diff --git a/week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb b/week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb
new file mode 100644
index 0000000..3867bbb
--- /dev/null
+++ b/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": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/week2/community-contributions/day2-openrouterAi.ipynb b/week2/community-contributions/day2-openrouterAi.ipynb
new file mode 100644
index 0000000..e05517b
--- /dev/null
+++ b/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": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " Before you read the next few cells\n",
+ " \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",
+ " \n",
+ " | \n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "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
+}