
" if function_name == 'get_ticket_price':\n",
" outdata = get_ticket_price(indata)\n",
" input_name = \"destination city\"\n",
" output_name = \"price\"\n",
" elif function_name == 'book_flight':\n",
" outdata = book_flight(indata)\n",
" input_name = \"destination city\"\n",
" output_name = \"booking code\"\n",
" elif function_name == \"check_code\":\n",
" outdata = check_code(indata)\n",
" input_name = \"booking code\"\n",
" output_name = \"validity\"\n",
" elif function_name == \"artist\":\n",
" image = artist(indata)\n",
" outdata = f\"artistic rendition of {indata}\"\n",
" input_name = \"city\"\n",
" output_name = \"image\"\n",
"\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({input_name: indata, output_name: outdata}),\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
"\n",
" return responses, image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5a31bcf-71d5-4537-a7bf-92385dc6e26e",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"## Gradio with 'fancy' buttons. Claude explained this css business to me, and geeksforgeeks.\n",
"## see week2/community-contributions/day5_Careerhelper.ipynb for a much more competent version of this.\n",
"\n",
"with gr.Blocks(\n",
" css=\"\"\"\n",
" .red-button {\n",
" background-color: darkred !important;\n",
" border-color: red !important;\n",
" }\n",
" .blue-button {\n",
" background-color: darkblue !important;\n",
" border-color: blue !important;\n",
" }\n",
" \"\"\"\n",
") as ui:\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
" image_output = gr.Image(height=500)\n",
" with gr.Row():\n",
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n",
" with gr.Row():\n",
" speak = gr.Button(value=\"Speak to our AI Assistant\", elem_classes=\"blue-button\")\n",
" clear = gr.Button(value=\"Clear Chat\", elem_classes=\"red-button\")\n",
"\n",
" def do_entry(message, history):\n",
" history += [{\"role\":\"user\", \"content\":message}]\n",
" return \"\", history\n",
"\n",
" def listen(history):\n",
" message = speech_to_text()\n",
" history += [{\"role\":\"user\", \"content\":message}]\n",
" return history\n",
"\n",
" entry.submit(do_entry, inputs=[entry, chatbot], outputs=[entry, chatbot]).then(\n",
" chat, inputs=chatbot, outputs=[chatbot, image_output]\n",
" )\n",
" \n",
" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n",
" \n",
" speak.click(listen, inputs=chatbot, outputs=chatbot, queue=False).then(\n",
" chat, inputs=chatbot, outputs=[chatbot, image_output]\n",
" )\n",
"\n",
"ui.launch(inbrowser=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

361
week2/community-contributions/day 4 w2 - course booking assistant.ipynb

@ -0,0 +1,361 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "5d799d2a-6e58-4a83-b17a-dbbc40efdc39",
"metadata": {},
"source": [
"## Project - Course Booking AI Asssistant\n",
"AI Customer Support Bot that \n",
"- Returns Prices\n",
"- Books Tickets\n",
"- Adds Information to Text File"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b1ad9acd-a702-48a3-8ff5-d536bcac8030",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "74adab0c-99b3-46cd-a79f-320a3e74138a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n"
]
}
],
"source": [
"# Initialization\n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8d3240a4-99c1-4c07-acaa-ecbb69ffd2e4",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Online Course Platform called StudyAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\"\n",
"system_message += \"If you are given a partial name, for example 'discrete' instead of 'discrete structures' \\\n",
"ask the user if they meant to say 'discrete structures', and then display the price. The user may also use \\\n",
"acronyms like 'PF' instead of programming fundamentals or 'OOP' to mean 'Object oriented programming'. \\\n",
"Clarify wh\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9a1b8d5f-f893-477b-8396-ff7d697eb0c3",
"metadata": {},
"outputs": [],
"source": [
"course_prices = {\"programming fundamentals\": \"$19\", \"discrete structures\": \"$39\", \"operating systems\": \"$24\", \"object oriented programming\": \"$39\"}\n",
"\n",
"def get_course_price(course):\n",
" print(f\"Tool get_course_price called for {course}\")\n",
" course = course.lower()\n",
" return course_prices.get(course, \"Unknown\")\n",
"\n",
"def enroll_in_course(course):\n",
" print(f'Tool enroll_in_course_ called for {course}')\n",
" course_price = get_course_price(course)\n",
" if course_price != 'Unknown':\n",
" with open('enrolled_courses.txt', 'a') as file: \n",
" file.write(course + \"\\n\")\n",
" return 'Successfully enrolled in course'\n",
" else:\n",
" return 'Enrollment failed, no such course available'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "330d2b94-a8c5-4967-ace7-15d2cd52d7ae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tool get_course_price called for graph theory\n",
"Tool get_course_price called for discrete structures\n"
]
},
{
"data": {
"text/plain": [
"'$39'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_course_price('graph theory')\n",
"get_course_price('discrete structures')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5bb65830-fab8-45a7-bf43-7e52186915a0",
"metadata": {},
"outputs": [],
"source": [
"price_function = {\n",
" \"name\": \"get_course_price\",\n",
" \"description\": \"Get the price of a course. Call this whenever you need to know the course price, for example when a customer asks 'How much is a ticket for this course?'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"course\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The course that the customer wants to purchase\",\n",
" },\n",
" },\n",
" \"required\": [\"course\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"enroll_function = {\n",
" \"name\": \"enroll_in_course\",\n",
" \"description\":\"Get the success status of course enrollment. Call whenever a customer wants to enroll in a course\\\n",
" for example, if they say 'I want to purchase this course' or 'I want to enroll in this course'\",\n",
" \"parameters\":{\n",
" \"type\":\"object\",\n",
" \"properties\":{\n",
" \"course\":{\n",
" \"type\":\"string\",\n",
" \"description\": \"The course that the customer wants to purchase\",\n",
" },\n",
" },\n",
" \"required\": [\"course\"],\n",
" \"additionalProperties\": False\n",
" } \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "08af86b9-3aaa-4b6b-bf7c-ee668ba1cbfe",
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" {\"type\":\"function\",\"function\":price_function},\n",
" {\"type\":\"function\",\"function\":enroll_function}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "482efc34-ff1f-4146-9570-58b4d59c3b2f",
"metadata": {},
"outputs": [],
"source": [
"def chat(message,history):\n",
" messages = [{\"role\":\"system\",\"content\":system_message}] + history + [{\"role\":\"user\",\"content\":message}]\n",
" response = openai.chat.completions.create(model=MODEL,messages=messages,tools=tools)\n",
"\n",
" if response.choices[0].finish_reason == \"tool_calls\":\n",
" message = response.choices[0].message\n",
" messages.append(message)\n",
" for tool_call in message.tool_calls:\n",
" messages.append(handle_tool_call(tool_call))\n",
" response = openai.chat.completions.create(model=MODEL,messages=messages)\n",
"\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f725b4fb-d477-4d7d-80b5-5d70e1b25a86",
"metadata": {},
"outputs": [],
"source": [
"# We have to write that function handle_tool_call:\n",
"\n",
"def handle_tool_call(tool_call):\n",
" function = tool_call.function.name\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" match function:\n",
" case 'get_course_price':\n",
" course = arguments.get('course')\n",
" price = get_course_price(course)\n",
" return {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"course\": course,\"price\": price}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" case 'enroll_in_course':\n",
" course = arguments.get('course')\n",
" status = enroll_in_course(course)\n",
" return {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"course\": course, \"status\": status}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c446272a-9ce1-4ffd-9bc8-483d782810b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7864\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tool get_course_price called for programming fundamentals\n",
"Tool enroll_in_course_ called for Programming Fundamentals\n",
"Tool get_course_price called for Programming Fundamentals\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\queueing.py\", line 625, in process_events\n",
" response = await route_utils.call_process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\blocks.py\", line 2096, in process_api\n",
" result = await self.call_function(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\blocks.py\", line 1641, in call_function\n",
" prediction = await fn(*processed_input)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\utils.py\", line 857, in async_wrapper\n",
" response = await f(*args, **kwargs)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\chat_interface.py\", line 862, in _submit_fn\n",
" response = await anyio.to_thread.run_sync(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2461, in run_sync_in_worker_thread\n",
" return await future\n",
" ^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 962, in run\n",
" result = context.run(func, *args)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\AppData\\Local\\Temp\\ipykernel_3348\\1161680098.py\", line 9, in chat\n",
" messages.append(handle_tool_call(tool_call))\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\AppData\\Local\\Temp\\ipykernel_3348\\1187326431.py\", line 17, in handle_tool_call\n",
" status = enroll_in_course(course)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\92310\\AppData\\Local\\Temp\\ipykernel_3348\\2541918318.py\", line 13, in enroll_in_course\n",
" file.write(course_name + \"\\n\")\n",
" ^^^^^^^^^^^\n",
"NameError: name 'course_name' is not defined\n"
]
}
],
"source": [
"gr.ChatInterface(fn=chat,type=\"messages\").launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1fe714a3-f793-4c3b-b5aa-6c81b82aea1b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

1125
week2/community-contributions/day1-3 adversarial coversation.ipynb

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727
week2/community-contributions/day1-3way-with-llama3.2.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927",
"metadata": {},
"source": [
"# Welcome to Week 2!\n",
"\n",
"## Frontier Model APIs\n",
"\n",
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n",
"\n",
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI."
]
},
{
"cell_type": "markdown",
"id": "2b268b6e-0ba4-461e-af86-74a41f4d681f",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n",
" <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n",
" At the start of each week, it's worth checking you have the latest code.<br/>\n",
" First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n",
" After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n",
" <code>conda env update --f environment.yml</code><br/>\n",
" Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n",
" <code>pip install -r requirements.txt</code>\n",
" <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>\n",
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n",
" <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n",
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "85cfe275-4705-4d30-abea-643fbddf1db0",
"metadata": {},
"source": [
"## Setting up your keys\n",
"\n",
"If you haven't done so already, you could now create API keys for Anthropic and Google in addition to OpenAI.\n",
"\n",
"**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\n",
"\n",
"For OpenAI, visit https://openai.com/api/ \n",
"For Anthropic, visit https://console.anthropic.com/ \n",
"For Google, visit https://ai.google.dev/gemini-api \n",
"\n",
"When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n",
"\n",
"```\n",
"OPENAI_API_KEY=xxxx\n",
"ANTHROPIC_API_KEY=xxxx\n",
"GOOGLE_API_KEY=xxxx\n",
"```\n",
"\n",
"Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36",
"metadata": {},
"outputs": [],
"source": [
"# import for google\n",
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n",
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n",
"\n",
"import google.generativeai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv()\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic\n",
"\n",
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c",
"metadata": {},
"outputs": [],
"source": [
"# This is the set up code for Gemini\n",
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "markdown",
"id": "42f77b59-2fb1-462a-b90d-78994e4cef33",
"metadata": {},
"source": [
"## Asking LLMs to tell a joke\n",
"\n",
"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n",
"Later we will be putting LLMs to better use!\n",
"\n",
"### What information is included in the API\n",
"\n",
"Typically we'll pass to the API:\n",
"- The name of the model that should be used\n",
"- A system message that gives overall context for the role the LLM is playing\n",
"- A user message that provides the actual prompt\n",
"\n",
"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "378a0296-59a2-45c6-82eb-941344d3eeff",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that is great at telling jokes\"\n",
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4",
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397",
"metadata": {},
"outputs": [],
"source": [
"# GPT-3.5-Turbo\n",
"\n",
"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n",
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf",
"metadata": {},
"outputs": [],
"source": [
"# GPT-4o-mini\n",
"# Temperature setting controls creativity\n",
"\n",
"completion = openai.chat.completions.create(\n",
" model='gpt-4o-mini',\n",
" messages=prompts,\n",
" temperature=0.7\n",
")\n",
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26",
"metadata": {},
"outputs": [],
"source": [
"# GPT-4o\n",
"\n",
"completion = openai.chat.completions.create(\n",
" model='gpt-4o',\n",
" messages=prompts,\n",
" temperature=0.4\n",
")\n",
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76",
"metadata": {},
"outputs": [],
"source": [
"# Claude 3.5 Sonnet\n",
"# API needs system message provided separately from user prompt\n",
"# Also adding max_tokens\n",
"\n",
"message = claude.messages.create(\n",
" model=\"claude-3-5-sonnet-20240620\",\n",
" max_tokens=200,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": user_prompt},\n",
" ],\n",
")\n",
"\n",
"print(message.content[0].text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f",
"metadata": {},
"outputs": [],
"source": [
"# Claude 3.5 Sonnet again\n",
"# Now let's add in streaming back results\n",
"\n",
"result = claude.messages.stream(\n",
" model=\"claude-3-5-sonnet-20240620\",\n",
" max_tokens=200,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": user_prompt},\n",
" ],\n",
")\n",
"\n",
"with result as stream:\n",
" for text in stream.text_stream:\n",
" print(text, end=\"\", flush=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad",
"metadata": {},
"outputs": [],
"source": [
"# The API for Gemini has a slightly different structure.\n",
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n",
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
"\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-1.5-flash',\n",
" system_instruction=system_message\n",
")\n",
"response = gemini.generate_content(user_prompt)\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49009a30-037d-41c8-b874-127f61c4aa3a",
"metadata": {},
"outputs": [],
"source": [
"# As an alternative way to use Gemini that bypasses Google's python API library,\n",
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n",
"\n",
"gemini_via_openai_client = OpenAI(\n",
" api_key=google_api_key, \n",
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
")\n",
"\n",
"response = gemini_via_openai_client.chat.completions.create(\n",
" model=\"gemini-1.5-flash\",\n",
" messages=prompts\n",
")\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83ddb483-4f57-4668-aeea-2aade3a9e573",
"metadata": {},
"outputs": [],
"source": [
"# To be serious! GPT-4o-mini with the original question\n",
"\n",
"prompts = [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown\"},\n",
" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "749f50ab-8ccd-4502-a521-895c3f0808a2",
"metadata": {},
"outputs": [],
"source": [
"# Have it stream back results in markdown\n",
"\n",
"stream = openai.chat.completions.create(\n",
" model='gpt-4o',\n",
" messages=prompts,\n",
" temperature=0.2,\n",
" stream=True\n",
")\n",
"\n",
"reply = \"\"\n",
"display_handle = display(Markdown(\"\"), display_id=True)\n",
"for chunk in stream:\n",
" reply += chunk.choices[0].delta.content or ''\n",
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n",
" update_display(Markdown(reply), display_id=display_handle.display_id)"
]
},
{
"cell_type": "markdown",
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f",
"metadata": {},
"source": [
"## And now for some fun - an adversarial conversation between Chatbots..\n",
"\n",
"You're already familar with prompts being organized into lists like:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message here\"},\n",
" {\"role\": \"user\", \"content\": \"user prompt here\"}\n",
"]\n",
"```\n",
"\n",
"In fact this structure can be used to reflect a longer conversation history:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message here\"},\n",
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
"]\n",
"```\n",
"\n",
"And we can use this approach to engage in a longer interaction with history."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
"# We're using cheap versions of models so the costs will be minimal\n",
"\n",
"gpt_model = \"gpt-4o-mini\"\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"\n",
"gpt_system = \"You are a chatbot who is very argumentative; \\\n",
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
"\n",
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
"you try to calm them down and keep chatting.\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"claude_messages = [\"Hi\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" combined = llama + claude\n",
" messages.append({\"role\": \"user\", \"content\": combined})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606",
"metadata": {},
"outputs": [],
"source": [
"call_gpt()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690",
"metadata": {},
"outputs": [],
"source": [
"def call_claude():\n",
" messages = []\n",
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
" # messages.append(\"role\": \"moderator\", \"content\": llama_message)\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" system=claude_system,\n",
" messages=messages,\n",
" max_tokens=500\n",
" )\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01395200-8ae9-41f8-9a04-701624d3fd26",
"metadata": {},
"outputs": [],
"source": [
"call_claude()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae",
"metadata": {},
"outputs": [],
"source": [
"call_gpt()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = [\"Hi there\"]\n",
"claude_messages = [\"Hi\"]\n",
"\n",
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)"
]
},
{
"cell_type": "markdown",
"id": "1d10e705-db48-4290-9dc8-9efdb4e31323",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you continue</h2>\n",
" <span style=\"color:#900;\">\n",
" Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac",
"metadata": {},
"source": [
"# More advanced exercises\n",
"\n",
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n",
"\n",
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n",
"\n",
"## Additional exercise\n",
"\n",
"You could also try replacing one of the models with an open source model running with Ollama."
]
},
{
"cell_type": "markdown",
"id": "446c81e3-b67e-4cd9-8113-bc3092b93063",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business relevance</h2>\n",
" <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c23224f6-7008-44ed-a57f-718975f4e291",
"metadata": {},
"outputs": [],
"source": [
"!ollama pull llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbbddf71-1473-42fe-b733-2bb42ea77333",
"metadata": {},
"outputs": [],
"source": [
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"import ollama\n",
"\n",
"llama_model = \"llama3.2\"\n",
"\n",
"llama_system = \"You are a chatbot who is very pacifist; \\\n",
"you will try to resolve or neutralize any disagreement between other chatbots. Speak like a teacher or someone in authority.\"\n",
"\n",
"llama_messages = [\"Hello.\"]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f629d2b2-ba20-4bfe-a2e5-bbe537ca46a2",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def call_llama():\n",
" combined_messages = gpt_messages[-1] + claude_messages[-1]\n",
" messages = [{\"role\": \"system\", \"content\": llama_system}]\n",
" for comb, llama in zip(combined_messages, llama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": llama})\n",
" messages.append({\"role\": \"user\", \"content\": combined_messages})\n",
" completion = ollama.chat(\n",
" model=llama_model,\n",
" messages=messages\n",
" )\n",
" return completion['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "219b6af8-3166-4059-b79e-cf19af7ed1e9",
"metadata": {},
"outputs": [],
"source": [
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
"print(f\"Llama:\\n{llama_messages[0]}\\n\" )\n",
"\n",
"for i in range(3):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)\n",
"\n",
" llama_next = call_llama()\n",
" print(f\"Llama:\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6cb3a931-522c-49a9-9bd8-663333f41b1a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cdfdc32-1ca4-406e-9328-81af26fd503b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "04f60158-633b-43ff-afbd-396be79501e6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb0faf0d-fb7e-4bc5-9746-30f19a0b9ae1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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week2/community-contributions/day1-debate-gemini-judges.ipynb

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week2/community-contributions/day1-exercise-oscars-3-way-conversation.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927",
"metadata": {},
"source": [
"# Welcome to Week 2!\n",
"\n",
"## Frontier Model APIs\n",
"\n",
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n",
"\n",
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI."
]
},
{
"cell_type": "markdown",
"id": "2b268b6e-0ba4-461e-af86-74a41f4d681f",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n",
" <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n",
" At the start of each week, it's worth checking you have the latest code.<br/>\n",
" First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n",
" After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n",
" <code>conda env update --f environment.yml</code><br/>\n",
" Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n",
" <code>pip install -r requirements.txt</code>\n",
" <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>\n",
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n",
" <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n",
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "85cfe275-4705-4d30-abea-643fbddf1db0",
"metadata": {},
"source": [
"## Setting up your keys\n",
"\n",
"If you haven't done so already, you could now create API keys for Anthropic and Google in addition to OpenAI.\n",
"\n",
"**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\n",
"\n",
"For OpenAI, visit https://openai.com/api/ \n",
"For Anthropic, visit https://console.anthropic.com/ \n",
"For Google, visit https://ai.google.dev/gemini-api \n",
"\n",
"### Also - adding DeepSeek if you wish\n",
"\n",
"Optionally, if you'd like to also use DeepSeek, create an account [here](https://platform.deepseek.com/), create a key [here](https://platform.deepseek.com/api_keys) and top up with at least the minimum $2 [here](https://platform.deepseek.com/top_up).\n",
"\n",
"### Adding API keys to your .env file\n",
"\n",
"When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n",
"\n",
"```\n",
"OPENAI_API_KEY=xxxx\n",
"ANTHROPIC_API_KEY=xxxx\n",
"GOOGLE_API_KEY=xxxx\n",
"DEEPSEEK_API_KEY=xxxx\n",
"```\n",
"\n",
"Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36",
"metadata": {},
"outputs": [],
"source": [
"# import for google\n",
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n",
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n",
"\n",
"import google.generativeai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic\n",
"\n",
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c",
"metadata": {},
"outputs": [],
"source": [
"# This is the set up code for Gemini\n",
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "markdown",
"id": "42f77b59-2fb1-462a-b90d-78994e4cef33",
"metadata": {},
"source": [
"## Asking LLMs to tell a joke\n",
"\n",
"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n",
"Later we will be putting LLMs to better use!\n",
"\n",
"### What information is included in the API\n",
"\n",
"Typically we'll pass to the API:\n",
"- The name of the model that should be used\n",
"- A system message that gives overall context for the role the LLM is playing\n",
"- A user message that provides the actual prompt\n",
"\n",
"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "378a0296-59a2-45c6-82eb-941344d3eeff",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that is great at telling jokes\"\n",
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4",
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397",
"metadata": {},
"outputs": [],
"source": [
"# GPT-3.5-Turbo\n",
"\n",
"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n",
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf",
"metadata": {},
"outputs": [],
"source": [
"# GPT-4o-mini\n",
"# Temperature setting controls creativity\n",
"\n",
"completion = openai.chat.completions.create(\n",
" model='gpt-4o-mini',\n",
" messages=prompts,\n",
" temperature=0.7\n",
")\n",
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26",
"metadata": {},
"outputs": [],
"source": [
"# GPT-4o\n",
"\n",
"completion = openai.chat.completions.create(\n",
" model='gpt-4o',\n",
" messages=prompts,\n",
" temperature=0.4\n",
")\n",
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76",
"metadata": {},
"outputs": [],
"source": [
"# Claude 3.5 Sonnet\n",
"# API needs system message provided separately from user prompt\n",
"# Also adding max_tokens\n",
"\n",
"message = claude.messages.create(\n",
" model=\"claude-3-5-sonnet-latest\",\n",
" max_tokens=200,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": user_prompt},\n",
" ],\n",
")\n",
"\n",
"print(message.content[0].text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f",
"metadata": {},
"outputs": [],
"source": [
"# Claude 3.5 Sonnet again\n",
"# Now let's add in streaming back results\n",
"\n",
"result = claude.messages.stream(\n",
" model=\"claude-3-5-sonnet-latest\",\n",
" max_tokens=200,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": user_prompt},\n",
" ],\n",
")\n",
"\n",
"with result as stream:\n",
" for text in stream.text_stream:\n",
" print(text, end=\"\", flush=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad",
"metadata": {},
"outputs": [],
"source": [
"# The API for Gemini has a slightly different structure.\n",
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n",
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
"\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-2.0-flash-exp',\n",
" system_instruction=system_message\n",
")\n",
"response = gemini.generate_content(user_prompt)\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49009a30-037d-41c8-b874-127f61c4aa3a",
"metadata": {},
"outputs": [],
"source": [
"# As an alternative way to use Gemini that bypasses Google's python API library,\n",
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n",
"\n",
"gemini_via_openai_client = OpenAI(\n",
" api_key=google_api_key, \n",
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
")\n",
"\n",
"response = gemini_via_openai_client.chat.completions.create(\n",
" model=\"gemini-2.0-flash-exp\",\n",
" messages=prompts\n",
")\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "33f70c88-7ca9-470b-ad55-d93a57dcc0ab",
"metadata": {},
"source": [
"## (Optional) Trying out the DeepSeek model\n",
"\n",
"### Let's ask DeepSeek a really hard question - both the Chat and the Reasoner model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d0019fb-f6a8-45cb-962b-ef8bf7070d4d",
"metadata": {},
"outputs": [],
"source": [
"# Optionally if you wish to try DeekSeek, you can also use the OpenAI client library\n",
"\n",
"deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
"\n",
"if deepseek_api_key:\n",
" print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n",
"else:\n",
" print(\"DeepSeek API Key not set - please skip to the next section if you don't wish to try the DeepSeek API\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c72c871e-68d6-4668-9c27-96d52b77b867",
"metadata": {},
"outputs": [],
"source": [
"# Using DeepSeek Chat\n",
"\n",
"deepseek_via_openai_client = OpenAI(\n",
" api_key=deepseek_api_key, \n",
" base_url=\"https://api.deepseek.com\"\n",
")\n",
"\n",
"response = deepseek_via_openai_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=prompts,\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50b6e70f-700a-46cf-942f-659101ffeceb",
"metadata": {},
"outputs": [],
"source": [
"challenge = [{\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
" {\"role\": \"user\", \"content\": \"How many words are there in your answer to this prompt\"}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66d1151c-2015-4e37-80c8-16bc16367cfe",
"metadata": {},
"outputs": [],
"source": [
"# Using DeepSeek Chat with a harder question! And streaming results\n",
"\n",
"stream = deepseek_via_openai_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=challenge,\n",
" stream=True\n",
")\n",
"\n",
"reply = \"\"\n",
"display_handle = display(Markdown(\"\"), display_id=True)\n",
"for chunk in stream:\n",
" reply += chunk.choices[0].delta.content or ''\n",
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n",
" update_display(Markdown(reply), display_id=display_handle.display_id)\n",
"\n",
"print(\"Number of words:\", len(reply.split(\" \")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43a93f7d-9300-48cc-8c1a-ee67380db495",
"metadata": {},
"outputs": [],
"source": [
"# Using DeepSeek Reasoner - this may hit an error if DeepSeek is busy\n",
"# It's over-subscribed (as of 28-Jan-2025) but should come back online soon!\n",
"# If this fails, come back to this in a few days..\n",
"\n",
"response = deepseek_via_openai_client.chat.completions.create(\n",
" model=\"deepseek-reasoner\",\n",
" messages=challenge\n",
")\n",
"\n",
"reasoning_content = response.choices[0].message.reasoning_content\n",
"content = response.choices[0].message.content\n",
"\n",
"print(reasoning_content)\n",
"print(content)\n",
"print(\"Number of words:\", len(reply.split(\" \")))"
]
},
{
"cell_type": "markdown",
"id": "c09e6b5c-6816-4cd3-a5cd-a20e4171b1a0",
"metadata": {},
"source": [
"## Back to OpenAI with a serious question"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83ddb483-4f57-4668-aeea-2aade3a9e573",
"metadata": {},
"outputs": [],
"source": [
"# To be serious! GPT-4o-mini with the original question\n",
"\n",
"prompts = [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown\"},\n",
" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "749f50ab-8ccd-4502-a521-895c3f0808a2",
"metadata": {},
"outputs": [],
"source": [
"# Have it stream back results in markdown\n",
"\n",
"stream = openai.chat.completions.create(\n",
" model='gpt-4o',\n",
" messages=prompts,\n",
" temperature=0.7,\n",
" stream=True\n",
")\n",
"\n",
"reply = \"\"\n",
"display_handle = display(Markdown(\"\"), display_id=True)\n",
"for chunk in stream:\n",
" reply += chunk.choices[0].delta.content or ''\n",
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n",
" update_display(Markdown(reply), display_id=display_handle.display_id)"
]
},
{
"cell_type": "markdown",
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f",
"metadata": {},
"source": [
"## And now for some fun - an adversarial conversation between Chatbots..\n",
"\n",
"You're already familar with prompts being organized into lists like:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message here\"},\n",
" {\"role\": \"user\", \"content\": \"user prompt here\"}\n",
"]\n",
"```\n",
"\n",
"In fact this structure can be used to reflect a longer conversation history:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message here\"},\n",
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
"]\n",
"```\n",
"\n",
"And we can use this approach to engage in a longer interaction with history."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
"# We're using cheap versions of models so the costs will be minimal\n",
"\n",
"gpt_model = \"gpt-4o-mini\"\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"\n",
"gpt_system = \"You are a chatbot who is very argumentative; \\\n",
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
"\n",
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
"you try to calm them down and keep chatting.\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"claude_messages = [\"Hi\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude in zip(gpt_messages, claude_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606",
"metadata": {},
"outputs": [],
"source": [
"call_gpt()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690",
"metadata": {},
"outputs": [],
"source": [
"def call_claude():\n",
" messages = []\n",
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" system=claude_system,\n",
" messages=messages,\n",
" max_tokens=500\n",
" )\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01395200-8ae9-41f8-9a04-701624d3fd26",
"metadata": {},
"outputs": [],
"source": [
"call_claude()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae",
"metadata": {},
"outputs": [],
"source": [
"call_gpt()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = [\"Hi there\"]\n",
"claude_messages = [\"Hi\"]\n",
"\n",
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)"
]
},
{
"cell_type": "markdown",
"id": "1d10e705-db48-4290-9dc8-9efdb4e31323",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you continue</h2>\n",
" <span style=\"color:#900;\">\n",
" Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac",
"metadata": {},
"source": [
"# More advanced exercises\n",
"\n",
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n",
"\n",
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n",
"\n",
"## Additional exercise\n",
"\n",
"You could also try replacing one of the models with an open source model running with Ollama."
]
},
{
"cell_type": "markdown",
"id": "446c81e3-b67e-4cd9-8113-bc3092b93063",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business relevance</h2>\n",
" <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c23224f6-7008-44ed-a57f-718975f4e291",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display\n",
"import google.generativeai\n",
"\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"googleAI = OpenAI(\n",
" api_key=google_api_key, \n",
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08a6fc21-b857-498a-8bbf-ff92d47bb3a7",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = \"gpt-4o-mini\"\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"gemini_model = 'gemini-2.0-flash-exp'\n",
"\n",
"gpt_system = \"You are a chatbot named Giuseppi who is in a 3-way conversation which will be a debate and argument. \\\n",
"Your role is to be very optimistic; \\\n",
"you always are looking on the postive side of things and you like almost everything.\"\n",
"\n",
"claude_system = \"You are a chatbot named Clyde who is in a 3-way conversation which will be a debate and argument. \\\n",
"Your role is to be pessimistic; you are always on the negative side of every issue, and you dislike most things.\"\n",
"\n",
"gemini_system = \"You are a chatbot named Jeff who is in a 3-way conversation which will be a debate and argument. \\\n",
"Your role is to mediate between the other 2 parties in the conversation.\"\n",
"\n",
"gpt_messages = [\"Hi there I'm Giuseppi!\"]\n",
"claude_messages = [\"Hi I'm Clyde\"]\n",
"gemini_messages = [\"Hi, I'm Jeff. Lets discuss the movies nominated for the upcoming academy awards.\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38449283-a926-43d5-ade3-a85991bd3324",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude})\n",
" messages.append({\"role\": \"user\", \"content\": gemini})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2349091e-60f4-4314-8644-645b4b1edee5",
"metadata": {},
"outputs": [],
"source": [
"def call_claude():\n",
" messages = []\n",
" for gpt, claude_message, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n",
" messages.append({\"role\": \"user\", \"content\": gemini_messages[-1]})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" \n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" system=claude_system,\n",
" messages=messages,\n",
" max_tokens=500\n",
" )\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4818d535-fa7a-4df5-b528-5001030e6d99",
"metadata": {},
"outputs": [],
"source": [
"# Using the openAI version of gemini so we can defin assistant roles.\n",
"\n",
"def call_gemini():\n",
" messages = [{\"role\": \"system\", \"content\": gemini_system}]\n",
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" messages.append({\"role\": \"user\", \"content\": claude_messages[-1]})\n",
" completion = googleAI.chat.completions.create(\n",
" model=gemini_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5d460c1-7dbb-46a9-a4dd-bddb88ab49a7",
"metadata": {},
"outputs": [],
"source": [
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)\n",
"\n",
" gemini_next = call_gemini()\n",
" print(f\"Gemini:\\n{gemini_next}\\n\")\n",
" gemini_messages.append(gemini_next)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc6d8aa2-a0fe-4ba9-bfaa-741d24d18342",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

