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Completed Day2 exercise. Included selenium chrome webdriver support to read webpages.

pull/106/head
Rakesh H K 4 months ago
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commit
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  1. 383
      week1/community-contributions/day2 Exercise_RHK.ipynb

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week1/community-contributions/day2 Exercise_RHK.ipynb

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{
"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": "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": 6,
"id": "402d5686-4e76-4110-b65a-b3906c35c0a4",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import ollama\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"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",
"\n",
"#!ollama pull llama3.2\n",
"MODEL = \"llama3.2\"\n",
"openai = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cca8ae91-ad1e-4239-951f-e1376a5ec934",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Please complete the verification in the browser and press Enter to continue... \n"
]
},
{
"data": {
"text/markdown": [
"This article discussing Serverless architecture is a comprehensive overview of the concept, its benefits, and challenges. Here's a summary of the main points:\n",
"\n",
"**What is Serverless Architecture?**\n",
"\n",
"Serverless computing allows developers to run their applications without managing servers or provisioning resources upfront. Instead, they pay only for the compute time used.\n",
"\n",
"**Key Techniques:**\n",
"\n",
"1. **Function as a Service (FaaS)**: Ephemeral function instances that can be executed with specific inputs and outputs.\n",
"2. **Background Services**: Tightly integrate third-party remote application services directly into the frontend of an app.\n",
"\n",
"**Benefits:**\n",
"\n",
"1. Reduced operational and development costs\n",
"2. Easier management and scaling\n",
"3. Reduced environmental impact\n",
"4. Faster time-to-market\n",
"\n",
"**Challenges:**\n",
"\n",
"1. Debugging and monitoring complexity\n",
"2. Limited control over server-side code execution\n",
"3. High dependencies on cloud providers\n",
"\n",
"**Serverless Landscape:**\n",
"\n",
"The author expects the Serverless community to grow, with upcoming conferences, meetups, and online groups.\n",
"\n",
"**Conclusion:**\n",
"\n",
"Serverless architecture offers significant advantages but also presents challenges. It's essential to weigh the pros and cons carefully before adopting a Serverless approach. Despite its \"slightly awkward teenage years,\" Serverless is expected to continue evolving and maturing in the near future.\n",
"\n",
"Key Takeaways:\n",
"\n",
"1. Understand the basics of Serverless computing.\n",
"2. Be-aware of the trade-offs between scalability, control, and cost.\n",
"3. Consider your use case before adopting a Serverless architecture.\n",
"4. Stay updated with the latest developments and best practices in the Serverless community."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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",
"}\n",
"\n",
"PATH_TO_CHROME_DRIVER = \"/Users/rakeshhk/Rakesh/softwares/chromedriver-mac-arm64/chromedriver\"\n",
"\n",
"class Website:\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.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.get_text(separator=\"\\n\", strip=True)\n",
"\n",
"def messages_for(website):\n",
" return [{\"role\":\"system\", \"content\": \"You are a technology trainer, please read the content provided and highlight the key points in less than 200 words.\"},\n",
" {\"role\":\"user\", \"content\":website.text}]\n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model = MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return response.choices[0].message.content\n",
"\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))\n",
" \n",
"display_summary(\"https://martinfowler.com/articles/serverless.html\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09f59679-22ff-46c4-a736-7309a6ca4365",
"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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