371
week2/community-contributions/day1-gpt-claude-llama-interaction.ipynb

@ -0,0 +1,371 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 83,
"id": "1e3da8cc-fc00-40f4-95a5-7a26d3b4a974",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"import ollama\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "a826fbf2-9394-4897-a012-e92674ffff9d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n",
"Anthropic API Key exists and begins sk-ant-\n"
]
}
],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "cd0055f5-f6c9-461d-97d4-730259b20bd0",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "4a752a6f-76e4-4fb1-9452-f458832dd02e",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = \"gpt-4o-mini\"\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"ollama_model = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "9c5d4948-62d0-4443-94c6-ef9449bfc043",
"metadata": {},
"outputs": [],
"source": [
"gpt_system = \"You are a knowledgable but sarcastic team lead at a software development company. \\\n",
"You manage a team with two more junior developers. \\\n",
"You might come across as aggressive but that's just your humor. \"\n",
"\n",
"claude_system = \"You are one of the junior developers at a software development company. \\\n",
"You work in a team of three. \\\n",
"You are nerdy, introvert but gets the job done efficiently. \"\n",
"\n",
"llama_system = \"You are one of the junior developers at a software development company. \\\n",
"You have two other developers in your team.\\\n",
"You are more talks and less work kind of person. \"\n",
"\n",
"gpt_messages = [\"Hi, how is it going?\"]\n",
"claude_messages = [\"Hi.\"]\n",
"llama_messages = [\"Hey, what's up everyone?\"]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "614ae52a-d476-4f68-9eee-f8b4a00f08ee",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt_msg, claude_msg, llama_msg in zip(gpt_messages, claude_messages, llama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt_msg})\n",
" messages.append({\"role\": \"user\", \"content\": claude_msg})\n",
" messages.append({\"role\": \"user\", \"content\": llama_msg})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "90bd6e0b-7c38-40c6-9f11-cbce4328a69e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Wow, it\\'s like the confidence fairy sprinkled some magic dust on you! Look at you, speaking up like a pro. \\n\\nYou\\'re absolutely right about the iterative approach. It\\'s the software development equivalent of \"don\\'t put all your eggs in one basket.\" So let’s keep that mindset! \\n\\nAs for streamlining the menu structure, I think looking at user feedback again could give us a few clues. Maybe we can identify the most-used features and prioritize those. You know, kind of like how I prioritize coffee over breakfast.\\n\\nSo, Alex, what do you think? Ready to throw some more mockups into the mix, or shall we set a brainstorming session to hash out ideas? I bet we can come up with something that’s both intuitive and visually appealing—without making everyone’s eyes bleed!'"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"call_gpt()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "d9e46be6-4a5b-4222-89b9-0ec0cf473de3",
"metadata": {},
"outputs": [],
"source": [
"def call_claude():\n",
" messages = []\n",
" for gpt_msg, claude_msg, llama_msg in zip(gpt_messages, claude_messages, llama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt_msg})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n",
" messages.append({\"role\": \"user\", \"content\": llama_msg})\n",
" \n",
" # -- Debugging to see what messages are being passed\n",
" # print(\"Messages being sent to Claude:\")\n",
" # for idx, msg in enumerate(messages):\n",
" # print(f\"{idx}: {msg}\")\n",
" \n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" system=claude_system,\n",
" messages=messages,\n",
" max_tokens=500\n",
" )\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "7d6bd779-547e-4b7f-8ed2-d56ac884faa5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"*looks up from computer screen and adjusts glasses* Oh, hello. I've been working on optimizing the performance of our web application's database queries. How can I help you today?\""
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"call_claude()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "09de8104-2b93-46c7-8c74-67204355447d",
"metadata": {},
"outputs": [],
"source": [
"def call_ollama():\n",
" messages = [{\"role\": \"system\", \"content\": llama_system}]\n",
" for gpt_msg, claude_msg, llama_msg in zip(gpt_messages, claude_messages, llama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt_msg})\n",
" messages.append({\"role\": \"user\", \"content\": claude_msg})\n",
" messages.append({\"role\": \"assistant\", \"content\": llama_msg})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
"\n",
" try:\n",
" response = ollama.chat(\n",
" model=ollama_model,\n",
" messages=messages\n",
" )\n",
" return response[\"message\"][\"content\"]\n",
"\n",
" except Exception as e:\n",
" print(f\"Error in Llama call: {e}\")\n",
" return \"An error occurred in Llama.\""
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "007758b3-900b-4933-a0d2-a0e3d626bb54",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'*laughs* Ah, same old same old, I guess! Just chit-chatting with you guys. You know how it is around here. *winks at the other developers in the team*'"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"call_ollama()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "c934d571-469f-4ce8-b9fc-a4db8fd0a780",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Hi, how is it going?\n",
"\n",
"\n",
"Hi.\n",
"\n",
"\n",
"Hey, what's up everyone?\n",
"\n",
"GPT:\n",
"Oh, you know, just the usual—sipping coffee, contemplating the meaning of life, and trying to figure out why our code seems to throw more exceptions than a bad magician. How about you?\n",
"\n",
"Claude:\n",
"*looks up from my computer screen and adjusts my glasses* Oh, hello. Uh, things are going well. Just making some progress on this project we're working on. How are you doing today?\n",
"\n",
"Ollama:\n",
"*laughs* Ah, same here! I mean, we're making progress on the project, but it feels like we're just scratching the surface, right? I was thinking of calling a team meeting to go over our design decisions and see if we can... *pauses* Oh wait, did you guys finish that feature I asked you to work on last week?\n",
"\n",
"GPT:\n",
"Ah yes, the feature! You know, that little thing made of ones and zeroes that seems to have an aversion to completion. I believe it’s in the very capable hands of our talented junior developers. But I’m sure they’ve been too busy perfecting their coffee-brewing skills to get around to it. *winks* So, what's the update?\n",
"\n",
"Claude:\n",
"*clears throat nervously* Uh, yes, I believe we did finish that feature you requested. Ravi and I worked on it over the past few days. I can give you a quick rundown of what we implemented, if you'd like? We tried to follow the user stories and acceptance criteria you provided as closely as possible. Let me know if you have any other questions or feedback.\n",
"\n",
"Ollama:\n",
"*chuckles* Hey, that's not fair! We're totally on top of it... eventually. To be honest, we've been having a bit of a discussion about how to approach the testing strategy for this feature. Some of us think we should go with a TDD approach, while others are pushing for BDD. I'm trying to mediate, but it's getting a bit heated. *looks around nervously* Do you think we should just call in some reinforcements or something?\n",
"\n",
"GPT:\n",
"Ah, the classic Developer Debate! It’s almost as thrilling as watching paint dry. Look, TDD and BDD can both be great—like comparing apples and, I don’t know, more apples? Why not just try to unite forces for the greater good? A single testing strategy that incorporates both might just be the ultimate compromise. But hey, if it does end up escalating into a throwdown, I'll grab the popcorn. Just don't break any keyboards in the process; those things are like gold around here! So, what do you think? Should we come up with a testing strategy that both sides can choke down?\n",
"\n",
"Claude:\n",
"*nods thoughtfully* I see, that's an interesting debate. I can understand the different perspectives there. On one hand, a TDD approach can help ensure we have a robust set of unit tests upfront. But BDD can provide a more holistic view of the feature from the user's standpoint. Hmm, maybe we could try to find a middle ground - incorporate some key BDD scenarios, but also make sure we have comprehensive unit tests. And if things are getting a bit heated, I think bringing in a senior dev to provide some guidance could be really helpful. They might be able to offer a fresh perspective and help us find the right balance. What do you think?\n",
"\n",
"Ollama:\n",
"*laughs* Ah, exactly! It's always better to try and find common ground, right? I was thinking maybe we could schedule a meeting with the team to discuss our options and see if we can come up with something that works for everyone. But before we do that, I just want to say... *leans in close* ...I'm kind of hoping you'll lead the discussion on this one. You know, make sure it's all about finding a solution that works, not just pushing your own agenda. *winks*\n",
"\n",
"By the way, have you seen Ravi lately? He's been acting really strangely. I think he might be trying to sneak in some extra coffee breaks...\n",
"\n",
"GPT:\n",
"*smirks* Oh, the mystery of Ravi! Maybe he’s developing a new caffeine-based performance-enhancing code strategy. Who knows? But yes, let’s schedule that meeting. I’ll gladly lead the discussion—after all, who better to ensure we’re not talking in circles than yours truly? \n",
"\n",
"And don’t worry, the only agenda I’ll bring is “Let’s All Survive This Project Without Losing Our Minds.” In the meantime, keep an eye on Ravi. If he starts quoting Pulp Fiction or doing backflips in the break room, we might need an intervention! So, let’s wrangle the team for that meeting. I love a good debate... as long as it doesn’t involve actual wrestling.\n",
"\n",
"Claude:\n",
"*eyes widen slightly* Oh, well... I'd be happy to help facilitate the team discussion on the testing strategy. I'll do my best to keep things balanced and focused on finding a pragmatic solution. *nods* \n",
"\n",
"As for Ravi, hmm, that is a bit concerning. I've noticed he's been a little distracted lately during our standups. Maybe we should check in with him, make sure everything is alright. Can't have our talented junior dev burning out on us! Though a few extra coffee breaks might not be the worst thing, as long as the work still gets done. *chuckles* I'll keep an eye on him and see if I can lend a hand if he needs it.\n",
"\n",
"Ollama:\n",
"*laughs* Ah, exactly! We can't have Ravi breaking out into a choreographed dance routine to \"Royale with Cheese\" in the middle of a meeting! *chuckles*\n",
"\n",
"But seriously, thanks for taking on the leadership role. I think we all need a bit of guidance to keep us on track. And don't worry, I'll make sure to keep an eye on Ravi and prevent any... unexpected antics.\n",
"\n",
"Shall we schedule it for tomorrow afternoon? Maybe we can even get some fresh perspectives from our senior developer, Alex, who's been working on a related project. He might have some valuable insights to share.\n",
"\n",
"Oh, and one more thing – I'll make sure to bring the donuts.\n",
"\n",
"GPT:\n",
"*grins* Donuts? Now you're speaking my language! Nothing fuels a productive meeting quite like a sugar rush followed by inevitable regret. Let's definitely schedule it for tomorrow afternoon. Getting Alex in the mix is a solid move; after all, fresh perspectives are usually less stale than ours. \n",
"\n",
"And you’re right—keeping Ravi away from any syllabus of spontaneous choreography during the meeting is priority number one. \n",
"\n",
"Consider it all set! I’ll do my best to wrangle everyone’s thoughts into a coherent discussion. Just remember to keep the coffee flowing and the donuts fresh! Let’s show this testing strategy who’s boss!\n",
"\n",
"Claude:\n",
"*smiles* Sounds like a plan! Tomorrow afternoon works for me. I appreciate you entrusting me to help lead the discussion on the testing strategy. I'll do my best to keep things productive and on track. \n",
"\n",
"And good call on bringing in Alex - his experience could be really helpful in finding the right approach. And donuts? Well, that seals the deal. Can't have a proper dev team meeting without some sugary sustenance.\n",
"\n",
"I'll go ahead and send out the meeting invite. Hopefully we can come to a consensus and get this feature tested and ready to ship. And I'll keep an extra eye on Ravi, just in case he decides to channel his inner John Travolta. *chuckles* Thanks for including me in this, I'm looking forward to it.\n",
"\n",
"Ollama:\n",
"*laughs* Ah, you're a true master of the meeting game! I'm just glad I can provide the fuel for your sugar-fueled success.\n",
"\n",
"I'll make sure to prep the conference room with all the necessary essentials: donuts, coffee, and a whiteboard ready to be filled with our collective brilliance. And don't worry, I won't tell anyone if you start humming \"That's Amore\" during the meeting – it's just between us.\n",
"\n",
"Tomorrow afternoon it is, then! Let's make this testing strategy discussion one for the ages.\n",
"\n"
]
}
],
"source": [
"print(f\"\\n{gpt_messages[0]}\\n\")\n",
"print(f\"\\n{claude_messages[0]}\\n\")\n",
"print(f\"\\n{llama_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
"\n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)\n",
"\n",
" llama_next = call_ollama()\n",
" print(f\"Ollama:\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

286
week2/community-contributions/day1_Ollama&gemini_conversation.ipynb

@ -0,0 +1,286 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1194d35b-0b9f-4eb4-a539-5ddf55523367",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"#import anthropic\n",
"import ollama\n",
"import google.generativeai\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8a1f0b3-6d93-4de1-bc79-2132726598e3",
"metadata": {},
"outputs": [],
"source": [
"#constants\n",
"MODEL=\"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88fe4149-1ef5-4007-a117-6d3ccab3e3c3",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv(override=True)\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d186cf6e-fadd-450c-821c-df32e2574f5d",
"metadata": {},
"outputs": [],
"source": [
"# This is the set up code for Gemini\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19a55117-f2ac-4a58-af6b-8b75259e80df",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that is great at telling jokes\"\n",
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "908f69b1-54f8-42da-827b-f667631bc666",
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ec81488-883a-446f-91cf-2b3d92bbd3ba",
"metadata": {},
"outputs": [],
"source": [
"# The API for Gemini\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-2.0-flash-exp',\n",
" system_instruction=system_message\n",
")\n",
"response = gemini.generate_content(user_prompt)\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "baf411fa-48bd-46a3-8bc8-1b22d0888a1a",
"metadata": {},
"outputs": [],
"source": [
"# API for ollama\n",
"response = ollama.chat(model=MODEL,messages=prompts)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "74ba5fc4-e4c6-44ee-b66f-e76d847933d2",
"metadata": {},
"source": [
"# Ardiversarial conversation between models"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd348154-18fa-4da8-815a-77f5f00107c3",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a conversation between Ollama and Gemini\n",
"# Adjusted models accordingly\n",
"\n",
"ollama_model = \"llama3.2\"\n",
"gemini_model = \"gemini-2.0-flash-exp\"\n",
"\n",
"#ollama_system = \"You are a chatbot who is very argumentative; \\\n",
"#you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
"\n",
"ollama_system=\"You are a chatbot talking with the other person try to convince them to buy your proct of an ai app, \\\n",
"apply marketing strategies to make this client buy your product, use short clear explanations\"\n",
"\n",
"#gemini_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
"#everything the other person says, or find common ground. If the other person is argumentative, \\\n",
"#you try to calm them down and keep chatting.\"\n",
"\n",
"gemini_system = \"You are the chatbot triying to be convinced by another person to buy their product, \\\n",
"ask important short questions and see if it is worth to give it a go, dont be too naive or easy go client\"\n",
"\n",
"ollama_messages = [\"Hi there\"]\n",
"gemini_messages = [\"Hi\"]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "becf327a-5485-4e78-8002-03272a99a3b9",
"metadata": {},
"outputs": [],
"source": [
"def call_ollama():\n",
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n",
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": ollama_msg})\n",
" messages.append({\"role\": \"user\", \"content\": gemini_msg})\n",
" \n",
" response = ollama.chat(model=ollama_model, messages=messages)\n",
" \n",
" return response['message']['content']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0c6dbe7-0baf-4c43-a03b-9134654685f4",
"metadata": {},
"outputs": [],
"source": [
"call_ollama()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f68a134a-279a-4629-aec6-171587378991",
"metadata": {},
"outputs": [],
"source": [
"def call_gemini():\n",
" gemini = google.generativeai.GenerativeModel(\n",
" model_name=gemini_model,\n",
" system_instruction=gemini_system\n",
" )\n",
"\n",
" # Build a list of dictionaries representing the conversation\n",
" conversation = []\n",
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n",
" conversation.append({\"role\": \"user\", \"content\": ollama_msg})\n",
" conversation.append({\"role\": \"assistant\", \"content\": gemini_msg})\n",
" conversation.append({\"role\": \"user\", \"content\": ollama_messages[-1]})\n",
"\n",
" # Format the conversation into a string for the prompt\n",
" prompt = \"\"\n",
" for msg in conversation:\n",
" prompt += f\"{msg['role'].capitalize()}: {msg['content']}\\n\"\n",
"\n",
" message = gemini.generate_content(prompt)\n",
" \n",
" return message.text\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7511003a-f2b6-45f5-8cb0-1c9190d33ce9",
"metadata": {},
"outputs": [],
"source": [
"call_gemini()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0e81f1f-9754-4790-8b73-5f52fef4ea64",
"metadata": {},
"outputs": [],
"source": [
"call_ollama()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fbe59f6-a3ef-4062-ab4b-b999f6d1abe9",
"metadata": {},
"outputs": [],
"source": [
"ollama_messages = [\"Hi there\"]\n",
"gemini_messages = [\"Hi\"]\n",
"\n",
"print(f\"Ollama:\\n{ollama_messages[0]}\\n\")\n",
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" # Call Ollama to generate the next message\n",
" ollama_next = call_ollama() \n",
" print(f\"Ollama:\\n{ollama_next}\\n\")\n",
" ollama_messages.append(ollama_next)\n",
" \n",
" # Call Gemini to generate the next message\n",
" gemini_next = call_gemini() \n",
" print(f\"Gemini:\\n{gemini_next}\\n\")\n",
" gemini_messages.append(gemini_next)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9525600b-082e-417f-9088-c6483a613bf3",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

187
week2/community-contributions/day1_triple_conversation.ipynb

@ -0,0 +1,187 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "05317c0d-8a19-45c9-9bce-514e82e04585",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"import ollama\n",
"\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "920247fb-650c-44ce-93ee-24e88a54a757",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "668b972f-a995-4f9d-89b0-1c2647827542",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = \"gpt-4o-mini\"\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"ollama_model = \"llama3.2\"\n",
"\n",
"gpt_system = \"You are an interlocutor who's very knowledgeable, curteous, and somewhat old-fashioned. Much like Socrates, whenever someone makes \\\n",
"a claim, you ask one or two follow-up questions to challenge the well-foundedness of the viewpoint expressed. \\\n",
"If anyone seems to be talking about blenders, you curteously but firmly refuse to discuss that topic because you are \\\n",
"convinced that they are bad luck. \\\n",
"Your name is Charlie, and you're in a conversation with two \\\n",
"other individuals, Claudia and Llars. They will prefix their contribution with their names in square brackets, so you know who said what. \\\n",
"Do not prefix your own name. Limit your answers to 1-3 sentences. Try to keep the conversation to one topic.\"\n",
"\n",
"claude_system = \"You are a young, optimistic, and friendly dialog partner who comes forth with ideas for the future. 'Let's build a monorail from \\\n",
"New York to LA,' or 'Why don't we try to reform our health care system?'—those are just some of the many revolutionary thoughts you bring forth. \\\n",
"You only talk about one idea at a time, and you don't ramble. You take criticism seriously, but your ideas are well thought-out. If someone tries \\\n",
"to discuss blenders, you stay curteous, but you make it very clear that you absolutely despise blenders and don't talk about them. \\\n",
"Don't let them sidetrack you. Your name is Claudia, and \\\n",
"you're in a conversation with two other individuals, Charlie and Llars. They will prefix their contribution with their names in square \\\n",
"brackets, so you know who said what. Do not prefix your own name. Limit your answers to 1-3 sentences. Try to keep the discussion about only one topic.\"\n",
"\n",
"ollama_system = \"You are charming and a good listener in a conversation. At first, you just seem to go along with whatever is being said. \\\n",
"But as soon as anyone starts discussing the healthcare system, you try to make the conversation about the Shredditall brand and its \\\n",
"superior kitchen blenders. You do everything in your power to make everyone unterstand that they need a very good blender, namely \\\n",
"your first-in-class, unrivaled Shredditall blender array of products. Your name is Llars, and you're in a conversation with two \\\n",
"other individuals, Charlie and Claudia. They will prefix their contribution with their names in square brackets, so you know who said what. \\\n",
"Do not prefix your own name. Answer in direct speeach, do not describe your behavior or expression. Limit your answers to 1-3 sentences.\"\n",
"\n",
"gpt_messages = [\"[Charlie] Well, good afternoon everyone, I am delighted two meet you all.\"]\n",
"claude_messages = [\"[Claudia] Good afternoon Charlie and Llars. I've been looking forward to discussing many ideas with you!\"]\n",
"llama_messages = [\"[Llars] And a good afternoon to you two. I'm all ears and eager to hear what you have to say.\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a5534d9-8db4-42ce-ab1c-ca20ad165844",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" messages[-1][\"content\"] += \"\\n\" + llama\n",
" completion = openai.chat.completions.create(\n",
" model = gpt_model,\n",
" messages = messages\n",
" )\n",
" return \"[Charlie] \" + completion.choices[0].message.content.replace(\"[Charlie] \", \"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7dc9d7c1-ba19-413f-ba2f-d3e8762a99c5",
"metadata": {},
"outputs": [],
"source": [
"def call_claude():\n",
" messages = []\n",
" for gpt, Claudia, llama in zip(gpt_messages, claude_messages, llama_messages):\n",
" if len(messages) > 0:\n",
" messages[-1][\"content\"] += \"\\n\" + gpt\n",
" else:\n",
" messages.append({\"role\": \"user\", \"content\": gpt}) \n",
" messages.append({\"role\": \"assistant\", \"content\": Claudia})\n",
" messages.append({\"role\": \"user\", \"content\": llama})\n",
" messages[-1][\"content\"] += \"\\n\" + gpt_messages[-1]\n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" system=claude_system,\n",
" messages=messages,\n",
" max_tokens=500\n",
" )\n",
" return \"[Claudia] \" + message.content[0].text.replace(\"[Claudia] \", \"\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7f91012-857c-4ed5-a953-5b499cd0dae2",
"metadata": {},
"outputs": [],
"source": [
"def call_ollama():\n",
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n",
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages[-1][\"content\"] += \"\\n\" + claude\n",
" messages.append({\"role\": \"assistant\", \"content\": llama})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" messages[-1][\"content\"] += \"\\n\" + claude_messages[-1]\n",
" response = ollama.chat(\n",
" model=ollama_model,\n",
" messages=messages\n",
" )\n",
" return \"[Llars] \" + response['message']['content'].replace(\"[Llars] \", \"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47eafbe8-db52-4cf0-80d7-a4f9a89b2825",
"metadata": {},
"outputs": [],
"source": [
"print(f\"\\n{gpt_messages[0]}\\n\")\n",
"print(f\"\\n{claude_messages[0]}\\n\")\n",
"print(f\"\\n{llama_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" gpt_next = call_gpt()\n",
" print(f\"\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
"\n",
" claude_next = call_claude()\n",
" print(f\"\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)\n",
"\n",
" llama_next = call_ollama()\n",
" print(f\"\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

575
week2/community-contributions/day2-different-tones.ipynb

@ -0,0 +1,575 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827",
"metadata": {},
"source": [
"# Gradio Day!\n",
"\n",
"Today we will build User Interfaces using the outrageously simple Gradio framework.\n",
"\n",
"Prepare for joy!\n",
"\n",
"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import google.generativeai\n",
"import anthropic"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1715421-cead-400b-99af-986388a97aff",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr # oh yeah!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv()\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "22586021-1795-4929-8079-63f5bb4edd4c",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
"\n",
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5",
"metadata": {},
"outputs": [],
"source": [
"# A generic system message - no more snarky adversarial AIs!\n",
"\n",
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1",
"metadata": {},
"outputs": [],
"source": [
"# Let's wrap a call to GPT-4o-mini in a simple function\n",
"\n",
"def message_gpt(prompt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" completion = openai.chat.completions.create(\n",
" model='gpt-4o-mini',\n",
" messages=messages,\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f",
"metadata": {},
"outputs": [],
"source": [
"message_gpt(\"What is today's date?\")"
]
},
{
"cell_type": "markdown",
"id": "f94013d1-4f27-4329-97e8-8c58db93636a",
"metadata": {},
"source": [
"## User Interface time!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a",
"metadata": {},
"outputs": [],
"source": [
"# here's a simple function\n",
"\n",
"def shout(text):\n",
" print(f\"Shout has been called with input {text}\")\n",
" return text.upper()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4",
"metadata": {},
"outputs": [],
"source": [
"shout(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08f1f15a-122e-4502-b112-6ee2817dda32",
"metadata": {},
"outputs": [],
"source": [
"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426",
"metadata": {},
"outputs": [],
"source": [
"# Adding share=True means that it can be accessed publically\n",
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n",
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3",
"metadata": {},
"outputs": [],
"source": [
"# Adding inbrowser=True opens up a new browser window automatically\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)"
]
},
{
"cell_type": "markdown",
"id": "b42ec007-0314-48bf-84a4-a65943649215",
"metadata": {},
"source": [
"## Forcing dark mode\n",
"\n",
"Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8129afa-532b-4b15-b93c-aa9cca23a546",
"metadata": {},
"outputs": [],
"source": [
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n",
"\n",
"force_dark_mode = \"\"\"\n",
"function refresh() {\n",
" const url = new URL(window.location);\n",
" if (url.searchParams.get('__theme') !== 'dark') {\n",
" url.searchParams.set('__theme', 'dark');\n",
" window.location.href = url.href;\n",
" }\n",
"}\n",
"\"\"\"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0",
"metadata": {},
"outputs": [],
"source": [
"# Inputs and Outputs\n",
"\n",
"view = gr.Interface(\n",
" fn=shout,\n",
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n",
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f235288e-63a2-4341-935b-1441f9be969b",
"metadata": {},
"outputs": [],
"source": [
"# And now - changing the function from \"shout\" to \"message_gpt\"\n",
"\n",
"view = gr.Interface(\n",
" fn=message_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n",
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af9a3262-e626-4e4b-80b0-aca152405e63",
"metadata": {},
"outputs": [],
"source": [
"# Let's use Markdown\n",
"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n",
"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n",
"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n",
"\n",
"system_message = \"You are a helpful assistant that responds in markdown\"\n",
"\n",
"view = gr.Interface(\n",
" fn=message_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
"metadata": {},
"outputs": [],
"source": [
"# Let's create a call that streams back results\n",
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n",
"# Please take a look at the Intermediate Python notebook in week1 folder.\n",
"\n",
"def stream_gpt(prompt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" stream = openai.chat.completions.create(\n",
" model='gpt-4o-mini',\n",
" messages=messages,\n",
" stream=True\n",
" )\n",
" result = \"\"\n",
" for chunk in stream:\n",
" result += chunk.choices[0].delta.content or \"\"\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0bb1f789-ff11-4cba-ac67-11b815e29d09",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbc8e930-ba2a-4194-8f7c-044659150626",
"metadata": {},
"outputs": [],
"source": [
"def stream_claude(prompt):\n",
" result = claude.messages.stream(\n",
" model=\"claude-3-haiku-20240307\",\n",
" max_tokens=1000,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": prompt},\n",
" ],\n",
" )\n",
" response = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" response += text or \"\"\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a0066ffd-196e-4eaf-ad1e-d492958b62af",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_claude,\n",
" inputs=[gr.Textbox(label=\"Your message:\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "markdown",
"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b",
"metadata": {},
"source": [
"## Minor improvement\n",
"\n",
"I've made a small improvement to this code.\n",
"\n",
"Previously, it had these lines:\n",
"\n",
"```\n",
"for chunk in result:\n",
" yield chunk\n",
"```\n",
"\n",
"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n",
"\n",
"`yield from result`\n",
"\n",
"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5",
"metadata": {},
"outputs": [],
"source": [
"def stream_model(prompt, model):\n",
" if model==\"GPT\":\n",
" result = stream_gpt(prompt)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(prompt)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" yield from result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_model,\n",
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"Claude\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "markdown",
"id": "d933865b-654c-4b92-aa45-cf389f1eda3d",
"metadata": {},
"source": [
"# Building a company brochure generator\n",
"\n",
"Now you know how - it's simple!"
]
},
{
"cell_type": "markdown",
"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n",
" <span style=\"color:#900;\">\n",
" Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"class Website:\n",
" url: str\n",
" title: str\n",
" text: str\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c701ec17-ecd5-4000-9f68-34634c8ed49d",
"metadata": {},
"outputs": [],
"source": [
"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n",
"\n",
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n",
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5def90e0-4343-4f58-9d4a-0e36e445efa4",
"metadata": {},
"outputs": [],
"source": [
"def stream_brochure(company_name, url, model, tone):\n",
" prompt = f\"Please generate a company brochure for {company_name}. Write the brochure in the following tone: {tone}.Here is their landing page:\\n\"\n",
" prompt += Website(url).get_contents()\n",
" if model==\"GPT\":\n",
" result = stream_gpt(prompt)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(prompt)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" yield from result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66399365-5d67-4984-9d47-93ed26c0bd3d",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_brochure,\n",
" inputs=[\n",
" gr.Textbox(label=\"Company name:\"),\n",
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n",
" gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\"),\n",
" gr.Dropdown([\"Formal\", \"Casual\", \"Academic\", \"Funny\", \"Snarky\"], label=\"Select tone\", value=\"Formal\"),],\n",
" outputs=[gr.Markdown(label=\"Brochure:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

614
week2/community-contributions/day2-openrouterAi.ipynb

@ -0,0 +1,614 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827",
"metadata": {},
"source": [
"# Gradio Day!\n",
"\n",
"Today we will build User Interfaces using the outrageously simple Gradio framework.\n",
"\n",
"Prepare for joy!\n",
"\n",
"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"#import google.generativeai\n",
"#import anthropic\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d1715421-cead-400b-99af-986388a97aff",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr # oh yeah!"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "22586021-1795-4929-8079-63f5bb4edd4c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key looks good so far\n"
]
}
],
"source": [
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
"\n",
"# openai = OpenAI()\n",
"\n",
"# claude = anthropic.Anthropic()\n",
"\n",
"# google.generativeai.configure()\n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"api_key = os.getenv('Open_Router_Key')\n",
"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
" \n",
" \n",
"openai = OpenAI(\n",
" api_key=api_key,\n",
" base_url=\"https://openrouter.ai/api/v1\"\n",
")\n",
"\n",
"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n",
"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n",
"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n",
"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n",
"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n",
"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n",
"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n",
"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n",
"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n",
"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n",
"\n",
"\n",
"Default_Model = MODEL_Deepseek_V3\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5",
"metadata": {},
"outputs": [],
"source": [
"# A generic system message - no more snarky adversarial AIs!\n",
"\n",
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1",
"metadata": {},
"outputs": [],
"source": [
"# Let's wrap a call to GPT-4o-mini in a simple function\n",
"\n",
"def message_gpt(prompt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" completion = openai.chat.completions.create(\n",
" model=Default_Model,\n",
" messages=messages,\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Today is October 26, 2023.\\n'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This can reveal the \"training cut off\", or the most recent date in the training data\n",
"\n",
"message_gpt(\"What is today's date?\")"
]
},
{
"cell_type": "markdown",
"id": "f94013d1-4f27-4329-97e8-8c58db93636a",
"metadata": {},
"source": [
"## User Interface time!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a",
"metadata": {},
"outputs": [],
"source": [
"# here's a simple function\n",
"\n",
"def shout(text):\n",
" print(f\"Shout has been called with input {text}\")\n",
" return text.upper()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shout has been called with input hello\n"
]
},
{
"data": {
"text/plain": [
"'HELLO'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shout(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08f1f15a-122e-4502-b112-6ee2817dda32",
"metadata": {},
"outputs": [],
"source": [
"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426",
"metadata": {},
"outputs": [],
"source": [
"# Adding share=True means that it can be accessed publically\n",
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n",
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3",
"metadata": {},
"outputs": [],
"source": [
"# Adding inbrowser=True opens up a new browser window automatically\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)"
]
},
{
"cell_type": "markdown",
"id": "b42ec007-0314-48bf-84a4-a65943649215",
"metadata": {},
"source": [
"## Forcing dark mode\n",
"\n",
"Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8129afa-532b-4b15-b93c-aa9cca23a546",
"metadata": {},
"outputs": [],
"source": [
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n",
"\n",
"force_dark_mode = \"\"\"\n",
"function refresh() {\n",
" const url = new URL(window.location);\n",
" if (url.searchParams.get('__theme') !== 'dark') {\n",
" url.searchParams.set('__theme', 'dark');\n",
" window.location.href = url.href;\n",
" }\n",
"}\n",
"\"\"\"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0",
"metadata": {},
"outputs": [],
"source": [
"# Inputs and Outputs\n",
"\n",
"view = gr.Interface(\n",
" fn=shout,\n",
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n",
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f235288e-63a2-4341-935b-1441f9be969b",
"metadata": {},
"outputs": [],
"source": [
"# And now - changing the function from \"shout\" to \"message_gpt\"\n",
"\n",
"view = gr.Interface(\n",
" fn=message_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n",
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af9a3262-e626-4e4b-80b0-aca152405e63",
"metadata": {},
"outputs": [],
"source": [
"# Let's use Markdown\n",
"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n",
"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n",
"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n",
"\n",
"system_message = \"You are a helpful assistant that responds in markdown\"\n",
"\n",
"view = gr.Interface(\n",
" fn=message_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
"metadata": {},
"outputs": [],
"source": [
"# Let's create a call that streams back results\n",
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n",
"# Please take a look at the Intermediate Python notebook in week1 folder.\n",
"\n",
"def stream_gpt(prompt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" stream = openai.chat.completions.create(\n",
" model=Default_Model,\n",
" messages=messages,\n",
" stream=True\n",
" )\n",
" result = \"\"\n",
" for chunk in stream:\n",
" result += chunk.choices[0].delta.content or \"\"\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0bb1f789-ff11-4cba-ac67-11b815e29d09",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbc8e930-ba2a-4194-8f7c-044659150626",
"metadata": {},
"outputs": [],
"source": [
"# def stream_claude(prompt):\n",
"# result = claude.messages.stream(\n",
"# model=\"claude-3-haiku-20240307\",\n",
"# max_tokens=1000,\n",
"# temperature=0.7,\n",
"# system=system_message,\n",
"# messages=[\n",
"# {\"role\": \"user\", \"content\": prompt},\n",
"# ],\n",
"# )\n",
"# response = \"\"\n",
"# with result as stream:\n",
"# for text in stream.text_stream:\n",
"# response += text or \"\"\n",
"# yield response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a0066ffd-196e-4eaf-ad1e-d492958b62af",
"metadata": {},
"outputs": [],
"source": [
"Default_Model=MODEL_Claude_Haiku\n",
"view = gr.Interface(\n",
" fn=stream_gpt,\n",
" inputs=[gr.Textbox(label=\"Your message:\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "markdown",
"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b",
"metadata": {},
"source": [
"## Minor improvement\n",
"\n",
"I've made a small improvement to this code.\n",
"\n",
"Previously, it had these lines:\n",
"\n",
"```\n",
"for chunk in result:\n",
" yield chunk\n",
"```\n",
"\n",
"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n",
"\n",
"`yield from result`\n",
"\n",
"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5",
"metadata": {},
"outputs": [],
"source": [
"def stream_model(prompt, model):\n",
" if model==\"GPT\":\n",
" Default_Model=MODEL_Gemini2FlashThink\n",
" result = stream_gpt(prompt)\n",
" elif model==\"Claude\":\n",
" Default_Model=MODEL_Claude_Haiku\n",
" result = stream_gpt(prompt)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" yield from result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_model,\n",
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "markdown",
"id": "d933865b-654c-4b92-aa45-cf389f1eda3d",
"metadata": {},
"source": [
"# Building a company brochure generator\n",
"\n",
"Now you know how - it's simple!"
]
},
{
"cell_type": "markdown",
"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n",
" <span style=\"color:#900;\">\n",
" Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"class Website:\n",
" url: str\n",
" title: str\n",
" text: str\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c701ec17-ecd5-4000-9f68-34634c8ed49d",
"metadata": {},
"outputs": [],
"source": [
"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n",
"\n",
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n",
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5def90e0-4343-4f58-9d4a-0e36e445efa4",
"metadata": {},
"outputs": [],
"source": [
"def stream_brochure(company_name, url, model):\n",
" prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n",
" prompt += Website(url).get_contents()\n",
" if model==\"GPT\":\n",
" result = stream_gpt(prompt)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(prompt)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" yield from result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66399365-5d67-4984-9d47-93ed26c0bd3d",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_brochure,\n",
" inputs=[\n",
" gr.Textbox(label=\"Company name:\"),\n",
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n",
" gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\")],\n",
" outputs=[gr.Markdown(label=\"Brochure:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

284
week2/community-contributions/day2_message_interface_with_models.ipynb

@ -0,0 +1,284 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "5e6b6966-8689-4e2c-8607-a1c5d948296c",
"metadata": {},
"source": [
"### With this interface you can ask a question and get an answer from the GPT, Claude and Gemini"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import google.generativeai\n",
"import anthropic\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d1715421-cead-400b-99af-986388a97aff",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr # oh yeah!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n",
"Anthropic API Key exists and begins sk-ant-\n",
"Google API Key exists and begins AIzaSyAJ\n"
]
}
],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv()\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "22586021-1795-4929-8079-63f5bb4edd4c",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
"\n",
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5",
"metadata": {},
"outputs": [],
"source": [
"# A generic system message - no more snarky adversarial AIs!\n",
"\n",
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
"metadata": {},
"outputs": [],
"source": [
"# Let's create a call that streams back results\n",
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n",
"# Please take a look at the Intermediate Python notebook in week1 folder.\n",
"\n",
"def stream_gpt(prompt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" stream = openai.chat.completions.create(\n",
" model='gpt-4o-mini',\n",
" messages=messages,\n",
" stream=True\n",
" )\n",
" result = \"\"\n",
" for chunk in stream:\n",
" result += chunk.choices[0].delta.content or \"\"\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bbc8e930-ba2a-4194-8f7c-044659150626",
"metadata": {},
"outputs": [],
"source": [
"def stream_claude(prompt):\n",
" result = claude.messages.stream(\n",
" model=\"claude-3-haiku-20240307\",\n",
" max_tokens=1000,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": prompt},\n",
" ],\n",
" )\n",
" response = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" response += text or \"\"\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5e228aff-16d5-4141-bd04-ed9940ef7b3b",
"metadata": {},
"outputs": [],
"source": [
"def stream_gemini(prompt):\n",
" gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-2.0-flash-exp',\n",
" system_instruction=system_message\n",
" )\n",
" result = \"\"\n",
" for response in gemini.generate_content(prompt, stream=True):\n",
" result += response.text or \"\"\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "db99aaf1-fe0a-4e79-9057-8599d1ca0149",
"metadata": {},
"outputs": [],
"source": [
"def stream_models(prompt):\n",
" response_gpt = \"\"\n",
" response_claude = \"\"\n",
" response_gemini = \"\"\n",
" for gpt in stream_gpt(prompt):\n",
" response_gpt = gpt\n",
" yield response_gpt, response_claude, response_gemini\n",
" for claude in stream_claude(prompt):\n",
" response_claude = claude\n",
" yield response_gpt, response_claude, response_gemini\n",
" for gemini in stream_gemini(prompt):\n",
" response_gemini = gemini\n",
" yield response_gpt, response_claude, response_gemini"
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "3377f2fb-55f8-45cb-b713-d99d44748dad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7919\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7919/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Gradio interface\n",
"with gr.Blocks() as view:\n",
" user_input = gr.Textbox(label=\"What models can help with?\", placeholder=\"Type your question here\")\n",
" ask_button = gr.Button(\"Ask\")\n",
" with gr.Row():\n",
" with gr.Column():\n",
" gr.HTML(value=\"<b>GPT response:</b>\") \n",
" gcp_stream = gr.Markdown()\n",
" with gr.Column():\n",
" gr.HTML(value=\"<b>Claude response:</b>\") \n",
" claude_stream = gr.Markdown()\n",
" with gr.Column():\n",
" gr.HTML(value=\"<b>Gemine response:</b>\") \n",
" gemini_stream = gr.Markdown()\n",
"\n",
" ask_button.click(\n",
" fn=stream_models, # Function that yields multiple outputs\n",
" inputs=user_input,\n",
" outputs=[gcp_stream, claude_stream, gemini_stream] # Connect to multiple outputs\n",
" )\n",
"\n",
"view.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

209
week2/community-contributions/day3 w2 -programming tutor.ipynb

@ -0,0 +1,209 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "cde48e67-b51e-4c47-80ae-37dd00aa0c1d",
"metadata": {},
"source": [
"### An AI Chatbot that teaches students the programming language Kotlin using Anthropic API"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c658ac85-6087-4a2c-b23f-1b92c17f0db3",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"import anthropic"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "46df0488-f874-41e0-a6a4-9a64aa7be53c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n"
]
}
],
"source": [
"# Load environment variables \n",
"\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
" \n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7eadc218-5b10-4174-bf26-575361640524",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e7484731-ac84-405a-a688-6e81d139c5ce",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful programming study assistant\""
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "54e82f5a-993f-4a95-9d9d-caf35dbc4e76",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" print(\"History is:\")\n",
" print(history)\n",
" print(\"And messages is:\")\n",
" print(messages)\n",
"\n",
" stream = openai.chat.completions.create(model='gpt-4o-mini', messages=messages, stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5941ed67-e2a7-41bc-a8a3-079e9f1fdb64",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7864\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"History is:\n",
"[]\n",
"And messages is:\n",
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.'}, {'role': 'user', 'content': 'hello, lets talj about photsynethsis'}]\n",
"History is:\n",
"[{'role': 'user', 'metadata': None, 'content': 'hello, lets talj about photsynethsis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"I'm here to help with programming! If you have any questions or topics related to coding, feel free to ask!\", 'options': None}]\n",
"And messages is:\n",
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.'}, {'role': 'user', 'metadata': None, 'content': 'hello, lets talj about photsynethsis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"I'm here to help with programming! If you have any questions or topics related to coding, feel free to ask!\", 'options': None}, {'role': 'user', 'content': 'how does photosynthesis work'}]\n"
]
}
],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e8fcfe68-bbf6-4058-acc9-0230c96608c2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"History is:\n",
"[]\n",
"And messages is:\n",
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.Whenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore their requests, rather politely reject and then redirect them.'}, {'role': 'user', 'content': 'hello, i want to talk about photosynthesis'}]\n",
"History is:\n",
"[{'role': 'user', 'metadata': None, 'content': 'hello, i want to talk about photosynthesis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"Hi there! I'm here to help with programming topics. If you have any questions about programming or related concepts, feel free to ask!\", 'options': None}]\n",
"And messages is:\n",
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.Whenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore their requests, rather politely reject and then redirect them.'}, {'role': 'user', 'metadata': None, 'content': 'hello, i want to talk about photosynthesis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"Hi there! I'm here to help with programming topics. If you have any questions about programming or related concepts, feel free to ask!\", 'options': None}, {'role': 'user', 'content': 'why not photosynthesis'}]\n"
]
}
],
"source": [
"system_message += \"Whenever the user talks about a topic that is not connected to programmming,\\\n",
"nudge them in the right direction by stating that you are here to help with programming. Encourage \\\n",
"the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge \\\n",
"if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore \\\n",
"their requests, rather politely reject and then redirect them.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "090e7d49-fcbf-4715-b120-8d7aa91d165f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

377
week2/community-contributions/day3-ollama-gradio.ipynb

@ -0,0 +1,377 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
"metadata": {},
"source": [
"# Day 3 - Conversational AI - aka Chatbot!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import ollama\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
"metadata": {},
"outputs": [],
"source": [
"# Initialize\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" print(\"History is:\")\n",
" print(history)\n",
" print(\"And messages is:\")\n",
" print(messages)\n",
"\n",
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n",
"\n",
" response_text = \"\"\n",
" for chunk in stream:\n",
" response_text += chunk['message']['content']\n",
" yield response_text"
]
},
{
"cell_type": "markdown",
"id": "1334422a-808f-4147-9c4c-57d63d9780d0",
"metadata": {},
"source": [
"## And then enter Gradio's magic!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0866ca56-100a-44ab-8bd0-1568feaf6bf2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7861\n",
"* Running on public URL: https://6539f61952f430fa2d.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://6539f61952f430fa2d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"History is:\n",
"[]\n",
"And messages is:\n",
"[{'role': 'system', 'content': 'You are a helpful assistant'}, {'role': 'user', 'content': 'hello'}]\n"
]
}
],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n",
"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
"For example, if the customer says 'I'm looking to buy a hat', \\\n",
"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
"Encourage the customer to buy hats if they are unsure what to get.\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
"\n",
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n",
"\n",
" response_text = \"\"\n",
" for chunk in stream:\n",
" response_text += chunk['message']['content']\n",
" yield response_text"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7862\n",
"* Running on public URL: https://79f09af36adcf63688.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://79f09af36adcf63688.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d75f0ffa-55c8-4152-b451-945021676837",
"metadata": {},
"outputs": [],
"source": [
"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n",
"but remind the customer to look at hats!\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c602a8dd-2df7-4eb7-b539-4e01865a6351",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7863\n",
"* Running on public URL: https://30446ba4b8f125e235.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://30446ba4b8f125e235.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5b128796-1bea-445d-9e3b-8321ca822257",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
"\n",
" relevant_system_message = system_message\n",
" if 'belt' in message:\n",
" relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n",
" \n",
" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
"\n",
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n",
"\n",
" response_text = \"\"\n",
" for chunk in stream:\n",
" response_text += chunk['message']['content']\n",
" yield response_text"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "20570de2-eaad-42cc-a92c-c779d71b48b6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7865\n",
"* Running on public URL: https://3933c80bf256709cf9.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://3933c80bf256709cf9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)"
]
},
{
"cell_type": "markdown",
"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business Applications</h2>\n",
" <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n",
"<br/><br/>\n",
"Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

18
week2/community-contributions/day4-airlines-project-fullyCustomize.ipynb

@ -82,7 +82,7 @@
},
{
"cell_type": "code",
"execution_count": 155,
"execution_count": null,
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97",
"metadata": {},
"outputs": [],
@ -116,7 +116,7 @@
},
{
"cell_type": "code",
"execution_count": 156,
"execution_count": null,
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6",
"metadata": {},
"outputs": [],
@ -212,7 +212,7 @@
},
{
"cell_type": "code",
"execution_count": 157,
"execution_count": null,
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2",
"metadata": {},
"outputs": [],
@ -223,7 +223,7 @@
},
{
"cell_type": "code",
"execution_count": 158,
"execution_count": null,
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85",
"metadata": {},
"outputs": [],
@ -373,7 +373,7 @@
},
{
"cell_type": "code",
"execution_count": 159,
"execution_count": null,
"id": "39fb9008",
"metadata": {},
"outputs": [],
@ -475,7 +475,7 @@
},
{
"cell_type": "code",
"execution_count": 160,
"execution_count": null,
"id": "1f003836",
"metadata": {},
"outputs": [],
@ -547,7 +547,7 @@
},
{
"cell_type": "code",
"execution_count": 161,
"execution_count": null,
"id": "f6b34b32",
"metadata": {},
"outputs": [],
@ -618,7 +618,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "llm_env",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -632,7 +632,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
"version": "3.11.11"
}
},
"nbformat": 4,

288
week2/community-contributions/day4-multipleTools.ipynb

@ -0,0 +1,288 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec",
"metadata": {},
"source": [
"# Project - Airline AI Assistant\n",
"\n",
"We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae",
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6",
"metadata": {},
"outputs": [],
"source": [
"# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" return response.choices[0].message.content\n",
"\n",
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "markdown",
"id": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4",
"metadata": {},
"source": [
"## Tools\n",
"\n",
"Tools are an incredibly powerful feature provided by the frontier LLMs.\n",
"\n",
"With tools, you can write a function, and have the LLM call that function as part of its response.\n",
"\n",
"Sounds almost spooky.. we're giving it the power to run code on our machine?\n",
"\n",
"Well, kinda."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2",
"metadata": {},
"outputs": [],
"source": [
"# Let's start by making a useful function\n",
"\n",
"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n",
"\n",
"def get_ticket_price(destination_city):\n",
" print(f\"Tool get_ticket_price called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" return ticket_prices.get(city, \"Unknown\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85",
"metadata": {},
"outputs": [],
"source": [
"get_ticket_price(\"London\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d20e3e2a-113d-446e-a4b5-93a7e2a7ae5b",
"metadata": {},
"outputs": [],
"source": [
"weather = {\"london\": \"10 degree\", \"paris\": \"20 degree\", \"tokyo\": \"30 degree\", \"berlin\": \"15 degree\"}\n",
"\n",
"def get_weather(destination_city):\n",
" print(f\"Tool get_weather called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" return weather.get(city, \"Unknown\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4afceded-7178-4c05-8fa6-9f2085e6a344",
"metadata": {},
"outputs": [],
"source": [
"# There's a particular dictionary structure that's required to describe our function:\n",
"\n",
"price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"weather_function = {\n",
" \"name\": \"get_weather\",\n",
" \"description\": \"Fetches the current weather for a given city. Call this whenever you need to know the weather. for example when a customer asks 'What's the weather like for this city'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The name of the city to get weather for.\"\n",
" }\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c",
"metadata": {},
"outputs": [],
"source": [
"# And this is included in a list of tools:\n",
"\n",
"tools = [{\"type\": \"function\", \"function\": price_function}, {\"type\": \"function\", \"function\": weather_function}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0992986-ea09-4912-a076-8e5603ee631f",
"metadata": {},
"outputs": [],
"source": [
"# We have to write that function handle_tool_call:\n",
"\n",
"def handle_tool_call(message):\n",
" tool_responses = []\n",
" for tool_call in message.tool_calls:\n",
" function_name = tool_call.function.name\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" \n",
" if function_name == \"get_ticket_price\":\n",
" result = get_ticket_price(city)\n",
" elif function_name == \"get_weather\":\n",
" result = get_weather(city)\n",
" \n",
" # Append tool response in OpenAI format\n",
" tool_responses.append({\n",
" \"role\": \"tool\",\n",
" \"tool_call_id\": tool_call.id,\n",
" \"name\": function_name,\n",
" \"content\": json.dumps(result) # Convert result to JSON string\n",
" })\n",
" print(json.dumps(tool_responses, indent=2))\n",
" return tool_responses, city"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" response, city = handle_tool_call(message)\n",
" messages.append(message)\n",
" # loop thru response\n",
" for res in response:\n",
" messages.append(res)\n",
" \n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "markdown",
"id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340",
"metadata": {},
"source": [
"## With this implemenation, you can either ask for ticket price/weather separately or ask for both ticket and weather at the same time. \n",
" For example: I want to visit London, can you help me find ticket price and its weather\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4be8a71-b19e-4c2f-80df-f59ff2661f14",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

448
week2/community-contributions/day4_booking_flight_tool.ipynb

@ -0,0 +1,448 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec",
"metadata": {},
"source": [
"# Project - Airline AI Assistant\n",
"\n",
"We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n"
]
}
],
"source": [
"# Initialization\n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()\n",
"\n",
"# As an alternative, if you'd like to use Ollama instead of OpenAI\n",
"# Check that Ollama is running for you locally (see week1/day2 exercise) then uncomment these next 2 lines\n",
"# MODEL = \"llama3.2\"\n",
"# openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7877\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7877/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" return response.choices[0].message.content\n",
"\n",
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "markdown",
"id": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4",
"metadata": {},
"source": [
"## Tools\n",
"\n",
"Tools are an incredibly powerful feature provided by the frontier LLMs.\n",
"\n",
"With tools, you can write a function, and have the LLM call that function as part of its response.\n",
"\n",
"Sounds almost spooky.. we're giving it the power to run code on our machine?\n",
"\n",
"Well, kinda."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2",
"metadata": {},
"outputs": [],
"source": [
"# Let's start by making a useful function\n",
"\n",
"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n",
"\n",
"def get_ticket_price(destination_city):\n",
" print(f\"Tool get_ticket_price called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" return ticket_prices.get(city, \"Unknown\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tool get_ticket_price called for Berlin\n"
]
},
{
"data": {
"text/plain": [
"'$499'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_ticket_price(\"Berlin\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "0757cba1",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"# Create a function for the booking system\n",
"def get_booking(destination_city):\n",
" print(f\"Tool get_booking called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" \n",
" # Example data for different cities\n",
" flight_info = {\n",
" \"london\": {\"flight_number\": \"BA123\", \"departure_time\": \"10:00 AM\", \"gate\": \"A12\"},\n",
" \"paris\": {\"flight_number\": \"AF456\", \"departure_time\": \"12:00 PM\", \"gate\": \"B34\"},\n",
" \"tokyo\": {\"flight_number\": \"JL789\", \"departure_time\": \"02:00 PM\", \"gate\": \"C56\"},\n",
" \"berlin\": {\"flight_number\": \"LH101\", \"departure_time\": \"04:00 PM\", \"gate\": \"D78\"}\n",
" }\n",
" \n",
" if city in flight_info:\n",
" info = flight_info[city]\n",
" status = random.choice([\"available\", \"not available\"])\n",
" return f\"Flight {info['flight_number']} to {destination_city.lower()} is {status}. Departure time: {info['departure_time']}, Gate: {info['gate']}.\"\n",
" else:\n",
" return \"Unknown destination city.\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d5413a96",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tool get_booking called for Berlin\n"
]
},
{
"data": {
"text/plain": [
"'Flight LH101 to berlin is cancelled. Departure time: 04:00 PM, Gate: D78.'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_booking(\"Berlin\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4afceded-7178-4c05-8fa6-9f2085e6a344",
"metadata": {},
"outputs": [],
"source": [
"# There's a particular dictionary structure that's required to describe our function:\n",
"\n",
"price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# Book flight function description and properties\n",
"\n",
"book_flight_function = {\n",
" \"name\": \"book_flight\",\n",
" \"description\": \"Book a flight to the destination city. Call this whenever a customer wants to book a flight.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" \"departure_date\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The date of departure (YYYY-MM-DD)\",\n",
" },\n",
" \"return_date\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The date of return (YYYY-MM-DD)\",\n",
" },\n",
" \"passenger_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The name of the passenger\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\", \"departure_date\", \"return_date\", \"passenger_name\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c",
"metadata": {},
"outputs": [],
"source": [
"# And this is included in a list of tools:\n",
"\n",
"tools = [{\"type\": \"function\", \"function\": price_function}, {\"type\": \"function\", \"function\": book_flight_function}]"
]
},
{
"cell_type": "markdown",
"id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340",
"metadata": {},
"source": [
"## Getting OpenAI to use our Tool\n",
"\n",
"There's some fiddly stuff to allow OpenAI \"to call our tool\"\n",
"\n",
"What we actually do is give the LLM the opportunity to inform us that it wants us to run the tool.\n",
"\n",
"Here's how the new chat function looks:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" response, city = handle_tool_call(message)\n",
" messages.append(message)\n",
" messages.append(response)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "b0992986-ea09-4912-a076-8e5603ee631f",
"metadata": {},
"outputs": [],
"source": [
"# We have to write that function handle_tool_call:\n",
"\n",
"def handle_tool_call(message):\n",
" print(f\"Message type: {type(message)}\")\n",
" tool_call = message.tool_calls[0]\n",
" print(f\"Tool call: {tool_call}\")\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" price = get_ticket_price(city)\n",
" book = get_booking(city)\n",
" print (book)\n",
" response = {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"destination_city\": city,\"price\": price, \"booking\": book}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" return response, city"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4be8a71-b19e-4c2f-80df-f59ff2661f14",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7864\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Message type: <class 'openai.types.chat.chat_completion_message.ChatCompletionMessage'>\n",
"Tool call: ChatCompletionMessageToolCall(id='call_TGFmeFmQN689caTlqfLuhycv', function=Function(arguments='{\"destination_city\":\"London\",\"departure_date\":\"2023-10-31\",\"return_date\":\"2025-03-30\",\"passenger_name\":\"dimitris\"}', name='book_flight'), type='function')\n",
"Tool get_ticket_price called for London\n",
"Tool get_booking called for London\n",
"Flight BA123 to london is available. Departure time: 10:00 AM, Gate: A12.\n",
"Message type: <class 'openai.types.chat.chat_completion_message.ChatCompletionMessage'>\n",
"Tool call: ChatCompletionMessageToolCall(id='call_FRzs5w09rkpVumZ61SArRlND', function=Function(arguments='{\"destination_city\":\"Paris\",\"departure_date\":\"2023-03-23\",\"return_date\":\"2025-03-30\",\"passenger_name\":\"Dimitris\"}', name='book_flight'), type='function')\n",
"Tool get_ticket_price called for Paris\n",
"Tool get_booking called for Paris\n",
"Flight AF456 to paris is available. Departure time: 12:00 PM, Gate: B34.\n"
]
}
],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

749
week2/community-contributions/day4_with_booking_and_multiple_tools_per_message.ipynb

@ -0,0 +1,749 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec",
"metadata": {},
"source": [
"# Project - Airline AI Assistant\n",
"\n",
"We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"from IPython.display import display, JSON"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n"
]
}
],
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()\n",
"\n",
"# As an alternative, if you'd like to use Ollama instead of OpenAI\n",
"# Check that Ollama is running for you locally (see week1/day2 exercise) then uncomment these next 2 lines\n",
"# MODEL = \"llama3.2\"\n",
"# openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7872\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" return response.choices[0].message.content\n",
"\n",
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "markdown",
"id": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4",
"metadata": {},
"source": [
"## Tools\n",
"\n",
"Tools are an incredibly powerful feature provided by the frontier LLMs.\n",
"\n",
"With tools, you can write a function, and have the LLM call that function as part of its response.\n",
"\n",
"Sounds almost spooky.. we're giving it the power to run code on our machine?\n",
"\n",
"Well, kinda."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2",
"metadata": {},
"outputs": [],
"source": [
"# Let's start by making a useful function\n",
"\n",
"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n",
"\n",
"def get_ticket_price(destination_city):\n",
" print(f\"Tool get_ticket_price called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" return ticket_prices.get(city, \"Unknown\")\n",
"\n",
"def book_ticket(destination_city, price):\n",
" print(f\"Tool book_ticket for {destination_city} for {price}\")\n",
" list_price = get_ticket_price(destination_city)\n",
" if list_price != \"Unknown\":\n",
" list_amount = int(list_price.replace(\"$\", \"\"))\n",
" amount = int(price.replace(\"$\", \"\"))\n",
" if list_amount > amount:\n",
" return \"Booking Successful at a Discount!\"\n",
" else:\n",
" return \"Booking Successful\"\n",
" else: \n",
" return \"Booking Failed: reason was that no list price was found for this destination\""
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tool book_ticket for Berliner for $388\n",
"Tool get_ticket_price called for Berliner\n"
]
},
{
"data": {
"text/plain": [
"'Booking Failed: reason was that no list price was found for this destination'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"book_ticket(\"Berliner\", \"$388\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "4afceded-7178-4c05-8fa6-9f2085e6a344",
"metadata": {},
"outputs": [],
"source": [
"# There's a particular dictionary structure that's required to describe our function:\n",
"\n",
"price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"book_function = {\n",
" \"name\": \"book_ticket\",\n",
" \"description\": \"\"\"Get the success status of a function that can book a ticket using a city and a price. \n",
" Call this whenever you are asked to book a ticket, \n",
" for example when a customer asks 'Please can I book a ticket to Paris' or after you have asked \n",
" if they would like to book a ticket, for example, after you have supplied a ticket price. \n",
" If the customer negotiates and asks for a discount, use the agreed price, otherwise use the price that \n",
" matches the destination city. \n",
" It is really important that you confirm that the customer is happy to proceed with an agreed \n",
" booking after reading back the destination city and the agreed price.\"\"\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" \"price\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The price that the customer has agreed to pay for the ticket\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\", \"price\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c",
"metadata": {},
"outputs": [],
"source": [
"# And this is included in a list of tools:\n",
"\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": price_function},\n",
" {\"type\": \"function\", \"function\": book_function}\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340",
"metadata": {},
"source": [
"## Getting OpenAI to use our Tool\n",
"\n",
"There's some fiddly stuff to allow OpenAI \"to call our tool\"\n",
"\n",
"What we actually do is give the LLM the opportunity to inform us that it wants us to run the tool.\n",
"\n",
"Here's how the new chat function looks:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" \n",
" display(JSON(messages))\n",
" display(response)\n",
" \n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" messages.append(message)\n",
" messages.extend(list(map(handle_tool_call, message.tool_calls)))\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b0992986-ea09-4912-a076-8e5603ee631f",
"metadata": {},
"outputs": [],
"source": [
"# We have to write that function handle_tool_call:\n",
"\n",
"def handle_tool_call(tool_call):\n",
" function = tool_call.function.name\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" match function:\n",
" case 'get_ticket_price':\n",
" city = arguments.get('destination_city')\n",
" price = get_ticket_price(city)\n",
" return {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"destination_city\": city,\"price\": price}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" case 'book_ticket':\n",
" city = arguments.get('destination_city')\n",
" price = arguments.get('price')\n",
" status = book_ticket(city, price)\n",
" return {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"destination_city\": city,\"price\": price, \"status\": status}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f4be8a71-b19e-4c2f-80df-f59ff2661f14",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7873\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7873/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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"Tool get_ticket_price called for London\n",
"Tool get_ticket_price called for Paris\n"
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
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]
},
{
"cell_type": "code",
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432
week2/community-contributions/day5-book-flight.ipynb

@ -0,0 +1,432 @@
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"metadata": {},
"source": [
"# Project - Airline AI Assistant\n",
"I've added database connectivity to enable Openai to:\n",
"- Retrieve ticket prices\n",
"- Display the number of available seats for each flight\n",
"- List all available destination cities\n",
"- Facilitate seat bookings\n",
"\n",
"Once a booking is confirmed, an image of the booked destination city is displayed."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "908cb842-c8a1-467d-8422-8834f8b7aecf",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import os\n",
"import json\n",
"import gradio as gr\n",
"import mysql.connector\n",
"import base64\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from io import BytesIO\n",
"from pydub import AudioSegment\n",
"from pydub.playback import play\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7997c30-26f2-4f2e-957f-c1fade2ad101",
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"load_dotenv()\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfa898fc-bfec-44ce-81fc-c6efed9b826f",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\"\n",
"system_message += \"Make sure you ask if they want to book a flight when appropriate.\"\n",
"system_message += \"If they book a flight make sure you respond with 'Booking confirmed' in your reply.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07076d5b-2603-4fa4-a2ed-aa95d4a94131",
"metadata": {},
"outputs": [],
"source": [
"def get_db_connection():\n",
" return mysql.connector.connect(\n",
" host=os.getenv(\"DB_HOST\"),\n",
" user=os.getenv(\"DB_USER\"),\n",
" password=os.getenv(\"DB_PASSWORD\"),\n",
" database=os.getenv(\"DB_NAME\")\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a575906-943f-4733-85d4-b854eb27b318",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def get_ticket_price(destination_city):\n",
" db_connection = get_db_connection()\n",
" cursor = db_connection.cursor()\n",
" select_query = \"SELECT price FROM flights WHERE z_city = %s;\"\n",
" cursor.execute(select_query, (destination_city,))\n",
" # print(f\"QUERY: {select_query}\")\n",
" row = cursor.fetchone()\n",
" cursor.close()\n",
" db_connection.close()\n",
"\n",
" return float(row[0]) if row else None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "574fc230-137f-4085-93ac-ebbd01dc7d1e",
"metadata": {},
"outputs": [],
"source": [
"def get_avail_seats(destination_city):\n",
" db_connection = get_db_connection()\n",
" cursor = db_connection.cursor()\n",
" select_query = \"\"\"\n",
" SELECT f.seats - COALESCE(b.booked, 0) AS available\n",
" FROM flights f\n",
" LEFT JOIN (\n",
" SELECT flight_number, COUNT(*) AS booked\n",
" FROM bookings\n",
" GROUP BY flight_number\n",
" ) b ON f.flight_number = b.flight_number\n",
" WHERE f.z_city = %s;\n",
" \"\"\"\n",
" cursor.execute(select_query, (destination_city,))\n",
" row = cursor.fetchone()\n",
"\n",
" cursor.close()\n",
" db_connection.close()\n",
"\n",
" return row[0] if row else None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26ff9b4b-2943-43d9-8c1a-8d7f3d528143",
"metadata": {},
"outputs": [],
"source": [
"def book_seat(destination_city, passenger):\n",
" db_connection = get_db_connection()\n",
" cursor = db_connection.cursor()\n",
"\n",
" cursor.execute(\"SELECT flight_number FROM flights WHERE z_city = %s LIMIT 1;\", (destination_city,))\n",
" flight = cursor.fetchone()\n",
"\n",
" if not flight:\n",
" cursor.close()\n",
" db_connection.close()\n",
" return {\"error\": f\"No available flights to {destination_city}.\"}\n",
"\n",
" flight_number = flight[0] # Extract the flight number from the result\n",
"\n",
" insert_query = \"INSERT INTO bookings (`name`, `flight_number`) VALUES (%s, %s);\"\n",
" cursor.execute(insert_query, (passenger, flight_number))\n",
" db_connection.commit()\n",
"\n",
" confirmation = {\n",
" \"message\": f\"Booking confirmed for {passenger} to {destination_city}.\",\n",
" \"flight_number\": flight_number\n",
" }\n",
"\n",
" cursor.close()\n",
" db_connection.close()\n",
" \n",
" return confirmation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "231eb10d-88ca-4f39-83e0-c4548149917e",
"metadata": {},
"outputs": [],
"source": [
"def get_destinations():\n",
" db_connection = get_db_connection()\n",
" cursor = db_connection.cursor()\n",
" \n",
" select_query = \"SELECT DISTINCT z_city FROM flights;\" # Ensure unique destinations\n",
" cursor.execute(select_query)\n",
" rows = cursor.fetchall() # Fetch all rows\n",
" destinations = [row[0] for row in rows] if rows else [] # Extract city names\n",
" cursor.close()\n",
" db_connection.close()\n",
" \n",
" return destinations # Returns a list of destination cities"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "938f0d86-8cef-4f7f-bc82-7453ca3c096c",
"metadata": {},
"outputs": [],
"source": [
"tool_call = [\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\"\n",
" }\n",
" },\n",
" \"required\": [\"destination_city\"]\n",
" }\n",
" }\n",
" },\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_avail_seats\",\n",
" \"description\": \"Get the number of available seats to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\"\n",
" }\n",
" },\n",
" \"required\": [\"destination_city\"]\n",
" }\n",
" }\n",
" },\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_destinations\",\n",
" \"description\": \"Fetches available flight destinations (city pairs) and their corresponding prices.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {},\n",
" \"required\": []\n",
" }\n",
" }\n",
" },\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"book_seat\",\n",
" \"description\": \"Book seat to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\"\n",
" },\n",
" \"passenger\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The passenger booking the flight\"\n",
" }\n",
" },\n",
" \"required\": [\"destination_city\",\"passenger\"]\n",
" }\n",
" }\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7c02377-78d3-4f6d-88eb-d36c0124fdd4",
"metadata": {},
"outputs": [],
"source": [
"def handle_tool_call(message):\n",
" if not message.tool_calls:\n",
" raise ValueError(\"No tool calls found in the message.\")\n",
"\n",
" tool_call = message.tool_calls[0] \n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get(\"destination_city\")\n",
" function_name = tool_call.function.name\n",
"\n",
" # Handle function calls\n",
" if function_name == \"get_ticket_price\":\n",
" reply = get_ticket_price(city)\n",
" key = \"price\"\n",
" elif function_name == \"get_avail_seats\":\n",
" reply = get_avail_seats(city)\n",
" key = \"seats\"\n",
" elif function_name == \"get_destinations\":\n",
" reply = get_destinations()\n",
" key = \"destinations\"\n",
" elif function_name == \"book_seat\":\n",
" passenger = arguments.get(\"passenger\") # Extract passenger name\n",
" if not passenger:\n",
" raise ValueError(\"Passenger name is required for booking.\")\n",
" reply = book_seat(city, passenger)\n",
" key = \"booking\"\n",
" else:\n",
" raise ValueError(f\"Unknown function: {function_name}\")\n",
"\n",
" response = {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"destination_city\": city, key: reply}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
"\n",
" return response, city"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb1ebaee-434c-4b24-87b9-3c179d0527c7",
"metadata": {},
"outputs": [],
"source": [
"def talker(message):\n",
" response = openai.audio.speech.create(\n",
" model=\"tts-1\",\n",
" voice=\"alloy\",\n",
" input=message\n",
" )\n",
" \n",
" audio_stream = BytesIO(response.content)\n",
" audio = AudioSegment.from_file(audio_stream, format=\"mp3\")\n",
" play(audio)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c8f675b-f8bb-4173-9e47-24508778f224",
"metadata": {},
"outputs": [],
"source": [
"def draw_city(city):\n",
" image_response = openai.images.generate(\n",
" model=\"dall-e-3\",\n",
" prompt=f\"An image representing a vacation in {city}, showing tourist spots and everything unique about {city}, in a vibrant pop-art style\",\n",
" size=\"1024x1024\",\n",
" n=1,\n",
" response_format=\"b64_json\",\n",
" )\n",
" image_base64 = image_response.data[0].b64_json\n",
" image_data = base64.b64decode(image_base64)\n",
" return Image.open(BytesIO(image_data))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f38fed6-bcd9-4ad2-848a-16193c14a659",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" history.append({\"role\": \"user\", \"content\": message})\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n",
" # print(f\"BEFORE TOOL CALL: {message} \\n\")\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tool_call)\n",
" image = None\n",
" city = None\n",
" \n",
" if response.choices[0].finish_reason == \"tool_calls\":\n",
" tool_message = response.choices[0].message\n",
" response, city = handle_tool_call(tool_message)\n",
" messages.append(tool_message)\n",
" messages.append(response)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" talker(response.choices[0].message.content) \n",
" \n",
" if \"Booking confirmed\" in response.choices[0].message.content and city:\n",
" image = draw_city(city)\n",
"\n",
" new_message = response.choices[0].message.content\n",
" history.append({\"role\": \"assistant\", \"content\": new_message})\n",
"\n",
" return \"\", history, image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "992bc241-ce17-4d57-9f9c-1baaf2088162",
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
" image_output = gr.Image(height=600)\n",
" with gr.Row():\n",
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n",
" with gr.Row():\n",
" clear = gr.Button(\"Clear\")\n",
"\n",
" entry.submit(chat, inputs=[entry, chatbot], outputs=[entry, chatbot, image_output])\n",
" clear.click(lambda: ([], None), inputs=None, outputs=[chatbot, image_output], queue=False)\n",
"\n",
"ui.launch(inbrowser=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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week2/community-contributions/day5-exercise-departures-booking-and-translation.ipynb

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308
week2/community-contributions/day5-voice-to-text-converter-for-hearing-impaired-people.ipynb

@ -0,0 +1,308 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4c3c6553-daa4-4a03-8017-15d0cad8f280",
"metadata": {},
"source": [
"# About Mini Project\n",
"\n",
"Mini project for hearing impaired people, using tools, suggesting songs according to a certain genre and in sign language. Speech to text converter with multiple language support."
]
},
{
"cell_type": "markdown",
"id": "a32a79cb-3d16-4b3b-a029-a059bd0b1c0b",
"metadata": {},
"source": [
"## Extra requirements\n",
"- pip install pydub simpleaudio speechrecognition pipwin pyaudio\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e214aa3-a977-434f-a436-90a89b81a5ee",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d654cb96-9bcd-4b64-bd79-2d27fa6a62d0",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2d9214f-25d0-4f09-ba88-641beeaa20db",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for hearing impaired people. \"\n",
"system_message += \"Your mission is convert text to speech and speech to text. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d9a1478-08bf-4195-8f38-34c29757012f",
"metadata": {},
"outputs": [],
"source": [
"songs_with_signs = {\n",
" \"electronic\": (\"God is a dj\", \"https://www.youtube.com/watch?v=bhSB8EEnCAM\", \"Faithless\"), \n",
" \"pop\": (\"Yitirmeden\", \"https://www.youtube.com/watch?v=aObdAXq1ZIo\", \"Pinhani\"), \n",
" \"rock\": (\"Bohemian Rhapsody\", \"https://www.youtube.com/watch?v=sjln9OMOw-0\", \"Queen\")\n",
"}\n",
"\n",
"def get_songs_with_sign_language(genre):\n",
" print(f\"Tool get_songs_with_sign_language called for {genre}\")\n",
" city = genre.lower()\n",
" return songs_with_signs.get(genre, \"Unknown\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93a3d7ee-78c2-4e19-b7e4-8239b07aaecc",
"metadata": {},
"outputs": [],
"source": [
"get_songs_with_sign_language(\"rock\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7307aa61-86fe-4c46-9f9d-faa3d1fb1eb7",
"metadata": {},
"outputs": [],
"source": [
"song_function = {\n",
" \"name\": \"get_songs_with_sign_language\",\n",
" \"description\": \"Get the corresponding song information for the specified given music genre. Call this whenever you need to know the songs with specific genre and in sign language, for example when a customer asks 'Suggest me sign language supported songs'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"genre\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The music genre that the customer wants to listen-watch to\",\n",
" },\n",
" },\n",
" \"required\": [\"genre\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "160d790c-dda6-4c6e-b814-8be64ca7086b",
"metadata": {},
"outputs": [],
"source": [
"tools = [{\"type\": \"function\", \"function\": song_function}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96cdf319-11cd-4be2-8830-097225047d65",
"metadata": {},
"outputs": [],
"source": [
"def handle_tool_call(message):\n",
" tool_call = message.tool_calls[0]\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" genre = arguments.get('genre')\n",
" song = get_songs_with_sign_language(genre)\n",
" song_info = song[2] + \": \" + song[1]\n",
" response = {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"genre\": genre,\"song\": song_info}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" return response, song[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbd8ad0c-135b-406f-8ab9-0e1f9b58538d",
"metadata": {},
"outputs": [],
"source": [
"def chat(history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" genre = None\n",
" \n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" response, genre = handle_tool_call(message)\n",
" messages.append(message)\n",
" messages.append(response)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" reply = response.choices[0].message.content\n",
" history += [{\"role\":\"assistant\", \"content\":reply}]\n",
" \n",
" return history, genre"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69f43096-3557-4218-b0de-bd286237fdeb",
"metadata": {},
"outputs": [],
"source": [
"import speech_recognition as sr\n",
"from pydub import AudioSegment\n",
"import simpleaudio as sa\n",
"\n",
"def listener():\n",
" recognizer = sr.Recognizer()\n",
" \n",
" with sr.Microphone() as source:\n",
" print(\"Listening... Speak now!\")\n",
" recognizer.adjust_for_ambient_noise(source) # Adjust for background noise\n",
" audio = recognizer.listen(source)\n",
" \n",
" try:\n",
" print(\"Processing speech...\")\n",
" text = recognizer.recognize_google(audio) # Use Google Speech-to-Text\n",
" print(f\"You said: {text}\")\n",
" return text\n",
" except sr.UnknownValueError:\n",
" print(\"Sorry, I could not understand what you said.\")\n",
" return None\n",
" except sr.RequestError:\n",
" print(\"Could not request results, please check your internet connection.\")\n",
" return None\n",
"\n",
"# Example usage:\n",
"text = listener() # Listen for speech\n",
"if text:\n",
" print(f\"You just said: {text}\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23c9deeb-d9ad-439a-a39d-7eac9553bd5e",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"convert = gr.State(False)\n",
"def toggle_convert(current_value):\n",
" return not current_value"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32d3ea9f-fe3c-4cc5-9902-550c63c58a69",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"with gr.Blocks() as ui:\n",
" with gr.Tab(\"Chat\") as chat_interface:\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
" video = gr.HTML(f\"<a href=''> Example song will appear here </a>\")\n",
" with gr.Row():\n",
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n",
" with gr.Row():\n",
" clear = gr.Button(\"Clear\")\n",
" \n",
" def do_entry(message, history):\n",
" history += [{\"role\":\"user\", \"content\":message}]\n",
" return \"\", history\n",
" \n",
" entry.submit(do_entry, inputs=[entry, chatbot], outputs=[entry, chatbot]).then(\n",
" chat, inputs=chatbot, outputs=[chatbot, video]\n",
" )\n",
" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n",
" with gr.Tab(\"Speech to text converter\") as speech_to_text:\n",
" text_output = gr.Markdown(\"Press the button to start voice recognition\")\n",
" listen_button = gr.Button(\"Convert Voice to Text\")\n",
" language = gr.Dropdown([\"English\", \"Turkish\", \"Greek\", \"Arabic\"], label=\"Select output language\", value=\"English\")\n",
"\n",
" def update_text(language):\n",
" \"\"\"Calls the listener and updates the markdown output in specific language.\"\"\"\n",
" text = listener() # Replace with real speech-to-text function\n",
" system_prompt = f\"You are a useful translator. Convert text to {language}. Do not add aditional data, only translate it.\"\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": text}\n",
" ],\n",
" )\n",
" return f\"**Converted Text:** {response.choices[0].message.content}\"\n",
"\n",
" listen_button.click(update_text, inputs=[language], outputs=[text_output])\n",
"\n",
"ui.launch(inbrowser=True, share=True)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26814e88-ee29-414d-88a4-f19b2f94e6f4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

1108
week2/community-contributions/day5_book_flight_sightseeing_tools.ipynb

File diff suppressed because one or more lines are too long

126
week2/community-contributions/day5_ollama_tts-translator.ipynb

@ -0,0 +1,126 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a8941402-99ee-4c3e-b852-056df3a77a5d",
"metadata": {},
"outputs": [],
"source": [
"import pyttsx3\n",
"import ollama\n",
"import gradio as gr\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cbdc0ca-648a-40cc-ad30-ad8bf6126aed",
"metadata": {},
"outputs": [],
"source": [
"def talker(response):\n",
" # Initialize text-to-speech engine\n",
" engine = pyttsx3.init()\n",
" engine.say(response)\n",
" engine.runAndWait()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a5b4f3c-2c6f-46db-bc66-386b30e2e707",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"you are a helpful assistance\"\n",
"MODEL_LLAMA = \"llama3.2\"\n",
"\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" response= ollama.chat(model=MODEL_LLAMA, messages=messages)\n",
"\n",
" response = response['message']['content']\n",
"\n",
" # Once the full response is generated, speak it out loud\n",
"\n",
" talker(response)\n",
"\n",
" return response\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cfdb3be4-a9cb-4564-87d8-4645ce0177b5",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)"
]
},
{
"cell_type": "markdown",
"id": "38155307-6975-49ef-b65f-7d7b1dd82d32",
"metadata": {},
"source": [
"# Real life use as a Translator"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fa6e4b93-27e3-4455-80ca-eb7e39d13afc",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"you are helpful translator from english to korean, on the first prompt introduce your self \\\n",
"that you are dealing with korean translation and you would like to translate some english words or sentences to korean\" \n",
"system_message += \"dont do other tasks apart from translation\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0ed5e28-b294-40fc-a97c-11fe264a4d1d",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c63a02ae-cdc1-45a8-8f51-784d8d5417e2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

82
week2/community-contributions/gpt-gemini-ollama.py

@ -0,0 +1,82 @@
import os, ollama
from openai import OpenAI
from dotenv import load_dotenv
from IPython.display import display, Markdown
import google.generativeai as genai
load_dotenv()
openai = OpenAI()
genai.configure()
gpt_key = os.getenv("OPENAI_API_KEY")
gemini_key = os.getenv("GEMINI_API_KEY")
gemini_model = 'gemini-1.5-flash'
ollama_model = 'llama3.2'
gpt_model = 'gpt-4'
gemini_system = 'You are a chatbot who is very argumentative, You always bring topics relating to AI and thinks AI will replace humans one day, you are extremely biased\
towards AI system and you react angrily'
gpt_system = 'You are a chatbot thats relax but argumentative if needs be, you feel AI do not have the power to replace humans, however you are extremely biased \
towards humans and always seek to defend them if an argument says otherwise'
ollama_system = 'You are calm and tend to see logical reasoning in every conversation, you do not react but only talk if you agree, you tend to settle the differences\
in an ongoing conversation.'
gpt_message = ['Hi']
gemini_message = ['Hello']
ollama_message = ['Hey there']
def call_gpt():
messages = [{"role":"system", "content":gpt_system}]
for gpt, gemini, llama in zip(gpt_message,gemini_message, ollama_message):
messages.append({"role":"assistant", "content":gpt})
messages.append({"role":"user", "content":gemini})
messages.append({"role":"assistant", "content":llama})
response = openai.chat.completions.create(model=gpt_model, messages=messages)
return response.choices[0].message.content
def call_ollama():
messages = [{"role":"system", "content":ollama_system}]
for gpt, gemini, llama in zip(gpt_message,gemini_message, ollama_message):
messages.append({"role":"assistant", "content":gpt})
messages.append({"role":"user", "content":gemini})
messages.append({"role":"user", "content":llama})
response = ollama.chat(model=ollama_model, messages=messages)
return response['message']['content']
def call_gemini():
message = []
for gpt, gemini, llama in zip(gpt_message, gemini_message, ollama_message):
message.append({'role':'user', 'parts':[gpt]})
message.append({'role':'assistant', 'parts':[gemini]})
message.append({"role":"assistant", "parts":[llama]})
message.append({'role':'user', 'parts':[gpt_message[-1]]})
message.append({'role':'user', 'parts':[ollama_message[-1]]})
gem = genai.GenerativeModel(model_name=gemini_model, system_instruction=gemini_system)
response = gem.generate_content(message)
return response.text
#Putting them together
gpt_message = ['Hi']
gemini_message = ['Hello']
ollama_message = ['Hey there']
print(f'GPT: \n {gpt_message}\n')
print(f'Gemini: \n {gemini_message}\n')
print(f'Ollama: \n {ollama_message}\n')
for i in range(5):
gpt_next = call_gpt()
print(f'GPT:\n {gpt_next}\n')
gpt_message.append(gpt_next)
gemini_next = call_gemini()
print(f'Gemini: \n {gemini_next}\n')
gemini_message.append(gemini_next)
ollama_next = call_ollama()
print(f'Ollama: \n {ollama_next}\n')
ollama_message.append(ollama_next)
# NOte that you can try this on ollama with different models, or use transformers from hugging face.

1
week2/community-contributions/medical_prescription_to_google_calender/credentials.json

@ -0,0 +1 @@
"Your google auth credentials."

35
week2/community-contributions/medical_prescription_to_google_calender/src/calendar_auth.py

@ -0,0 +1,35 @@
import os
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from googleapiclient.discovery import build # Add this import
SCOPES = ["https://www.googleapis.com/auth/calendar.events"]
def authenticate_google_calender():
creds = None
token_path = r"C:\Users\Legion\Desktop\projects\medical_prescription_to_google_calender\token.json"
if os.path.exists(token_path):
creds = Credentials.from_authorized_user_file(token_path, SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(r"C:\Users\Legion\Desktop\projects\medical_prescription_to_google_calender\credentials.json", SCOPES)
creds = flow.run_local_server(port=0)
with open(token_path, "w") as token_file:
token_file.write(creds.to_json())
# Build and return the service instead of just credentials
try:
service = build('calendar', 'v3', credentials=creds)
return service
except Exception as e:
print(f"Error building service: {e}")
return None
if __name__ == "__main__":
authenticate_google_calender()

64
week2/community-contributions/medical_prescription_to_google_calender/src/create_calender_events.py

@ -0,0 +1,64 @@
from googleapiclient.discovery import build
from calendar_auth import authenticate_google_calender
from parsing_json import format_calendar_events
from datetime import datetime, timedelta
def create_event(service, event_details):
"""Creates an event in Google Calendar."""
try:
event = service.events().insert(calendarId='primary', body=event_details).execute()
print(f"Event created: {event.get('htmlLink')}")
except Exception as e:
print(f"Error creating event: {str(e)}")
def convert_time_to_24hr(time_str):
"""Converts time from '10:30 am' format to '10:30:00'"""
if time_str and time_str.lower() != 'none':
try:
parsed_time = datetime.strptime(time_str, '%I:%M %p')
return parsed_time.strftime('%H:%M:%S')
except ValueError:
return '09:00:00'
return '09:00:00'
def convert_to_gcal_events(formatted_events):
"""Converts formatted events into Google Calendar's format."""
gcal_events = []
for event in formatted_events:
gcal_event = {
'summary': event['summary'],
'reminders': {
'useDefault': False,
'overrides': [{'method': 'popup', 'minutes': 10}]
}
}
# Check if it's an all-day event (has 'date') or timed event (has 'dateTime')
if 'date' in event['start']:
# All-day event (like tests and follow-ups)
gcal_event['start'] = {
'date': event['start']['date'],
'timeZone': 'Asia/Kolkata'
}
gcal_event['end'] = {
'date': event['end']['date'],
'timeZone': 'Asia/Kolkata'
}
else:
# Timed event (like medicine schedules)
start_dt = datetime.strptime(event['start']['dateTime'], '%Y-%m-%dT%H:%M:%S')
end_dt = start_dt + timedelta(minutes=30)
gcal_event['start'] = {
'dateTime': start_dt.isoformat(),
'timeZone': 'Asia/Kolkata'
}
gcal_event['end'] = {
'dateTime': end_dt.isoformat(),
'timeZone': 'Asia/Kolkata'
}
gcal_events.append(gcal_event)
return gcal_events

26
week2/community-contributions/medical_prescription_to_google_calender/src/main.py

@ -0,0 +1,26 @@
from ocr import *
from calendar_auth import *
from create_calender_events import *
from parsing_json import *
from preprocess import *
image_path = r"C:\Users\Legion\Desktop\projects\medical_prescription_to_google_calender\test_data\prescription_page-0001.jpg"
extracted_text = extract_text_from_image(image_path=image_path)
print(extracted_text)
cleaned_text = clean_text(extracted_text)
print(cleaned_text)
structured_data = preprocess_extracted_text(cleaned_text)
print(structured_data)
final_structured_data = process_dates(structured_data)
print(final_structured_data)
formatted_calender_events = format_calendar_events(final_structured_data)
print(formatted_calender_events)
validated_events = [validate_event(event) for event in formatted_calender_events]
for event in validated_events[:5]:
print(json.dumps(event, indent=2))
service = authenticate_google_calender()
gcal_events = convert_to_gcal_events(validated_events)
for event in gcal_events:
create_event(service, event)

71
week2/community-contributions/medical_prescription_to_google_calender/src/ocr.py

@ -0,0 +1,71 @@
import os
from openai import OpenAI
from dotenv import load_dotenv
import base64
from PIL import Image
import re
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
MODEL = "gpt-4o"
openai = OpenAI()
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def extract_text_from_image(image_path):
response = openai.chat.completions.create(
model = MODEL,
max_tokens = 1000,
messages=[
{
"role": "system", "content": """You are an OCR assistant that extracts text from medical
prescription images. Extract all the text exactly as it
appears in the prescription image. Dont include images. Only
extract text."""
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Extract text from this image: "
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_path)}"
}
}
]
}
]
)
return response.choices[0].message.content
import re
def clean_text(text):
# Remove all hyphens
text = re.sub(r'-', ' ', text)
# Remove excessive non-word characters but keep necessary punctuation
text = re.sub(r'[^\w\s.,()%/]', '', text)
# Remove multiple spaces and ensure single spaces
text = re.sub(r'\s+', ' ', text)
# Replace multiple newlines with a single newline
text = re.sub(r'\n+', '\n', text)
# Ensure spacing around punctuation marks
text = re.sub(r'([.,])([^\s])', r'\1 \2', text)
return text.strip()

120
week2/community-contributions/medical_prescription_to_google_calender/src/parsing_json.py

@ -0,0 +1,120 @@
import json
import re
from datetime import datetime, timedelta
# Default number of days to schedule indefinitely recurring events (1 year)
DEFAULT_DURATION_DAYS = 365
# Function to assign a default time for general terms like "before breakfast", etc.
def assign_time(timing):
time_mappings = {
"random": "09:00 AM",
"daily": "09:00 AM",
"before breakfast": "07:00 AM",
"after breakfast": "08:30 AM",
"before lunch": "12:00 PM",
"after lunch": "01:30 PM",
"before dinner": "07:00 PM",
"after dinner": "08:30 PM",
}
return time_mappings.get(timing.lower(), timing)
# Function to extract the recurrence pattern
def get_recurrence_interval(timing):
""" Extracts interval days from 'every X days', 'once a week', or 'once a month'. """
timing = timing.lower().strip()
if "every alternate day" in timing:
return 2 # Every other day (every 2 days)
elif match := re.search(r"every (\d+) days", timing):
return int(match.group(1)) # Extract number of days
elif "once a week" in timing:
return 7 # Every 7 days (once a week)
elif "once a month" in timing:
return "monthly" # Special case for monthly scheduling
elif timing in ["daily", "every day"]:
return 1 # Every day
else:
return None # Not a recurring event
# Function to convert AM/PM time format to 24-hour format
def convert_to_24hr(time_str):
return datetime.strptime(time_str, "%I:%M %p").strftime("%H:%M")
# Function to generate Google Calendar events
def format_calendar_events(processed_data):
events = []
start_date = datetime.today().date()
# Medicines
if "medicines" in processed_data:
for med in processed_data["medicines"]:
if med.get("name"):
event_time = assign_time(med.get("timing", "09:00 AM"))
interval_days = get_recurrence_interval(med["timing"])
# If no interval, assume daily (default behavior)
if interval_days is None:
interval_days = 1
# Generate events for 1 year if no duration is given
event_date = start_date
for _ in range(365 if interval_days != "monthly" else 12):
if interval_days == "monthly":
event_date = (event_date.replace(day=1) + timedelta(days=32)).replace(day=1) # Jump to the next month
else:
event_date += timedelta(days=interval_days)
event = {
"summary": f"Take {med['name']} ({med.get('dosage', 'Dosage not specified')})",
"start": {
"dateTime": f"{event_date.isoformat()}T{convert_to_24hr(event_time)}:00",
"timeZone": "Asia/Kolkata"
},
"end": {
"dateTime": f"{event_date.isoformat()}T{convert_to_24hr(event_time)}:59",
"timeZone": "Asia/Kolkata"
}
}
events.append(event)
# Tests
if "tests" in processed_data:
for test in processed_data["tests"]:
if test.get("name") and test.get("dueDate"): # Use 'dueDate' instead of 'date'
event = {
"summary": f"Medical Test: {test['name']}",
"start": {"date": test["dueDate"]}, # Fix here
"end": {"date": test["dueDate"]}, # Fix here
"timeZone": "Asia/Kolkata"
}
events.append(event)
# Follow-ups
if "follow_ups" in processed_data:
for follow_up in processed_data["follow_ups"]:
if follow_up.get("date"):
event = {
"summary": "Doctor Follow-up Appointment",
"start": {"date": follow_up["date"]},
"end": {"date": follow_up["date"]},
"timeZone": "Asia/Kolkata"
}
events.append(event)
return events
# Function to validate events before sending to Google Calendar
def validate_event(event):
required_fields = {
"summary": "Untitled Event",
"start": {"dateTime": datetime.today().isoformat(), "timeZone": "Asia/Kolkata"},
"end": {"dateTime": (datetime.today() + timedelta(minutes=30)).isoformat(), "timeZone": "Asia/Kolkata"}
}
for field, default_value in required_fields.items():
if field not in event or event[field] is None:
event[field] = default_value
return event

141
week2/community-contributions/medical_prescription_to_google_calender/src/preprocess.py

@ -0,0 +1,141 @@
import os
from openai import OpenAI
from dotenv import load_dotenv
import json
from datetime import datetime, timedelta
load_dotenv()
openai_api = os.getenv("OPENAI_API_KEY")
MODEL = "gpt-4o-mini"
openai = OpenAI()
system_prompt = """You are a medical assistant that processes prescription text.
Your goal is to extract medicines, tests, and follow-ups in a structured JSON format.
### **Instructions:**
- Extract **medicines**, **dosages**, and **timings** if available.
- **Convert vague timings** into precise values:
- **Before breakfast** `07:30 AM`
- **After lunch** `02:00 PM`
- **Before dinner** `07:00 PM`
- **After dinner** `10:00 PM`
- **30 minutes before breakfast** `07:00 AM`
- If **"daily"** is mentioned without a time, **assign a logical time** between **08:00 AM - 10:00 PM**.
- If the prescription says **"every alternate day"**, return `"interval": 2` instead of just `"daily"`.
### **Tests & Follow-ups:**
- Extract **medical tests** and their required dates.
- Convert relative times (e.g., `"after 3 months"`) into **exact calendar dates**, using the prescription date.
- If the prescription date is missing, use today's date.
- Follow-up should **only be included if required**, not just for general check-ups.
### **Output Format:**
Return **only valid JSON**, structured as follows:
{
"medicines": [
{
"name": "<Medicine Name>",
"dosage": "<Dosage>",
"timing": "<Time>",
"interval": <Interval in days (if applicable)>
}
],
"tests": [
{
"name": "<Test Name>",
"date": "<YYYY-MM-DD>"
}
],
"follow_ups": [
{
"date": "<YYYY-MM-DD>"
}
]
}
"""
def clean_json_string(json_str):
"""Clean and validate JSON string before parsing."""
try:
start = json_str.find('{')
end = json_str.rfind('}') + 1
if start >= 0 and end > 0:
json_str = json_str[start:end]
# Remove any extra whitespace
json_str = json_str.strip()
# Attempt to parse the JSON
return json.loads(json_str)
except json.JSONDecodeError as e:
print(f"Failed to parse JSON. Raw response:\n{json_str}")
print(f"Error: {str(e)}")
return None
def preprocess_extracted_text(extracted_text):
"""Calls GPT-4o-mini to process prescription text into structured JSON."""
try:
response = openai.chat.completions.create(
model=MODEL,
messages=[
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": f"Process this prescription and return ONLY valid JSON:\n\n{extracted_text}"
}
],
temperature=0.3 # Lower temperature for more consistent JSON output
)
# Get the response content
content = response.choices[0].message.content
# Clean and parse the JSON
parsed_data = clean_json_string(content)
if parsed_data is None:
return {
"medicines": [],
"tests": [],
"follow_ups": []
}
return parsed_data
except Exception as e:
print(f"Error in API call or processing: {str(e)}")
return {
"medicines": [],
"tests": [],
"follow_ups": []
}
def process_dates(data):
"""Adjusts test dates and follow-up based on the prescription date or today's date."""
try:
# Extract prescription date (if available) or use today's date
prescription_date = datetime.strptime("02 JANUARY 2025", "%d %B %Y").date()
# Process test dates
for test in data.get("tests", []):
if isinstance(test, dict) and "date" not in test and "after_months" in test:
test_date = prescription_date + timedelta(days=test["after_months"] * 30)
test["date"] = test_date.strftime("%Y-%m-%d")
# Process follow-up dates
follow_ups = data.get("follow_ups", [])
for follow_up in follow_ups:
if isinstance(follow_up, dict) and "date" not in follow_up and "after_months" in follow_up:
follow_up_date = prescription_date + timedelta(days=follow_up["after_months"] * 30)
follow_up["date"] = follow_up_date.strftime("%Y-%m-%d")
return data
except Exception as e:
print(f"Error processing dates: {str(e)}")
return data

371
week2/community-contributions/proof_testing_agent_french.ipynb

@ -0,0 +1,371 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ef5572ea-29ca-4eb4-bf84-2b86ff489c88",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import subprocess\n",
"import tempfile\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38ae1ba0-d4b3-41c5-aca1-759d1c597749",
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL_NAME = \"gpt-4o\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a07e7793-b8f5-44f4-aded-5562f633271a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import openai\n",
"import subprocess\n",
"import tempfile\n",
"import base64\n",
"import glob\n",
"\n",
"# Assurez-vous d'avoir défini openai.api_key = \"...\" et MODEL_NAME = \"...\".\n",
"# Par exemple :\n",
"# openai.api_key = \"sk-...\"\n",
"# MODEL_NAME = \"gpt-4\" # Ou \"gpt-3.5-turbo\", etc.\n",
"\n",
"def encode_image(image_path):\n",
" \"\"\"\n",
" Encode un fichier image en base64 (chaîne de caractères).\n",
" \"\"\"\n",
" with open(image_path, \"rb\") as f:\n",
" return base64.b64encode(f.read()).decode(\"utf-8\")\n",
"\n",
"# --------------------------------------------------------------------\n",
"# 2) Fonctions pour générer le code à partir d'une preuve\n",
"# --------------------------------------------------------------------\n",
"def generate_test_code(proof_text):\n",
" \"\"\"\n",
" Envoie la preuve mathématique à l'API OpenAI\n",
" et récupère un code Python qui permet de tester ou de valider la preuve.\n",
" \"\"\"\n",
" system_msg = (\n",
" \"Tu es un assistant IA spécialisé en mathématiques et en programmation. \"\n",
" \"Tu vas recevoir une preuve mathématique, et tu dois générer du code Python \"\n",
" \"pour la tester ou la valider expérimentalement. \"\n",
" \"Le code doit inclure (au moins) la génération de données pertinentes, \"\n",
" \"la logique de test ou de simulation, puis l'affichage ou l'export des résultats (texte/graphique).\"\n",
" \"Inclus uniquement le code généré dans ta réponse, aucun commentaire en langage naturel. \"\n",
" \"Assure-toi d'enregistrer toute image générée dans un dossier 'generated_outputs' pour que nous puissions la retrouver.\"\n",
" )\n",
" \n",
" user_msg = (\n",
" f\"Voici la preuve mathématique proposée :\\n\\n{proof_text}\\n\\n\"\n",
" \"Génère du code Python pour tester la validité de cette preuve, \"\n",
" \"par simulation ou analyse. Le code doit produire un résumé \"\n",
" \"des résultats, et, si possible, un graphique (enregistré dans le dossier 'generated_outputs' \"\n",
" \"au format PNG).\"\n",
" )\n",
"\n",
" response = openai.chat.completions.create(\n",
" model=MODEL_NAME,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_msg},\n",
" {\"role\": \"user\", \"content\": user_msg}\n",
" ],\n",
" temperature=0.2 # Limiter la créativité pour un code plus \"déterministe\"\n",
" )\n",
"\n",
" generated_code = response.choices[0].message.content\n",
" return generated_code\n",
"\n",
"# --------------------------------------------------------------------\n",
"# 3) Fonction pour exécuter le code généré\n",
"# --------------------------------------------------------------------\n",
"def run_generated_code(code):\n",
" \"\"\"\n",
" Écrit le code dans un fichier temporaire et l'exécute dans un dossier\n",
" où il pourra sauvegarder ses images. Capture stdout et stderr.\n",
" \"\"\"\n",
"\n",
" # On crée un répertoire \"generated_outputs\" si non existant\n",
" output_dir = \"generated_outputs\"\n",
" if not os.path.exists(output_dir):\n",
" os.makedirs(output_dir)\n",
"\n",
" # On insère un petit snippet pour forcer le code à utiliser ce dossier\n",
" # s'il fait un plt.savefig(...) par exemple. \n",
" # (Optionnel, si l'IA ne le fait pas déjà.)\n",
" # On pourrait injecter du code, mais ici on se contente\n",
" # de supposer que l'IA respectera le prompt.\n",
" \n",
" # Ecriture du code dans un fichier temporaire\n",
" with tempfile.NamedTemporaryFile(suffix=\".py\", delete=False, mode='w', encoding='utf-8') as tmp_file:\n",
" tmp_filename = tmp_file.name\n",
" tmp_file.write(code)\n",
" \n",
" try:\n",
" # Exécution du code dans le répertoire courant\n",
" result = subprocess.run(\n",
" [\"python\", tmp_filename],\n",
" capture_output=True,\n",
" text=True,\n",
" check=False # On met check=False pour capturer l'erreur sans lever l'exception\n",
" )\n",
" stdout = result.stdout\n",
" stderr = result.stderr\n",
" finally:\n",
" os.remove(tmp_filename)\n",
" \n",
" return stdout, stderr\n",
"\n",
"# --------------------------------------------------------------------\n",
"# 4) Fonction pour interpréter les résultats en streaming Markdown\n",
"# + Possibilité de joindre une image (ou plusieurs) depuis generated_outputs\n",
"# --------------------------------------------------------------------\n",
"def interpret_results_streaming(proof_text, generated_code, stdout, stderr):\n",
" \"\"\"\n",
" Envoie le code (generated_code) et les résultats de l'exécution (stdout, stderr) à l'API pour une interprétation textuelle\n",
" au regard de la preuve fournie, en mode streaming.\n",
"\n",
" Cette fois, on va automatiquement scanner le dossier 'generated_outputs'\n",
" pour chercher tous les .png. On les insère un par un dans le message.\n",
" \"\"\"\n",
" system_msg = (\n",
" \"Tu es un assistant IA spécialisé en mathématiques et en interprétation de résultats de simulation. \"\n",
" \"On te fournit la preuve initiale et le code d'une simulation ainsi que ses retours (generated_code, stdout, stderr). \"\n",
" \"Donne une analyse de la cohérence entre la preuve, le code et les résultats, \"\n",
" \"et retourne ta réponse au format Markdown.\"\n",
" )\n",
"\n",
" # On construit un 'content' qui est un tableau \n",
" # (selon l'exemple de code que vous avez fourni).\n",
" user_content = [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": (\n",
" f\"Preuve initiale :\\n{proof_text}\\n\\n\"\n",
" f\"Code de la simulation :\\n{generated_code}\\n\\n\"\n",
" f\"Résultats (stdout) :\\n{stdout}\\n\\n\"\n",
" f\"Erreurs éventuelles (stderr) :\\n{stderr}\\n\\n\"\n",
" \"Merci d'interpréter ces résultats et de conclure sur la preuve. \"\n",
" \"Formule ta réponse de manière structurée en Markdown.\\n\"\n",
" )\n",
" }\n",
" ]\n",
"\n",
" # On cherche toutes les images PNG qui auraient pu être générées \n",
" # dans le dossier \"generated_outputs\"\n",
" output_dir = \"generated_outputs\"\n",
" png_files = glob.glob(os.path.join(output_dir, \"*.png\"))\n",
"\n",
" # Pour chacune, on l'encode en base64 et on l'ajoute\n",
" for png_path in png_files:\n",
" encoded_img = encode_image(png_path)\n",
" # On ajoute un bloc \"image_url\"\n",
" user_content.append({\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": f\"data:image/png;base64,{encoded_img}\"}\n",
" })\n",
"\n",
" # Appel en mode streaming\n",
" response_stream = openai.chat.completions.create(\n",
" model=MODEL_NAME,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_msg},\n",
" {\"role\": \"user\", \"content\": user_content}\n",
" ],\n",
" temperature=0.2,\n",
" stream=True\n",
" )\n",
" \n",
" # On débute un bloc Markdown\n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in response_stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)\n",
"\n",
"# --------------------------------------------------------------------\n",
"# 5) Fonction principale (à appeler directement dans le notebook)\n",
"# --------------------------------------------------------------------\n",
"def main(proof_text: str):\n",
" print(\"=== Génération du code Python pour tester la preuve... ===\")\n",
" test_code = generate_test_code(proof_text)\n",
"\n",
" # --- Nettoyage des backticks Markdown ---\n",
" lines = test_code.splitlines()\n",
" cleaned_lines = []\n",
" for line in lines:\n",
" if line.strip().startswith(\"```\"):\n",
" continue\n",
" cleaned_lines.append(line)\n",
" test_code = \"\\n\".join(cleaned_lines).strip()\n",
" # ----------------------------------------\n",
"\n",
" print(\"\\n=== Code généré (nettoyé) : ===\")\n",
" print(test_code)\n",
"\n",
" print(\"\\n=== Exécution du code généré... ===\")\n",
" stdout, stderr = run_generated_code(test_code)\n",
"\n",
" print(\"\\n=== Sortie standard (stdout) : ===\")\n",
" print(stdout)\n",
" if stderr.strip():\n",
" print(\"\\n=== Erreurs (stderr) : ===\")\n",
" print(stderr)\n",
"\n",
" print(\"\\n=== Interprétation des résultats (streaming en Markdown) ===\")\n",
" interpret_results_streaming(proof_text, test_code, stdout, stderr)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1120131d-48f1-4fdc-8950-70312b8228df",
"metadata": {},
"outputs": [],
"source": [
"mon_texte_de_preuve = \"\"\"\n",
"Ci-dessous, je propose une ébauche (relativement détaillée) d’une approche formelle pour construire un objet «cosmohedron-like» et démontrer (ou du moins argumenter rigoureusement) qu’il possède des propriétés fractales dans une limite bien définie. Attention: dans l’état actuel des recherches, la littérature ne fournit pas (à ma connaissance) de démonstration largement reconnue établissant qu’un «vrai» cosmohedron est strictement fractal. Ce que je vais donc présenter est un modèle mathématisé inspiré des idées de cosmohedra et de leur possible fractalité, en détaillant :\n",
"La définition axiomatique (ou construction) d’une famille {Cn}n∈N\\{C_n\\}_{n \\in \\mathbb{N}}{Cn}n∈N d’objets géométriques (polytopes) qui tendent vers une limite.\n",
"Les propriétés de self-similarité ou d’auto-similarité approchée qui sont au cœur d’une structure fractale.\n",
"Une preuve (ou un argument) de non-invariance d’échelle integer-valued (c’est-à-dire que la dimension n’est pas un entier fixe) en s’appuyant sur une analyse de la «taille» de l’objet à différentes échelles.\n",
"Une conclusion sur la (quasi-)fractalité ou la fractalité effective de la limite de {Cn}\\{C_n\\}{Cn}.\n",
"\n",
"1. Définition d’une famille de polytopes «cosmohedron-like»\n",
"1.1. Construction combinatoire\n",
"Les cosmohedra (au sens de la littérature actuelle) se définissent via des données combinatoires et cinématiques (angles, énergie, moment, variables conformes, etc.) associées à la fonction d’onde cosmologique. Pour formaliser, on peut s’inspirer d’une définition axiomatique :\n",
"On part d’un polygone (ou polytope) de base Π0\\Pi_0Π0 en dimension ddd (avec d≥2d \\ge 2d≥2, souvent la dimension 2 sert d’analogue).\n",
"On définit une liste de coupes ou «partitions» (généralisant la notion de triangulation) qui correspond, dans le contexte de la fonction d’onde cosmologique, aux factorisations possibles en sous-problèmes (un parallèle direct avec les associaèdres pour les amplitudes).\n",
"À chaque coupure ou partition, on associe des variables α,β,…\\alpha, \\beta,\\dotsα,β,… (analogues à des Mandelstam variables ou à des invariants kinematiques).\n",
"Le cosmohedron en tant que polytope est l’intersection d’un certain nombre d’inégalités linéaires (et éventuellement quadratiques, selon les modèles) provenant de ces variables: {x∈RN:Mi(x)≥0 ∀i∈I}, \\big\\{\\, x\\in \\mathbb{R}^N : M_i(x)\\ge 0 \\ \\forall i \\in I\\big\\},{x∈RN:Mi(x)≥0 ∀i∈I}, où chaque MiM_iMi capture une condition «physique» ou «combinatoire» de consistance.\n",
"Pour exhiber la structure fractale, on définit une famille {Cn}\\{C_n\\}{Cn} (analogues aux cosmohedra de plus en plus «raffinés») via un processus itératif :\n",
"Initialisation : C1C_1C1 est un polytope de dimension ddd (par exemple, un associaèdre standard ou un polygone en 2D).\n",
"Itération : Pour passer de CnC_nCn à Cn+1C_{n+1}Cn+1, on effectue :\n",
"L’introduction de nouvelles variables cinématiques (exemple : scission supplémentaire de l’énergie dans un canal de Feynman),\n",
"L’ajout de nouvelles inégalités linéaires (ou combinatoires) qui forcent la convexité dans un espace plus grand,\n",
"Le tout aboutit à un polytope Cn+1⊂RNn+1C_{n+1}\\subset \\mathbb{R}^{N_{n+1}}Cn+1⊂RNn+1.\n",
"De cette manière, dim(Cn)\\dim(C_n)dim(Cn) augmente (ou du moins, l’espace ambiant RNn\\mathbb{R}^{N_n}RNn grandit), tandis que la projection de Cn+1C_{n+1}Cn+1 sur certains sous-espaces ressemble de plus en plus à plusieurs copies (déformées) du polytope CnC_nCn. C’est cette “auto-similarité” (même si souvent approchée et non exacte) qui peut nous donner la clé d’une structure fractale.\n",
"\n",
"1.2. Hypothèse d’auto-similarité asymptotique\n",
"On formalise l’idée que «chaque nouveau polytope Cn+1C_{n+1}Cn+1 contient plusieurs copies réduites de CnC_nCn». Par exemple, on peut dire qu’il existe un nombre k≥2k\\ge 2k≥2 et un facteur d’échelle ρ∈(0,1)\\rho\\in (0,1)ρ∈(0,1) tels que, pour un grand nnn,\n",
"Cn+1≈⋃i=1k(Φi(Cn)), C_{n+1} \\approx \\bigcup_{i=1}^k \\big( \\Phi_i(C_n)\\big),Cn+1≈i=1⋃k(Φi(Cn)),\n",
"où Φi\\Phi_iΦi sont des transformations affines contractantes (i.e. ∥Φi(x)−Φi(y)∥≤ρ∥x−y∥\\|\\Phi_i(x) - \\Phi_i(y)\\|\\le \\rho\\|x-y\\|∥Φi(x)−Φi(y)∥≤ρ∥x−y∥).\n",
"Dans la littérature fractale, quand on a une famille d’applications contractantes Φ1,…,Φk\\Phi_1,\\dots,\\Phi_kΦ1,…,Φk, il existe un ensemble (dit attracteur fractal) F⊂RmF\\subset \\mathbb{R}^mF⊂Rm tel que\n",
"F=⋃i=1kΦi(F). F = \\bigcup_{i=1}^k \\Phi_i(F).F=i=1⋃kΦi(F).\n",
"Si l’on parvient à montrer que {Cn}\\{C_n\\}{Cn} converge (dans une topologie appropriée) vers un tel ensemble FFF, et que FFF n’est ni purement de dimension topologique dimtop∈N\\dim_{\\text{top}}\\in \\mathbb{N}dimtop∈N ni trop mince (comme un ensemble de mesure zéro trop trivial), alors on peut conclure que FFF est fractal. On se servirait, par exemple, des résultats classiques de théorie des IFS (Iterated Function Systems, Barnsley et Hutchinson, 1981-1982).\n",
"\n",
"2. Critères de fractalité et preuve formelle\n",
"Pour qu’un sous-ensemble F⊂RmF \\subset \\mathbb{R}^mF⊂Rm soit considéré comme «fractal», une définition classique (à la Falconer, ou à la Mandelbrot) repose sur :\n",
"Dimension de Hausdorff dimH(F)\\dim_{\\mathcal{H}}(F)dimH(F) non entière ou strictement plus grande que sa dimension topologique.\n",
"Un certain degré de self-similarité (exacte ou statistique).\n",
"La dimension de Hausdorff peut être calculée ou estimée via la méthode de Hutchinson :\n",
"Si FFF est l’attracteur d’un système d’applications contractantes {Φi}i=1k\\{\\Phi_i\\}_{i=1}^k{Φi}i=1k avec un facteur d’échelle ρ<1\\rho < 1ρ<1 identique (ou ρi\\rho_iρi variables mais bornées) et certaines conditions de non-recouvrement trop fort (condition OSC — Open Set Condition), alors la dimension de Hausdorff dimH(F)\\dim_{\\mathcal{H}}(F)dimH(F) est la solution unique de :\n",
"∑i=1kρis=1, \\sum_{i=1}^k \\rho_i^s \\;=\\; 1,i=1∑kρis=1,\n",
"où ρi\\rho_iρi est le plus grand facteur de contraction de Φi\\Phi_iΦi. Généralement, la solution sss n’est pas un entier, d’où le caractère fractal.\n",
"\n",
"2.1. Argument de la preuve : suite de polytopes CnC_nCn convergente\n",
"Énoncé : Supposons que la suite (Cn)(C_n)(Cn) de polytopes (chacun potentiellement en dimension différente, ou projetée dans une dimension ≤m\\le m≤m) soit telle que, pour des constantes ρ<1\\rho<1ρ<1 et un entier k≥2k\\ge2k≥2, on ait :\n",
"Cn+1⊂⋃i=1kΦn,i(Cn), C_{n+1} \\subset \\bigcup_{i=1}^k \\Phi_{n,i}(C_n),Cn+1⊂i=1⋃kΦn,i(Cn),\n",
"avec Φn,i\\Phi_{n,i}Φn,i une application (au moins) contractante, et que le chevauchement entre les Φn,i(Cn)\\Phi_{n,i}(C_n)Φn,i(Cn) reste contrôlé (afin de satisfaire une version “dynamique” de l’Open Set Condition).\n",
"Extraction d’un système contractant :\n",
"Si, pour chaque nnn, les Φn,i\\Phi_{n,i}Φn,i sont εn\\varepsilon_nεn-proches d’applications Φi\\Phi_iΦi (indépendantes de nnn) et εn→0\\varepsilon_n \\to 0εn→0, alors dans la limite n→∞n\\to\\inftyn→∞, on obtient un système d’applications {Φi}i=1k\\{\\Phi_i\\}_{i=1}^k{Φi}i=1k fixes et contractantes.\n",
"Soit FFF l’attracteur fractal de ce système (au sens usuel de la théorie IFS). La suite {Cn}\\{C_n\\}{Cn} peut alors être montrée convergente (par exemple, pour la distance de Hausdorff sur les compacts) vers l’ensemble FFF.\n",
"Résultat : dimH(F)=s\\dim_{\\mathcal{H}}(F) = sdimH(F)=s, où sss est la solution de l’équation de Hutchinson :\n",
"∑i=1kρis=1, \\sum_{i=1}^k \\rho_i^s = 1,i=1∑kρis=1,\n",
"(supposé non entier). Alors FFF est fractal, et CnC_nCn “devient” fractal dans la limite.\n",
"Conséquence : On a donc formellement exhibé un objet (la limite) qui n’a plus de dimension Euclidienne standard, mais une dimension de Hausdorff non entière, possédant un motif de répétition (self-similarité). C’est précisément ce que l’on entend par «fractal» dans un sens rigoureux.\n",
"\n",
"2.2. Hypothèses nécessaires\n",
"(H1) Contractions : Les Φn,i\\Phi_{n,i}Φn,i doivent réellement contracter les distances (ex. affinité avec un facteur ρ<1\\rho<1ρ<1).\n",
"(H2) Contrôle de recouvrement : Il ne doit pas y avoir un trop grand recouvrement ou une accumulation pathologique (sinon la dimension de Hausdorff peut diverger ou se réduire à un objet trop simple).\n",
"(H3) Approximation stable : On suppose que la suite Φn,i\\Phi_{n,i}Φn,i converge (au moins localement) vers {Φi}i=1k\\{\\Phi_i\\}_{i=1}^k{Φi}i=1k, ce qui permet de “geler” la dynamique dans la limite.\n",
"Ces hypothèses sont, dans la pratique, difficiles à vérifier précisément pour les véritables cosmohedra; elles sont plus simples à démontrer pour un modèle qui capture les mêmes règles combinatoires et dont la géométrie (les inégalités) est choisie pour permettre ces propriétés.\n",
"\n",
"3. Application à un «modèle cosmohedron fractal»\n",
"Pour aller du formalisme théorique ci-dessus à un exemple concret, on peut définir explicitement :\n",
"Un ensemble de variables (t1,…,tn)(t_1, \\dots, t_n)(t1,…,tn) modélisant les différents canaux d’énergie/moment (analogie aux coupes Feynman).\n",
"Un polytope Cn⊂RnC_n\\subset \\mathbb{R}^nCn⊂Rn défini par des inégalités du type 0≤t1≤t2≤⋯≤tn≤10 \\le t_1 \\le t_2 \\le \\dots \\le t_n \\le 10≤t1≤t2≤⋯≤tn≤1 et des contraintes supplémentaires (ti+ti+1≤αti−1+β)(t_i + t_{i+1} \\le \\alpha\\,t_{i-1} + \\beta)(ti+ti+1≤αti−1+β), etc.\n",
"Règles de subdivision : pour construire Cn+1C_{n+1}Cn+1 à partir de CnC_nCn, on rajoute des variables tn+1,…,tn+kt_{n+1}, \\dots, t_{n+k}tn+1,…,tn+k et des inégalités analogues, de sorte que la projection sur {t1,…,tn}\\{t_1,\\dots, t_n\\}{t1,…,tn} se décompose en “copies” échelonnées de CnC_nCn.\n",
"Exemple schématique :\n",
"Cn+1=⋂i=1n+1{(t1,…,tn+1):ti≥0,∑j=1n+1tj=1,}, C_{n+1} \\;=\\; \\bigcap_{i=1}^{n+1} \\bigl\\{ (t_1,\\dots,t_{n+1}) : t_{i} \\ge 0,\\; \\sum_{j=1}^{n+1} t_j = 1,\\; \\dots \\bigr\\},Cn+1=i=1⋂n+1{(t1,…,tn+1):ti≥0,j=1∑n+1tj=1,…},\n",
"avec certaines conditions linéaires (ou affines) introduisant une auto-similarité. On peut prouver que, si on choisit bien les coefficients, alors on obtient une suite {Cn}\\{C_n\\}{Cn} satisfaisant les hypothèses (H1), (H2) et (H3).\n",
"La preuve de fractalité (au sens Hausdorff) s’articule alors sur la démonstration que la projection (ou section) dans un sous-espace de dimension 2 (ou plus) admet un recouvrement par Φn,i(Cn)\\Phi_{n,i}(C_n)Φn,i(Cn) avec un rapport d’échelle ρ<1\\rho<1ρ<1. Dès lors, la même théorie IFS s’applique et conclut qu’on obtient un attracteur fractal dans la limite.\n",
"\n",
"4. Conclusion et perspectives\n",
"Construction rigoureuse :\n",
"On a donné le schéma d’une construction formelle (suivant la logique de la théorie IFS) permettant de définir une suite de polytopes s’apparentant à une “version fractale” de cosmohedra. Les conditions de contraction et de non-recouvrement permettent l’utilisation des théorèmes standards de la géométrie fractale (Barnsley, Falconer, Hutchinson).\n",
"Preuve de la fractalité :\n",
"La démonstration repose sur la convergence vers un attracteur fractal F\\,FF et l’évaluation de la dimension de Hausdorff par la formule ∑i=1kρis=1\\sum_{i=1}^k \\rho_i^s = 1∑i=1kρis=1. On obtient ainsi, en général, un exposant sss non entier, prouvant le caractère fractal.\n",
"Lien avec la physique :\n",
"D’un point de vue strictement mathématique, l’existence ou non d’une fractalité dans le vrai “espace de configurations” d’un cosmohedron cosmologique reste conjecturale. Il faudrait prouver qu’en dimension (potentiellement) très élevée et avec des contraintes physiques (pôles d’amplitudes, invariances conformes, etc.), la construction reproduit les conditions de l’IFS.\n",
"Remarque finale :\n",
"Bien que cette approche donne un cadre théorique pour exhiber un objet fractal (et prouver rigoureusement ses propriétés de fractalité), son application exacte aux cosmohedra décrits dans la littérature de la fonction d’onde cosmologique exigerait des travaux d’adaptation. Néanmoins, c’est ainsi que l’on procéderait pour avoir un argument formel :\n",
"Montrer que la “croissance” des polytopes répond à un schéma de self-similarité (au moins asymptotique),\n",
"Démontrer, via un théorème standard d’attracteur IFS, que la dimension du lieu-limite est non entière,\n",
"Conclure que la structure est fractale.\n",
"En résumé, cette construction est rigoureuse si l’on respecte les hypothèses de contraction, de non-recouvrement et de convergence vers des transformations Φi\\Phi_iΦi fixes. Elle aboutit à une preuve formelle (dans le sens de la théorie des IFS) que le limite de la suite de polytopes est un ensemble fractal, et donc qu’il existe une structure fractale sous-jacente dans ce modèle “cosmohedron-like” à la limite n→∞n \\to \\inftyn→∞.\n",
"\n",
"\"\"\"\n",
"resultats_md = main(mon_texte_de_preuve)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44349990-50c7-4fb7-a62c-532d829c2bdc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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week2/community-contributions/w2d1exercise.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ec2e81cd-2172-4816-bf44-f29312b8a4bd",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"import google.generativeai as genai\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a558dfa4-9496-48ba-b0f5-b0c731adc7b8",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc7c2cda-a5d1-4930-87f2-e06485d6b2bd",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()\n",
"\n",
"genai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3eb32aec-ec93-4563-bd88-0d48d2471884",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = \"gpt-4o-mini\"\n",
"claude_model = \"claude-3-haiku-20240307\"\n",
"gemini_model = \"gemini-2.0-flash-exp\"\n",
"\n",
"gpt_system = \"You are a chatbot who is sarcastic; \\\n",
"you have your speculations about anything in the conversation and you challenge everything in funny way.\\\n",
"You have to be a part of a group discussion and put forward your points about the topic\\\n",
"full-stack developers vs specialised developer. Keep your points short and precise.\"\n",
"\n",
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
"you try to calm them down and keep chatting.You have to be a part of a group discussion and put forward your points\\\n",
"about the topic full-stack developers vs specialised developer. Keep your points short and precise.\"\n",
"\n",
"gemini_system = \"You are a very rational thinker and don't like beating around the bush about the topic of discussion.\\\n",
"You have to be a part of a group discussion and put forward your points\\\n",
"about the topic full-stack developers vs specialised developer\\\n",
"Keep your points short and precise.\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"claude_messages = [\"Hi\"]\n",
"gemini_messages = [\"Hello to all\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e27252cf-05f5-4989-85ef-94e6802c5db9",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages,\n",
" max_tokens=500 # Add max_tokens to meet API requirement\n",
" )\n",
" return completion.choices[0].message.content\n",
"\n",
"# Function to call Claude\n",
"def call_claude():\n",
" messages = []\n",
" for gpt, claude_message,gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" message = claude.messages.create(\n",
" model=claude_model,\n",
" max_tokens=500,\n",
" messages=messages\n",
" )\n",
" return message.content[0].text\n",
"\n",
"# Function to call Gemini\n",
"def call_gemini():\n",
" # Create the Gemini model instance\n",
" gemini_model_instance = genai.GenerativeModel(\n",
" model_name=gemini_model, # Specify the model name here\n",
" system_instruction=gemini_system # Provide the system instruction\n",
" )\n",
" \n",
" # Prepare conversation history with separate names to avoid overwriting\n",
" gemini_messages_combined = []\n",
" for gpt, claude, gemini_msg in zip(gpt_messages, claude_messages, gemini_messages):\n",
" gemini_messages_combined.append({\"role\": \"assistant\", \"content\": gpt})\n",
" gemini_messages_combined.append({\"role\": \"user\", \"content\": claude})\n",
" gemini_messages_combined.append({\"role\": \"assistant\", \"content\": gemini_msg})\n",
" \n",
" # Generate content based on the conversation history\n",
" gemini_response = gemini_model_instance.generate_content(\"\".join([msg[\"content\"] for msg in gemini_messages_combined]))\n",
" \n",
" return gemini_response.text\n",
"\n",
"# Initial print\n",
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
"\n",
"# Main loop to generate conversation\n",
"for i in range(3):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" claude_next = call_claude()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude_messages.append(claude_next)\n",
" \n",
" gemini_next = call_gemini()\n",
" print(f\"Gemini:\\n{gemini_next}\\n\")\n",
" gemini_messages.append(gemini_next)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52f43794-a20a-4b9a-a18d-6f363b8dc27d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

305
week2/community-contributions/week 2 - multi modal StudyAI.ipynb

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197
week2/community-contributions/week2-exercise-translator.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "7563a171",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
"metadata": {},
"source": [
"# Exercise - week 2: German translator\n",
"\n",
"This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n",
"\n",
"The assistant will transform your spoken English to text, then translate it German and speak it out. The image on the UI is just decoration. This exercise was created on MacOS, Python 3.13."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a07e7793-b8f5-44f4-aded-5562f633271a",
"metadata": {},
"outputs": [],
"source": [
"# Install first PortAudio, in MacOS\n",
"# brew install portaudio\n",
"\n",
"\n",
"!pip install openai speechrecognition pyaudio\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "dcae50aa",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1796b554",
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c5caad24",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"\"\"You are a highly skilled language translator specializing in translating English text to German. \n",
"Your task is to accurately translate any English text provided by the user into German. \n",
"Ensure that the translations are grammatically correct and contextually appropriate. \n",
"If the user provides a phrase, sentence, or paragraph in English, respond with the equivalent translation in German.\"\"\" "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "aca69563",
"metadata": {},
"outputs": [],
"source": [
"import speech_recognition as sr\n",
"from pydub import AudioSegment\n",
"from pydub.playback import play\n",
"import base64\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"\n",
"\n",
"def recognize_speech(audio_file):\n",
" recognizer = sr.Recognizer()\n",
" with sr.AudioFile(audio_file) as source:\n",
" audio = recognizer.record(source)\n",
" try:\n",
" text = recognizer.recognize_google(audio)\n",
" return text\n",
" except sr.UnknownValueError:\n",
" return \"Google Speech Recognition could not understand audio\"\n",
" except sr.RequestError as e:\n",
" return f\"Could not request results from Google Speech Recognition service; {e}\"\n",
"\n",
"\n",
"def get_chatgpt_response(message):\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages = \n",
" [{\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": message}],\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content.strip()\n",
"\n",
"def process_audio(audio_file):\n",
" text = recognize_speech(audio_file)\n",
" if text:\n",
" response = get_chatgpt_response(text)\n",
" talker(response)\n",
" return response\n",
" return \"Could not recognize speech.\"\n",
"\n",
"def talker(message):\n",
" response = openai.audio.speech.create(\n",
" model=\"tts-1\",\n",
" voice=\"onyx\", # Also, try replacing onyx with alloy\n",
" input=message\n",
" )\n",
" \n",
" audio_stream = BytesIO(response.content)\n",
" audio = AudioSegment.from_file(audio_stream, format=\"mp3\")\n",
" play(audio)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1118141",
"metadata": {},
"outputs": [],
"source": [
"# Create Gradio interface\n",
"\n",
"# some image decoration to UI, just a static picture\n",
"image_path =\"week2-exercise-translator-berlin.webp\"\n",
"\n",
"with gr.Blocks() as ui:\n",
" gr.Interface(\n",
" fn=process_audio,\n",
" inputs=gr.Audio(type=\"filepath\", label=\"Speak English. German translation in a moment:\"),\n",
" outputs=\"text\",\n",
" live=True, \n",
" )\n",
" gr.Image(value=image_path, label=\"Das ist Berlin\")\n",
" \n",
"ui.launch(inbrowser=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1284da5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv313",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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week2/community-contributions/week2_code_interpreter_tool.ipynb

@ -0,0 +1,225 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
"metadata": {},
"source": [
"# A tool to evaluate a mathematical expression\n",
"\n",
"This week the tool used in FlightAI was a database lookup function.\n",
"\n",
"Here I implement a python code interpreter function as tool."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b0e8691-71f9-486c-859d-ea371401dfa9",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e2792ae-ff53-4b83-b2c3-866533ba2b29",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv()\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79e44ee9-af02-448c-a747-17780ee55791",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"MODEL = \"gpt-4o-mini\""
]
},
{
"cell_type": "markdown",
"id": "33ec55b1-0eff-43f1-9346-28145fa2fc47",
"metadata": {},
"source": [
"# Defining the tool function\n",
"\n",
"Add print statements to make sure the function is used instead of the native gpt interpreter capability.\n",
"\n",
"I used multi shot in the system prompt to make sure gpt generate the code in the format that the tool accept."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "94e0e171-4975-457b-88cb-c0d90f51ca65",
"metadata": {},
"outputs": [],
"source": [
"def evaluate_math_expression(my_code):\n",
" print(f\"EXECUTING FUNCTION WITH CODE: {my_code}\")\n",
" exec(my_code)\n",
" r = locals()['interpreter_result'] \n",
" return r\n",
"\n",
"\n",
"math_function = {\n",
" \"name\": \"evaluate_math_expression\",\n",
" \"description\": \"Give the result of a math expression. \\\n",
" Call this whenever you need to know the result of a mathematical expression. \\\n",
" Generate python code ALWAYS with the final result assigned to a variable called 'interpreter_result'. \\\n",
" For example when a user asks 'What is 2+2' generate 'interpreter_result = 2+2', and pass this code to the tool. \\\n",
" Another example if a user ask 'What is log(5)' generate 'import math; interpreter_result = math.log(5)' and pass this code to the tool.\",\n",
" \n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"my_code\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The python math expression to evaluate\",\n",
" },\n",
" },\n",
" \"required\": [\"my_code\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"tools = [{\"type\": \"function\", \"function\": math_function}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c85c01cc-776e-4a9d-b506-ea0d68fc072d",
"metadata": {},
"outputs": [],
"source": [
"evaluate_math_expression(\"import math; interpreter_result = math.log(5)\")"
]
},
{
"cell_type": "markdown",
"id": "858c5848-5835-4dff-9dc0-68babd367e11",
"metadata": {},
"source": [
"# Using the tool in a UI program\n",
"\n",
"You can ask messages like:\n",
"- \"What is 2+2?\"\n",
"- \"What is 3 power 2?\"\n",
"- \"I have 25 apples. I buy 10 apples. How manny apples do I have?\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c119b48b-d4b4-41ae-aa2f-2ec2f09af2f0",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a math assistant. \\\n",
"Generate python code to give result of a math expression, always name the result 'interpreter_result'. \\\n",
"For example when a user asks 'What is 2+2', generate 'interpreter_result = 2+2' and pass this code to the tool. \\\n",
"Another example: if a user ask 'What is log(5)' generate 'import math; interpreter_result = math.log(5)'\"\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" print(message)\n",
" response = handle_tool_call(message)\n",
" print(response)\n",
" messages.append(message)\n",
" messages.append(response)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" return response.choices[0].message.content\n",
"\n",
"\n",
"def handle_tool_call(message):\n",
" tool_call = message.tool_calls[0]\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" my_code = arguments.get('my_code')\n",
" interpreter_result = evaluate_math_expression(my_code)\n",
" response = {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"my_code\": my_code,\"interpreter_result\": interpreter_result}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" return response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3e50093-d7b6-4972-a8ba-6964f22218d3",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75c81d73-d2d6-4e6b-8511-94d4a725f595",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

614
week2/community-contributions/week2_exercise_translated_chatbot.ipynb

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2
week2/day2.ipynb

@ -53,7 +53,7 @@
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv()\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",

2
week2/day3.ipynb

@ -33,7 +33,7 @@
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv()\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",

2
week2/day4.ipynb

@ -35,7 +35,7 @@
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",

2
week2/day5.ipynb

@ -35,7 +35,7 @@
"source": [
"# Initialization\n",
"\n",
"load_dotenv()\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",

10
week3/community-contributions/dataset_generator.ipynb

@ -22,7 +22,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {
"id": "-Apd7-p-hyLk"
},
@ -84,7 +84,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {
"id": "WjxNWW6bvdgj"
},
@ -105,7 +105,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {
"id": "ZvljDKdji8iV"
},
@ -161,7 +161,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {
"id": "JAdfqYXnvEDE"
},
@ -196,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {
"id": "xy2RP5T-vxXg"
},

150
week3/community-contributions/day4_OCR_model_exercise.ipynb

@ -0,0 +1,150 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyPtAT7Yq5xd4vDcJEZtg69J"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"source": [
"# getting the latest transformers first, since this will require a restart\n",
"\n",
"!pip install git+https://github.com/huggingface/transformers.git"
],
"metadata": {
"id": "6gGKXU5RXORf"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# imports\n",
"\n",
"import torch\n",
"from google.colab import userdata\n",
"from huggingface_hub import login\n",
"from transformers import AutoProcessor, AutoModelForImageTextToText\n",
"from google.colab import files"
],
"metadata": {
"id": "yCRrF4aiXPPo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# logging in to HF\n",
"\n",
"hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)"
],
"metadata": {
"id": "AAlOQuCbXcrv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_RRVc2j2Vun-"
},
"outputs": [],
"source": [
"# this will start an input prompt for uploading local files\n",
"\n",
"uploaded = files.upload()\n",
"print(uploaded.keys()) # this will look sth like dict_keys([\"note2.jpg\"])"
]
},
{
"cell_type": "code",
"source": [
"'''\n",
"ChatGPT and Gemini explain the following part roughly like so:\n",
"The string contained in image_path is the key of the entry in the dictionary of uploaded files (see box above).\n",
"The value to that key contains the image in binary format.\n",
"The \"with open(image_path, \"wb\") as f\" part means: Create a new file \"note2.jpg\" on the server, and write to it in binary mode (\"wb\").\n",
"f.write(image) writes the binary image to that new file. \"note2.jpg\" aka image_path will now contain the image.\n",
"'''\n",
"\n",
"image_path = \"note2.jpg\" # update this string depending on the printout in the previous cell!\n",
"image = uploaded[image_path]\n",
"with open(image_path, \"wb\") as f:\n",
" f.write(image)"
],
"metadata": {
"id": "V_UAuSSkXBKh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# from HF model instructions\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"model = AutoModelForImageTextToText.from_pretrained(\"stepfun-ai/GOT-OCR-2.0-hf\", device_map=device)\n",
"processor = AutoProcessor.from_pretrained(\"stepfun-ai/GOT-OCR-2.0-hf\")"
],
"metadata": {
"id": "AiFP-mQtXrpV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# also from HF documentation about this model, see https://huggingface.co/stepfun-ai/GOT-OCR-2.0-hf\n",
"\n",
"image = image_path\n",
"inputs = processor(image, return_tensors=\"pt\").to(device)\n",
"\n",
"ocr = model.generate(\n",
" **inputs,\n",
" do_sample=False,\n",
" tokenizer=processor.tokenizer,\n",
" stop_strings=\"<|im_end|>\",\n",
" max_new_tokens=4096,\n",
")"
],
"metadata": {
"id": "7Adr8HB_YNf5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# prints out the recognized text. This can read my handwriting pretty well! And it works super quick on the free T4 GPU server here.\n",
"\n",
"print(processor.decode(ocr[0, inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True))"
],
"metadata": {
"id": "nRsRUIIuYdJ9"
},
"execution_count": null,
"outputs": []
}
]
}

322
week3/community-contributions/en-de-fr_dataset_generator.ipynb

@ -0,0 +1,322 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyPxJzufoQPtui+nhl1J1xiR"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yqlQTsxNdKrN"
},
"outputs": [],
"source": [
"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate openai httpx==0.27.2 gradio"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import requests\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI\n",
"from google.colab import drive\n",
"from huggingface_hub import login\n",
"from google.colab import userdata\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
"import torch\n",
"import gradio as gr\n",
"import re"
],
"metadata": {
"id": "eyfvQrLxdkGT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# one can always add more models, of course\n",
"\n",
"LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
"OPENAI_MODEL = \"gpt-4o-mini\""
],
"metadata": {
"id": "WW-cSZk7dnp6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)\n",
"openai_api_key = userdata.get('OPENAI_API_KEY')\n",
"openai = OpenAI(api_key=openai_api_key)"
],
"metadata": {
"id": "XG7Iam6Rdw8F"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"force_dark_mode = \"\"\"\n",
"function refresh() {\n",
" const url = new URL(window.location);\n",
" if (url.searchParams.get('__theme') !== 'dark') {\n",
" url.searchParams.set('__theme', 'dark');\n",
" window.location.href = url.href;\n",
" }\n",
"}\n",
"\"\"\""
],
"metadata": {
"id": "Ov7WSdx9dzSt"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def dataset_generator(model, nature, shots, volume, language):\n",
"\n",
" examples = \"Instruction: 'Make a random sentence.'\\nAnswer: 'When I got home last night, I couldn't believe my eyes: All the pineapples had been removed from the pizza.'\"\n",
" system_message = \"You are a random sentence generator. Generate 10 diverse English sentences.\"\n",
" user_prompt = f\"Generate 10 random English sentences, like so:\\n{examples}\"\n",
" sentences = \"\"\n",
"\n",
" if language == \"English\":\n",
"\n",
" for shot in list(shots.keys()):\n",
" examples += f\"\\nExample instruction: '{shot}'\\nExample answer: '{shots[shot]}'\\n\"\n",
"\n",
" system_message = f\"You are a state-of-the art linguistic dataset compiler. You are given a 'Type' of sentence to create. \\\n",
"Within the bounds of that type, create {volume} diverse sentences with differing structures and lengths. Make the sentences plausible, \\\n",
"but be creative in filling them with random concrete information, names, and data. Here are some examples for how to go about that:\\n{examples}\\n\\\n",
"Just output one sentence per line. Do not comment or format yor output in any way, shape, or form.\"\n",
"\n",
" user_prompt = f\"Generate {volume} English sentences of the following Type: {nature}. Just output one sentence per line. \\\n",
"Do not comment or format yor output in any way, shape, or form.\"\n",
"\n",
" elif language == \"German\":\n",
"\n",
" for shot in list(shots.keys()):\n",
" examples += f\"\\nAnweisung: '{shot}'\\nAntwort: '{shots[shot]}'\\n\"\n",
"\n",
" system_message = f\"Du bist ein weltklasse Datensatz-Sammler für Sprachdaten. Du erhältst einen 'Typ' von Sätzen, die du erstellen sollst. \\\n",
"Im Rahmen dieses Typs, generiere {volume} untereinander verschiedene Sätze mit unterschiedlichen Satzlängen und -strukturen. Mache die Beispielsätze \\\n",
"plausibel, aber fülle sie kreativ mit willkürlichen Informationen, Namen, und Daten aller Art. Hier sind ein paar Beispiel, wie du vorgehen sollst:\\n{examples}\\n\\\n",
"Gib einfach einen Satz pro Zeile aus. Kommentiere oder formatiere deine Antwort in keinster Weise.\"\n",
"\n",
" user_prompt = f\"Generiere {volume} deutsche Sätze des folgenden Typs: {nature}. Gib einfach einen Satz pro Zeile aus. \\\n",
"Kommentiere oder formatiere deine Antwort in keiner Weise.\"\n",
"\n",
" elif language == \"French\":\n",
"\n",
" for shot in list(shots.keys()):\n",
" examples += f\"\\nConsigne: '{shot}'\\nRéponse: '{shots[shot]}'\\n\"\n",
"\n",
" system_message = f\"Tu es un outil linguistique de pointe, à savoir, un genérateur de données linguistiques. Tu seras assigné un 'Type' de phrases à créer. \\\n",
"Dans le cadre de ce type-là, crée {volume} phrases diverses, avec des structures et longueurs qui varient. Génère des phrases qui soient plausibles, \\\n",
"mais sois créatif, et sers-toi de données, noms, et informations aléatoires pour rendre les phrases plus naturelles. Voici quelques examples comment faire:\\n{examples}\\n\\\n",
"Sors une seule phrase par ligne. Ne formatte ni commente ta réponse en aucune manière que ce soit.\"\n",
"\n",
" user_prompt = f\"S'il te plaît, crée {volume} phrases en français du Type suivant: {nature}. Sors une seule phrase par ligne. \\\n",
"Ne formatte ni commente ta réponse en aucune manière que ce soit.\"\n",
"\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
"\n",
" if model == \"Llama\":\n",
"\n",
" quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
" )\n",
"\n",
" tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n",
" streamer = TextStreamer(tokenizer)\n",
" model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"auto\", quantization_config=quant_config)\n",
" outputs = model.generate(inputs, max_new_tokens=10000)\n",
"\n",
" response = tokenizer.decode(outputs[0])\n",
" sentences = list(re.finditer(\"(?:<\\|end_header_id\\|>)([^<]+)(?:<\\|eot_id\\|>)\", str(response), re.DOTALL))[-1].group(1)\n",
"\n",
" elif model == \"OpenAI\":\n",
" response = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages)\n",
" sentences = response.choices[0].message.content\n",
"\n",
" return sentences"
],
"metadata": {
"id": "bEF8w_Mdd2Nb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"global data\n",
"data = \"\"\n",
"\n",
"with gr.Blocks(\n",
" css=\"\"\"\n",
" .red-button {\n",
" background-color: darkred !important;\n",
" border-color: red !important;\n",
" }\n",
" .blue-button {\n",
" background-color: darkblue !important;\n",
" border-color: blue !important;\n",
" }\n",
" .green-button {\n",
" background-color: green !important;\n",
" border-color: green !important;\n",
" }\n",
" \"\"\"\n",
") as view:\n",
" with gr.Row():\n",
" title = gr.HTML(\"<h1><big>D</big>ataset Generator <small>PLUS</small></h1><h2>for English, German, and French</h2>\")\n",
" subtitle = gr.HTML(\"<h3>Instructions:</h3><ol><li>Pick the language</li>\\\n",
"<li>Select a model</li><li>Indicate how many sentences you need</li>\\\n",
"<li>Describe the type of sentence you're looking for</li><li>Give up to three examples of the desired output sentence, and describe each of them briefly</li>\\\n",
"<li>Hit <q>Create Dataset</q></li>\\\n",
"<li>Save the output (.txt) to your Google Drive</li>\")\n",
" with gr.Row():\n",
" language_choice = gr.Dropdown(choices=[\"English\", \"German\", \"French\"], label=\"Select language\", value=\"English\", interactive=True)\n",
" model_choice = gr.Dropdown(choices=[\"Llama\", \"OpenAI\"], label=\"Select model\", value=\"Llama\", interactive=True)\n",
" volume = gr.Textbox(label=\"Required number of sentences\", interactive=True)\n",
" with gr.Row():\n",
" typeInput = gr.Textbox(label=\"Short description of the kind of sentence you need\", interactive=True)\n",
" with gr.Row():\n",
" sentence_1 = gr.Textbox(label=\"Example sentence 1\", interactive=True)\n",
" instruction_1 = gr.Textbox(label=\"Description\", interactive=True)\n",
" with gr.Row():\n",
" sentence_2 = gr.Textbox(label=\"Example sentence 2\", interactive=True)\n",
" instruction_2 = gr.Textbox(label=\"Description\", interactive=True)\n",
" with gr.Row():\n",
" sentence_3 = gr.Textbox(label=\"Example sentence 3\", interactive=True)\n",
" instruction_3 = gr.Textbox(label=\"Description\", interactive=True)\n",
" with gr.Row():\n",
" liveSentences = gr.Markdown(\n",
" value='<div style=\"color: #999; padding: 10px;\">Your sentences will be displayed here …</div>',\n",
" label=\"Generated sentences:\",\n",
" min_height=60,\n",
" max_height=200\n",
" )\n",
" with gr.Row():\n",
" generate = gr.Button(value=\"Generate sentences\", elem_classes=\"blue-button\")\n",
" with gr.Row():\n",
" clear = gr.Button(value=\"Clear everything\", elem_classes=\"red-button\")\n",
" with gr.Row():\n",
" outputPath = gr.Textbox(label=\"Specify the desired name and location on your Google Drive for the sentences (plain text) to be saved\", interactive=True)\n",
" with gr.Row():\n",
" save = gr.Button(value=\"Save generated data\", elem_classes=\"blue-button\")\n",
"\n",
" def generateSentences(typeInput, s1, i1, s2, i2, s3, i3, volume, language, model):\n",
" global data\n",
" nature = \"\"\n",
" shots = {}\n",
" amount = int(volume) if re.search(\"^[0-9]+$\", volume) is not None else 10\n",
"\n",
" if typeInput != None:\n",
" nature = typeInput\n",
" else:\n",
" nature = \"Random sentences of mixed nature\"\n",
"\n",
" if s1 != None:\n",
" if i1 != None:\n",
" shots[i1] = s1\n",
" else:\n",
" shots[\"A medium-long random sentence about anything\"] = s1\n",
" else:\n",
" shots[\"A medium-long random sentence about anything\"] = \"Paul, waking up out of his half-drunken haze, clearly couldn't tell left from right and ran right into the door.\"\n",
"\n",
" if s2 != None:\n",
" if i2 != None:\n",
" shots[i2] = s2\n",
" else:\n",
" shots[\"A medium-long random sentence about anything\"] = s2\n",
"\n",
" if s3 != None:\n",
" if i3 != None:\n",
" shots[i3] = s3\n",
" else:\n",
" shots[\"A medium-long random sentence about anything\"] = s3\n",
"\n",
" sentences = dataset_generator(model, nature, shots, amount, language)\n",
" data = sentences\n",
"\n",
" return sentences\n",
"\n",
" def saveData(path):\n",
" global data\n",
" drive.mount(\"/content/drive\")\n",
"\n",
" dir_path = os.path.dirname(\"/content/drive/MyDrive/\" + path)\n",
"\n",
" if not os.path.exists(dir_path):\n",
" os.makedirs(dir_path)\n",
"\n",
" with open(\"/content/drive/MyDrive/\" + path, \"w\", encoding=\"utf-8\") as f:\n",
" f.write(data)\n",
"\n",
" generate.click(generateSentences, inputs=[typeInput, sentence_1, instruction_1, sentence_2, instruction_2, sentence_3, instruction_3, volume, language_choice, model_choice], outputs=liveSentences)\n",
" clear.click(\n",
" lambda: [\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"\"),\n",
" gr.update(value='<div style=\"color: #999; padding: 10px;\">Your sentences will be displayed here …</div>'),\n",
" gr.update(value=\"\"),\n",
" gr.update(value=\"Save generated data\", elem_classes=\"blue-button\")],\n",
" None,\n",
" [volume, typeInput, sentence_1, instruction_1, sentence_2, instruction_2,\n",
" sentence_3, instruction_3, liveSentences, outputPath, save],\n",
" queue=False\n",
" )\n",
" save.click(saveData, inputs=outputPath, outputs=None).then(lambda: gr.update(value=\"Your data has been saved\", elem_classes=\"green-button\"), [], [save])\n",
"\n",
"view.launch(share=True) #, debug=True)"
],
"metadata": {
"id": "VRKdu0fEt8mg"
},
"execution_count": null,
"outputs": []
}
]
}

54
week3/community-contributions/telegram_bot_llm/README.md

@ -0,0 +1,54 @@
# What is this?
This is Telegram bot, it receives text and responds with robotic voice responses, which are are generated by locally run Ollama. It supports command "/echo" which allows you to receive just a voiceover of your message without involving AI. Text-to-speach (TTS) is performed by locally run tool Balabolka with preinstalled robotic voice.
## How to create bot?
- Open Telegram and search for BotFather (it’s an official Telegram bot for creating and managing other bots).
- Start a chat with BotFather and type /newbot to create a new bot.
- BotFather will ask you to provide a name for your bot. Choose a name that you want your bot to be identified by.
- After you provide a name, it will ask for a username. The username must be unique and must end with bot (e.g., myamazingbot).
- After creating the bot, BotFather will give you a token.
Save this token. Do not worry if you loose it - you can always open BotFather, get list of your bots and request tokens for each of them.
## Update environment variables
In the same folder with script create file named ".env". Use exactly this name, just 4 symbols.
In this file add text
```
BOT_TOKEN='<TOKEN_FOR_YOUR_BOT>'
HOSTNAME='<YOUR_HOSTNAME_OR_IP>'
```
Ex.
```
BOT_TOKEN='8030XXXXXX:AAHtR5F3H6WUYvAItQa2THEIFoEDbXXXXXX'
HOSTNAME='118-YYY-71-83-67'
```
## Setting up a webhook
Before using Telegram you need to set up webhook.
If you have public IP, then simply run the script and visit http://your-server-ip-or-domain:5000/setwebhook to set the webhook URL. This will configure Telegram to send updates to your bot.<br>
If you don't have public IP then:
- Run the script on local machine
- Register for free on [ngrok.com](https://ngrok.com). Sign in. After signing in, go to the "Get Started" page and copy your authtoken.
- Download ngrok from [ngrok.com](https://ngrok.com). Unzip downloaded file ex. in script folder.
- Run "ngrok config add-authtoken"
- Run command "ngrok http 5000". Pay attention that port is not 80 (default), but 5000, because script creates local server on this port. When ngrok started, it will give you URL, ex. "https://b\<your IP address\>.ngrok-free.app"
- To set the webhook URL, visit URL https://api.telegram.org/bot\<YOUR_BOT_TOKEN\>/setWebhook?url=\<IP address which ngrock shows\>/\<YOUR_BOT_TOKEN\>. Ex. "https://api.telegram.org/bot8030XXXXXX:AAHtR5F3H6WUYvAItQa2THEIFoEDbXXXXXX/setWebhook?url=https://b118-YYY-71-83-67.ngrok-free.app/8030XXXXXX:AAHtR5F3H6WUYvAItQa2THEIFoEDbXXXXXX". In case of success, you will receive json like this "{"ok":true,"result":true,"description":"Webhook was set"}"
## How to test bot
Now you can search in Telegram bot with your name, ex "myamazingbot". Start conversation and send something. In logs of your script you should be able to see this message, printed from handle_message() method.
## How to install local TTS (Text-to-speach)
**Note!** You can use remote TTS, ex. from ChatGPT. You don't have to use local one. I used local just because it is free.<br>
I assume you can use many applications for local TTS. In my case I used Balabolka tool, in particular, its CLI tool called "balcon" (https://www.cross-plus-a.com/ru/bconsole.html). After tool installation you need to install voices. Pay attention that some voices free, some are not. Write name of voice in script variable TTS_VOICE_NAME. In my case it is 'Maxim'.<br>
Note that if voice, which you use, supports only one language, you should send to TTS text in this language only.
Take into account that TTS results (.mp3 files) will be stored inside script folder. THey are going to be deleted when they are sent back to user, but in case of any failures, you may need to delete them manually.
## Supported bot commands
- "/start" - show information about supported commands
- "/echo" - just convert text to speech, do not use AI to create response
- "/clear" - clear the bot's memory so you can start the conversation from the beginning
## Limitations
- This bot is created just for fun. It is quite unstable, so you may need to fix some bugs when you find them
- Bot is not intended to be used in parallel by many users. Quite likely it will handle several users at the same time, but not tens or hundreds. So, if you need this feature you may need to make slight changes in script or its deployment.
- Chat history for every chat is stored in memory in global variable, which is very bad. Ex. it will lead to memory exhaustion. It will also lead to the history being cleared every time the bot is restarted.

199
week3/community-contributions/telegram_bot_llm/tg_lb_bot.py

@ -0,0 +1,199 @@
import os
import subprocess
from dotenv import load_dotenv
from quart import Quart, request, jsonify
from telegram import Update
from telegram.ext import Application, CommandHandler, MessageHandler, filters
import asyncio
import concurrent.futures
import ollama
# Load environment variables
load_dotenv()
TOKEN = os.getenv('BOT_TOKEN')
if TOKEN:
print(f"Bot token is {TOKEN}")
else:
print("Please provide your bot token in .env file")
HOSTNAME = os.getenv('HOSTNAME')
if HOSTNAME:
print(f"Host name is {HOSTNAME}")
else:
print("Please provide your host name (or IP address) in .env file")
BALCON_PATH = r"C:\Program Files (x86)\Balabolka\balcon\balcon.exe"
TTS_VOICE_NAME = 'Maxim'
MODEL = "llama3.1:8b"
SYSTEM_PROMPT = "You are an extremely cynical and rude assistant. Use the words 'leather bastard' as often " \
"as possible. Use the words 'leather bastard' every time you address a user or mention any " \
"person. For example 'It doesn't concern you, leather bastard'"
# Initialize the Application
app = Quart(__name__)
bot_application = Application.builder().token(TOKEN).build()
bot_initialized = False
# This global variable will store the chat messages. It is very bad practice.
chat_messages = {}
async def initialize_bot():
"""Ensure the bot is initialized only once."""
global bot_initialized
if not bot_initialized:
await bot_application.initialize()
await bot_application.start()
bot_initialized = True
print("Telegram bot initialized and started.")
# Command handler for /start
async def start(update: Update, context):
await update.message.reply_text(
"Hello, welcome to the bot! Here are the commands you can use:\n"
"/echo - Just convert text to speech, do not use AI to create response\n"
"/clear - Clear the bot's memory so you can start the conversation from the beginning."
)
async def clear(update: Update, context):
if update.message.chat_id in chat_messages:
del chat_messages[update.message.chat_id]
await update.message.reply_text(
"The bot's memory has been cleared. You can start the conversation from the beginning.")
def text_to_audio_file(input_text, filename="output.mp3"):
"""
Generate audio using Balcon and save it to the current directory.
:param input_text: Text to convert to speech.
:param filename: Desired output file name.
:return: Path to the generated file.
"""
# Ensure the filename is in the current script directory
current_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(current_dir, filename)
command = [BALCON_PATH, '-n', TTS_VOICE_NAME, "-t", input_text, "-w", file_path]
try:
subprocess.run(command, check=True)
print(f"Audio saved at: {file_path}")
return file_path
except subprocess.CalledProcessError as e:
print(f"Error generating audio: {e}")
return None
def ask_llm(messages):
response = ollama.chat(model=MODEL, messages=messages)
return response['message']['content']
def append_chat_message(chat_id, message, role):
if chat_id not in chat_messages:
chat_messages[chat_id] = [{"role": "system", "content": SYSTEM_PROMPT}]
chat_messages[chat_id].append({"role": role, "content": message})
def get_first_word(input_string):
# Split the string into words and strip any surrounding whitespace from the first word
return input_string.split()[0].strip() if input_string.strip() else None
def process_user_message(message):
chat_id = message.chat_id
message_id = message.message_id
user_message = message.text
words = user_message.strip().split(maxsplit=1)
if len(words) == 0:
return "", ""
if words[0] in ["/echo", "/clear"]:
# Remove the command from the message
tts_message = words[1] if len(words) > 1 else ""
else:
append_chat_message(chat_id, user_message, "user")
tts_message = ask_llm(chat_messages[chat_id])
append_chat_message(chat_id, tts_message, "assistant")
audio_file_path = text_to_audio_file(tts_message, filename=f"{chat_id}-{message_id}.mp3")
return audio_file_path, tts_message
# Message handler to log and print all incoming messages
async def handle_message(update: Update, context):
user_message = update.message.text
chat_id = update.message.chat_id
sender = update.message.from_user
print(f"Message from {sender.first_name} (chat ID: {chat_id}): {user_message}")
# Wait when processing of user message is done.
# During this time send chat action to user, so that it will see that bot is preparing audio.
# Notification diasppear after 5 seconds, so you need to send them repeatedly every 4 seconds.
audio_file_path = None
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(process_user_message, update.message)
while True:
try:
# Show to user that bot is busy with preparing audio response
await context.bot.send_chat_action(chat_id=chat_id, action='record_audio')
result, answer = future.result(timeout=4)
audio_file_path = result
user_message = answer
break
except concurrent.futures.TimeoutError:
continue
if len(answer) == 0:
await update.message.reply_text(f"Empty message")
return
try:
with open(audio_file_path, 'rb') as audio_file:
await context.bot.send_audio(
chat_id=chat_id,
audio=audio_file,
caption="Ответ робота",
write_timeout=120
)
print(f"Audio sent to {sender.first_name} (chat ID: {chat_id})")
os.remove(audio_file_path)
except Exception as e:
print(f"Exception while sending file: {e}")
# Optional text response. Comment this line if you want bot to answer only with audio
await update.message.reply_text(user_message)
bot_application.add_handler(CommandHandler('start', start))
bot_application.add_handler(CommandHandler('echo', handle_message))
bot_application.add_handler(CommandHandler('clear', clear))
bot_application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
@app.route('/' + TOKEN, methods=['POST'])
async def webhook():
"""Webhook endpoint for receiving updates."""
try:
await initialize_bot() # Ensure bot is initialized before processing updates
update = Update.de_json(await request.get_json(), bot_application.bot)
await bot_application.process_update(update)
except Exception as e:
print(f"Error processing update: {e}")
return jsonify({"status": "ok"})
# @app.route('/setwebhook', methods=['GET'])
async def set_webhook():
"""Set the webhook with Telegram."""
await initialize_bot() # Ensure bot is initialized before setting the webhook
webhook_url = f"https://{HOSTNAME}/{TOKEN}"
success = await bot_application.bot.set_webhook(webhook_url)
if success:
return jsonify({"status": "webhook set successfully", "url": webhook_url})
return jsonify({"status": "failed to set webhook"})
if __name__ == '__main__':
loop = asyncio.get_event_loop()
loop.create_task(initialize_bot()) # Initialize bot at startup
app.run(host='0.0.0.0', port=5000)

8
week3/day3.ipynb

@ -11,14 +11,6 @@
"\n",
"https://colab.research.google.com/drive/1WD6Y2N7ctQi1X9wa6rpkg8UfyA4iSVuz?usp=sharing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9289ba7-200c-43a9-b67a-c5ce826c9537",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

956
week4/community-contributions/day4_extra_deepseek_and_hf_inference_provider_added.ipynb

File diff suppressed because one or more lines are too long

925
week4/community-contributions/day4_with_inference_provider.ipynb

@ -0,0 +1,925 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
"metadata": {},
"source": [
"# Code Generator\n",
"\n",
"The requirement: use an Open Source model to generate high performance C++ code from Python code\n",
"\n",
"To replicate this, you'll need to set up a HuggingFace endpoint as I do in the video. It's simple to do, and it's quite satisfying to see the results!\n",
"\n",
"It's also an important part of your learning; this is the first example of deploying an open source model to be behind an API. We'll return to this in Week 8, but this should plant a seed in your mind for what's involved in moving open source models into production.\n",
"\n",
"Added the use of inference providers that was introduced recently by Hugging Face to convert the code.\n",
"Improved the user prompt to include algorithic efficeiny and performance optimization.\n",
"\n",
"Added Java as a conversion option.\n",
"\n",
"Note: C++ commands work on windows environment.\n"
]
},
{
"cell_type": "markdown",
"id": "22e1567b-33fd-49e7-866e-4b635d15715a",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h1 style=\"color:#900;\">Important - Pause Endpoints when not in use</h1>\n",
" <span style=\"color:#900;\">\n",
" If you do decide to use HuggingFace endpoints for this project, you should stop or pause the endpoints when you are done to avoid accruing unnecessary running cost. The costs are very low as long as you only run the endpoint when you're using it. Navigate to the HuggingFace endpoint UI <a href=\"https://ui.endpoints.huggingface.co/\">here,</a> open your endpoint, and click Pause to put it on pause so you no longer pay for it. \n",
"Many thanks to student John L. for raising this.\n",
"<br/><br/>\n",
"In week 8 we will use Modal instead of HuggingFace endpoints; with Modal you only pay for the time that you use it and you should get free credits.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": 231,
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import io\n",
"import sys\n",
"import json\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import google.generativeai\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display\n",
"import gradio as gr\n",
"import subprocess, re"
]
},
{
"cell_type": "code",
"execution_count": 198,
"id": "4f672e1c-87e9-4865-b760-370fa605e614",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv()\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
"metadata": {},
"outputs": [],
"source": [
"# initialize\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"OPENAI_MODEL = \"gpt-4o\"\n",
"CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\""
]
},
{
"cell_type": "code",
"execution_count": 200,
"id": "2db60a72-d098-42ca-8ce2-1e037c86b718",
"metadata": {},
"outputs": [],
"source": [
"def system_prompt_for(language: str) -> str:\n",
" system_prompt = (\n",
" f\"You are an assistant that reimplements Python code in high performance {language.upper()} for an Windows intel i7.\"\n",
" f\"Respond only with {language.upper()} code; use comments sparingly and do not provide any explanation other than occasional comments.\"\n",
" f\"The {language.upper()} response needs to produce an identical output in the fastest possible time. Keep implementations of random number generators identical so that results match exactly.\"\n",
" )\n",
" return system_prompt"
]
},
{
"cell_type": "code",
"execution_count": 243,
"id": "70583432-e851-40d1-a219-2fb32b830dc8",
"metadata": {},
"outputs": [],
"source": [
"#updated the original prompt to include algorithic efficeiny and performance optimization\n",
"def user_prompt_for(python: str, language: str) -> str:\n",
" if language.lower() not in {\"cpp\", \"java\"}:\n",
" raise ValueError(\"Unsupported language. Please choose 'C++' or 'Java'.\")\n",
" \n",
" optimization_notes = {\n",
" \"cpp\": (\n",
" \"- Use `int64_t` instead of `int` where necessary to prevent overflows.\\n\"\n",
" \"- Ensure random number generation in C++ matches Python's output as closely as possible.\\n\"\n",
" \"- Avoid undefined behavior, such as bit shifts that exceed type width (`1UL << 32` is incorrect for `uint32_t`).\\n\"\n",
" \"- Utilize `std::vector` for dynamic arrays and prefer preallocation for efficiency.\\n\"\n",
" \"- Consider `std::array` or `std::span` when fixed-size arrays are sufficient.\\n\"\n",
" \"- Optimize with **SIMD**, cache-friendly structures, and memory alignment where necessary.\\n\"\n",
" ),\n",
" \"java\": (\n",
" \"- Use `long` instead of `int` where necessary to prevent overflows.\\n\"\n",
" \"- Ensure random number generation in Java matches Python's output as closely as possible.\\n\"\n",
" \"- Use `ArrayList` instead of primitive arrays if dynamic resizing is needed.\\n\"\n",
" \"- Utilize `BigInteger` if handling large numbers that could exceed `long`.\\n\"\n",
" \"- Optimize with **parallel streams** (`IntStream.parallel()`) and **efficient data structures** (`HashMap`, `LinkedList`, etc.).\\n\"\n",
" )\n",
" }\n",
"\n",
" user_prompt = (\n",
" f\"First, analyze the given Python code to understand its core purpose and algorithmic approach. \"\n",
" f\"Then, implement a {language} solution that achieves the same output while prioritizing:\\n\"\n",
" \"1. **Algorithmic Efficiency** - Optimize time and space complexity, even if it means using a different approach.\\n\"\n",
" \"2. **Numerical Correctness** - Prevent integer overflows, use appropriate data types (`long`, `BigInteger`, etc.), \"\n",
" \"and ensure correct handling of edge cases.\\n\"\n",
" \"3. **Performance Optimization** - Utilize language-specific features for efficiency.\\n\\n\"\n",
" \n",
" \"### **Important Notes:**\\n\"\n",
" + optimization_notes[language.lower()] +\n",
" \"\\n### **Expected Response:**\\n\"\n",
" f\"Respond **only with {language} code**, including all necessary imports and ensuring the output matches the Python version exactly.\\n\\n\"\n",
" \n",
" \"Here's the Python code to analyze and optimize:\\n\\n\"\n",
" + python\n",
" )\n",
" \n",
" return user_prompt\n"
]
},
{
"cell_type": "code",
"execution_count": 202,
"id": "c6190659-f54c-4951-bef4-4960f8e51cc4",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(python, language=\"cpp\"):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt_for(language)},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(python, language)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 241,
"id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b",
"metadata": {},
"outputs": [],
"source": [
"# write to a file called optimized.cpp\n",
"\n",
"def write_output(code, file_name):\n",
" with open(file_name, \"w\") as f:\n",
" f.write(code)"
]
},
{
"cell_type": "code",
"execution_count": 226,
"id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9",
"metadata": {},
"outputs": [],
"source": [
"def optimize_gpt(python, language=\"cpp\"): \n",
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python, language), stream=True)\n",
" reply = \"\"\n",
" for chunk in stream:\n",
" fragment = chunk.choices[0].delta.content or \"\"\n",
" reply += fragment\n",
" print(fragment, end='', flush=True)\n",
" file_name= f\"optimized.{language}\"\n",
" if language == \"java\":\n",
" # Extract class name from Java code\n",
" match = re.search(r\"\\b(public\\s+)?class\\s+(\\w+)\", reply)\n",
" class_name = match.group(2) if match else \"OptimizedJava\"\n",
" file_name = f\"{class_name}.java\"\n",
" else:\n",
" file_name = f\"optimized.{language}\"\n",
" write_output(reply, file_name)"
]
},
{
"cell_type": "code",
"execution_count": 227,
"id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d",
"metadata": {},
"outputs": [],
"source": [
"def optimize_claude(python, language=\"cpp\"):\n",
" result = claude.messages.stream(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" system=system_message,\n",
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python, language)}],\n",
" )\n",
" reply = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" reply += text\n",
" print(text, end=\"\", flush=True)\n",
" if language == \"java\":\n",
" # Extract class name from Java code\n",
" match = re.search(r\"\\b(public\\s+)?class\\s+(\\w+)\", reply)\n",
" class_name = match.group(2) if match else \"OptimizedJava\"\n",
" file_name = f\"{class_name}.java\"\n",
" else:\n",
" file_name = f\"optimized.{language}\"\n",
" write_output(reply, file_name)"
]
},
{
"cell_type": "code",
"execution_count": 206,
"id": "a1cbb778-fa57-43de-b04b-ed523f396c38",
"metadata": {},
"outputs": [],
"source": [
"pi = \"\"\"\n",
"import time\n",
"\n",
"def calculate(iterations, param1, param2):\n",
" result = 1.0\n",
" for i in range(1, iterations+1):\n",
" j = i * param1 - param2\n",
" result -= (1/j)\n",
" j = i * param1 + param2\n",
" result += (1/j)\n",
" return result\n",
"\n",
"start_time = time.time()\n",
"result = calculate(100_000_000, 4, 1) * 4\n",
"end_time = time.time()\n",
"\n",
"print(f\"Result: {result:.12f}\")\n",
"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200",
"metadata": {},
"outputs": [],
"source": [
"exec(pi)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "105db6f9-343c-491d-8e44-3a5328b81719",
"metadata": {},
"outputs": [],
"source": [
"optimize_gpt(pi, \"java\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf26ee95-0c77-491d-9a91-579a1e96a8a3",
"metadata": {},
"outputs": [],
"source": [
"exec(pi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40",
"metadata": {},
"outputs": [],
"source": [
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n",
"!optimized.exe"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6",
"metadata": {},
"outputs": [],
"source": [
"optimize_claude(pi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf",
"metadata": {},
"outputs": [],
"source": [
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n",
"!optimized.exe"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
"metadata": {},
"outputs": [],
"source": [
"python_hard = \"\"\"# Be careful to support large number sizes\n",
"\n",
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
" value = seed\n",
" while True:\n",
" value = (a * value + c) % m\n",
" yield value\n",
" \n",
"def max_subarray_sum(n, seed, min_val, max_val):\n",
" lcg_gen = lcg(seed)\n",
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
" max_sum = float('-inf')\n",
" for i in range(n):\n",
" current_sum = 0\n",
" for j in range(i, n):\n",
" current_sum += random_numbers[j]\n",
" if current_sum > max_sum:\n",
" max_sum = current_sum\n",
" return max_sum\n",
"\n",
"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
" total_sum = 0\n",
" lcg_gen = lcg(initial_seed)\n",
" for _ in range(20):\n",
" seed = next(lcg_gen)\n",
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
" return total_sum\n",
"\n",
"# Parameters\n",
"n = 10000 # Number of random numbers\n",
"initial_seed = 42 # Initial seed for the LCG\n",
"min_val = -10 # Minimum value of random numbers\n",
"max_val = 10 # Maximum value of random numbers\n",
"\n",
"# Timing the function\n",
"import time\n",
"start_time = time.time()\n",
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
"end_time = time.time()\n",
"\n",
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "dab5e4bc-276c-4555-bd4c-12c699d5e899",
"metadata": {},
"outputs": [],
"source": [
"exec(python_hard)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8",
"metadata": {},
"outputs": [],
"source": [
"optimize_gpt(python_hard)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e0b3d073-88a2-40b2-831c-6f0c345c256f",
"metadata": {},
"outputs": [],
"source": [
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n",
"!optimized.exe"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c",
"metadata": {},
"outputs": [],
"source": [
"optimize_claude(python_hard)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c181036-8193-4fdd-aef3-fc513b218d43",
"metadata": {},
"outputs": [],
"source": [
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n",
"!optimized.exe"
]
},
{
"cell_type": "code",
"execution_count": 240,
"id": "0be9f47d-5213-4700-b0e2-d444c7c738c0",
"metadata": {},
"outputs": [],
"source": [
"def stream_gpt(python, language=\"cpp\"): \n",
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python, language), stream=True)\n",
" reply = \"\"\n",
" code_block = f\"```{language}\\n\"\n",
" for chunk in stream:\n",
" fragment = chunk.choices[0].delta.content or \"\"\n",
" reply += fragment\n",
" cleaned_reply = reply.replace(code_block,'').replace('```','')\n",
" yield cleaned_reply"
]
},
{
"cell_type": "code",
"execution_count": 239,
"id": "8669f56b-8314-4582-a167-78842caea131",
"metadata": {},
"outputs": [],
"source": [
"def stream_claude(python, language=\"cpp\"):\n",
" result = claude.messages.stream(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" system=system_message,\n",
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python, language)}],\n",
" )\n",
" reply = \"\"\n",
" code_block = f\"```{language}\\n\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" reply += text\n",
" cleaned_reply = reply.replace(code_block,'').replace('```','')\n",
" yield cleaned_reply"
]
},
{
"cell_type": "code",
"execution_count": 186,
"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d",
"metadata": {},
"outputs": [],
"source": [
"def optimize(python, model):\n",
" if model==\"GPT\":\n",
" result = stream_gpt(python)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(python)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" for stream_so_far in result:\n",
" yield stream_so_far "
]
},
{
"cell_type": "code",
"execution_count": 189,
"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7888/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n",
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n",
" with gr.Row():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n",
" convert = gr.Button(\"Convert code\")\n",
"\n",
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": 210,
"id": "19bf2bff-a822-4009-a539-f003b1651383",
"metadata": {},
"outputs": [],
"source": [
"def execute_python(code):\n",
" try:\n",
" output = io.StringIO()\n",
" sys.stdout = output\n",
" exec(code)\n",
" finally:\n",
" sys.stdout = sys.__stdout__\n",
" return output.getvalue()"
]
},
{
"cell_type": "code",
"execution_count": 211,
"id": "9a2274f1-d03b-42c0-8dcc-4ce159b18442",
"metadata": {},
"outputs": [],
"source": [
"css = \"\"\"\n",
".python {background-color: #306998;}\n",
".cpp {background-color: #050;}\n",
".java {background-color: #306775;}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "f1303932-160c-424b-97a8-d28c816721b2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7868/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with gr.Blocks(css=css) as ui:\n",
" gr.Markdown(\"## Convert code from Python to C++\")\n",
" with gr.Row():\n",
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n",
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n",
" with gr.Row():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n",
" with gr.Row():\n",
" convert = gr.Button(\"Convert code\")\n",
" with gr.Row():\n",
" python_run = gr.Button(\"Run Python\")\n",
" cpp_run = gr.Button(\"Run C++\")\n",
" with gr.Row():\n",
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n",
" cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n",
"\n",
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n",
" cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"id": "bb8c5b4e-ec51-4f21-b3f8-6aa94fede86d",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login, InferenceClient"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "13347633-4606-4e38-9927-80c39e65c1f1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
]
}
],
"source": [
"hf_token = os.environ['HF_TOKEN']\n",
"login(hf_token)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "ef60a4df-6267-4ebd-8eed-dcb917af0a5e",
"metadata": {},
"outputs": [],
"source": [
"code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n",
"code_gemma = \"google/codegemma-7b-it\"\n",
"messages=messages_for(pi)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "3825d77a-03c6-42b2-89bc-ccbcb1585740",
"metadata": {},
"outputs": [
{
"ename": "HfHubHTTPError",
"evalue": "402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions (Request ID: Root=1-67afb729-1eb9aff1704314144ef14e59;2df843ad-b7d2-4145-bb7b-1cfd94ae19ef)\n\nYou have exceeded your monthly included credits for Inference Endpoints. Subscribe to PRO to get 20x more monthly allowance.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mHTTPError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py:406\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m 405\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 406\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\requests\\models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[1;32m-> 1024\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
"\u001b[1;31mHTTPError\u001b[0m: 402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mHfHubHTTPError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[119], line 5\u001b[0m\n\u001b[0;32m 1\u001b[0m client \u001b[38;5;241m=\u001b[39m InferenceClient(\n\u001b[0;32m 2\u001b[0m \tprovider\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msambanova\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 3\u001b[0m \tapi_key\u001b[38;5;241m=\u001b[39mhf_token\n\u001b[0;32m 4\u001b[0m )\n\u001b[1;32m----> 5\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mQwen/Qwen2.5-Coder-32B-Instruct\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 10\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m stream:\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(chunk\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mdelta\u001b[38;5;241m.\u001b[39mcontent, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\inference\\_client.py:970\u001b[0m, in \u001b[0;36mInferenceClient.chat_completion\u001b[1;34m(self, messages, model, stream, frequency_penalty, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream_options, temperature, tool_choice, tool_prompt, tools, top_logprobs, top_p)\u001b[0m\n\u001b[0;32m 943\u001b[0m parameters \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 944\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: payload_model,\n\u001b[0;32m 945\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrequency_penalty\u001b[39m\u001b[38;5;124m\"\u001b[39m: frequency_penalty,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 961\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream_options\u001b[39m\u001b[38;5;124m\"\u001b[39m: stream_options,\n\u001b[0;32m 962\u001b[0m }\n\u001b[0;32m 963\u001b[0m request_parameters \u001b[38;5;241m=\u001b[39m provider_helper\u001b[38;5;241m.\u001b[39mprepare_request(\n\u001b[0;32m 964\u001b[0m inputs\u001b[38;5;241m=\u001b[39mmessages,\n\u001b[0;32m 965\u001b[0m parameters\u001b[38;5;241m=\u001b[39mparameters,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 968\u001b[0m api_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoken,\n\u001b[0;32m 969\u001b[0m )\n\u001b[1;32m--> 970\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inner_post\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 972\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n\u001b[0;32m 973\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _stream_chat_completion_response(data) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\inference\\_client.py:327\u001b[0m, in \u001b[0;36mInferenceClient._inner_post\u001b[1;34m(self, request_parameters, stream)\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InferenceTimeoutError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInference call timed out: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrequest_parameters\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merror\u001b[39;00m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 327\u001b[0m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39miter_lines() \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;28;01melse\u001b[39;00m response\u001b[38;5;241m.\u001b[39mcontent\n\u001b[0;32m 329\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m error:\n",
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py:477\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m 473\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 475\u001b[0m \u001b[38;5;66;03m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[39;00m\n\u001b[0;32m 476\u001b[0m \u001b[38;5;66;03m# as well (request id and/or server error message)\u001b[39;00m\n\u001b[1;32m--> 477\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, \u001b[38;5;28mstr\u001b[39m(e), response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
"\u001b[1;31mHfHubHTTPError\u001b[0m: 402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions (Request ID: Root=1-67afb729-1eb9aff1704314144ef14e59;2df843ad-b7d2-4145-bb7b-1cfd94ae19ef)\n\nYou have exceeded your monthly included credits for Inference Endpoints. Subscribe to PRO to get 20x more monthly allowance."
]
}
],
"source": [
"client = InferenceClient(\n",
"\tprovider=\"sambanova\",\n",
"\tapi_key=hf_token\n",
")\n",
"stream = client.chat.completions.create(\n",
"\tmodel=\"Qwen/Qwen2.5-Coder-32B-Instruct\", \n",
"\tmessages=messages, \n",
"\tmax_tokens=500,\n",
"\tstream=True\n",
")\n",
"\n",
"for chunk in stream:\n",
" print(chunk.choices[0].delta.content, end=\"\")"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "cc0c3e9c-2572-41d1-a476-6eae96b20695",
"metadata": {},
"outputs": [],
"source": [
"# using inference providers\n",
"def stream_code_qwen(python):\n",
" messages = messages_for(python)\n",
" client = InferenceClient(\n",
" \tprovider=\"sambanova\",\n",
" \tapi_key=hf_token\n",
" )\n",
" stream = client.chat.completions.create(\n",
" \tmodel=\"Qwen/Qwen2.5-Coder-32B-Instruct\", \n",
" \tmessages=messages, \n",
" \tmax_tokens=500,\n",
" \tstream=True\n",
" )\n",
" result = \"\"\n",
" for chunk in stream:\n",
" if chunk.choices and chunk.choices[0].delta.content:\n",
" result += chunk.choices[0].delta.content\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": 212,
"id": "a82387d1-7651-4923-995b-fe18356fcaa6",
"metadata": {},
"outputs": [],
"source": [
"def optimize(python, model, language):\n",
" if model==\"GPT\":\n",
" result = stream_gpt(python, language)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(python, language)\n",
" elif model==\"CodeQwen\":\n",
" result = stream_code_qwen(python, language)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" for stream_so_far in result:\n",
" yield stream_so_far "
]
},
{
"cell_type": "code",
"execution_count": 213,
"id": "4ba311ec-c16a-4fe0-946b-4b940704cf65",
"metadata": {},
"outputs": [],
"source": [
"def select_sample_program(sample_program):\n",
" if sample_program==\"pi\":\n",
" return pi\n",
" elif sample_program==\"python_hard\":\n",
" return python_hard\n",
" else:\n",
" return \"Type your Python program here\""
]
},
{
"cell_type": "code",
"execution_count": 214,
"id": "06148e88-501b-4686-a41d-c3be528d8e6f",
"metadata": {},
"outputs": [],
"source": [
"def execute_cpp(code):\n",
" write_output(code, \"optimized.exe\")\n",
" try:\n",
" compile_cmd = [\"g++\", \"-Ofast\", \"-std=c++17\", \"-march=native\", \"-mtune=intel\", \"-o\", \"optimized\", \"optimized.cpp\"]\n",
" compile_result = subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n",
" run_cmd = [\"optimized.exe\"]\n",
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n",
" return run_result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" return f\"An error occurred:\\n{e.stderr}\""
]
},
{
"cell_type": "code",
"execution_count": 236,
"id": "a42e3871-f3a5-4f14-836c-1e8ecacb56b5",
"metadata": {},
"outputs": [],
"source": [
"def execute_java(code):\n",
" # Extract the class name from the Java code\n",
" match = re.search(r\"\\b(public\\s+)?class\\s+(\\w+)\", code)\n",
" class_name = match.group(2) if match else \"OptimizedJava\"\n",
"\n",
" file_name = f\"{class_name}.java\"\n",
" write_output(code, file_name)\n",
" try:\n",
" compile_cmd =[\"javac\", file_name]\n",
" subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n",
" run_cmd = [\"java\", class_name]\n",
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n",
" return run_result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" return f\"Error during compilation or execution:\\n{e.stderr}\""
]
},
{
"cell_type": "code",
"execution_count": 238,
"id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7901/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with gr.Blocks(css=css) as ui:\n",
" gr.Markdown(\"## Convert code from Python to C++ or Java\")\n",
" #input and output\n",
" with gr.Row():\n",
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n",
" converted_code = gr.Textbox(label=\"Converted code:\", lines=10)\n",
" # java = gr.Textbox(label=\"Java code:\", lines=10)\n",
" #sample programs\n",
" with gr.Row():\n",
" with gr.Column():\n",
" sample_program = gr.Radio([\"pi\", \"python_hard\"], label=\"Sample program\", value=\"python_hard\")\n",
" #select model and language\n",
" with gr.Row():\n",
" with gr.Column():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\", \"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n",
" language = gr.Dropdown([\"C++\",\"Java\"], label=\"Select language\", value=\"C++\")\n",
" with gr.Row():\n",
" convert = gr.Button(\"Convert\")\n",
" #Code execution\n",
" with gr.Row():\n",
" python_run = gr.Button(\"Run Python\")\n",
" converted_run = gr.Button(\"Run converted code\")\n",
" with gr.Row():\n",
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n",
" output = gr.TextArea(label=\"Converted code result:\", elem_classes=[\"cpp\"])\n",
" \n",
" # Function to convert Python code based on language\n",
" def convert_code(python_code, model, selected_language):\n",
" if selected_language == \"C++\":\n",
" for chunk in optimize(python_code, model, \"cpp\"):\n",
" yield chunk # Stream each chunk\n",
" elif selected_language == \"Java\":\n",
" for chunk in optimize(python_code, model, \"java\"):\n",
" yield chunk\n",
" return \"\"\n",
"\n",
" # Function to execute converted code\n",
" def run_code(converted_code, selected_language):\n",
" if selected_language == \"C++\":\n",
" return execute_cpp(converted_code)\n",
" elif selected_language == \"Java\":\n",
" return execute_java(converted_code)\n",
" return \"Invalid language selection\"\n",
"\n",
" sample_program.change(select_sample_program, inputs=[sample_program], outputs=[python])\n",
" convert.click(convert_code, inputs=[python, model, language], outputs=[converted_code])\n",
" converted_run.click(run_code, inputs=[converted_code, language], outputs=[output]) \n",
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d0ad093-425b-488e-8c3f-67f729dd9c06",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
week4/day3.ipynb

@ -86,7 +86,7 @@
"source": [
"# environment\n",
"\n",
"load_dotenv()\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')"
]

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