diff --git a/week2/community-contributions/day2-gradio-company-brochure-with-llama.ipynb b/week2/community-contributions/day2-gradio-company-brochure-with-llama.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1c14de02-8bd2-4f75-bcd8-d4f2e58e2a24",
+ "metadata": {},
+ "source": [
+ "# Hi everyone\n",
+ "I wanted to be able to use Llama3.2 in streaming mode with all the other paid frontier models, so as a demonstration, here's the Company Brochure Generator with Gradio, enhanched with Llama3.2 (using ollama library)!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2e02ac9c-7034-4aa1-9626-a7049168f096",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "* Running on local URL: http://127.0.0.1:7875\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": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "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 ollama\n",
+ "import gradio as gr\n",
+ "\n",
+ "load_dotenv()\n",
+ "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+ "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
+ "\n",
+ "openai = OpenAI()\n",
+ "claude = anthropic.Anthropic()\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.\"\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\"\n",
+ "\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\n",
+ "\n",
+ "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\n",
+ "\n",
+ "def stream_llama(prompt):\n",
+ " messages = [\n",
+ " {\"role\": \"user\", \"content\": prompt}\n",
+ " ]\n",
+ " response = \"\"\n",
+ " for chunk in ollama.chat(\n",
+ " model=\"llama3.2\", \n",
+ " messages=messages, \n",
+ " stream=True\n",
+ " ):\n",
+ " # Check if the chunk contains text\n",
+ " if chunk.get('message', {}).get('content'):\n",
+ " # Append the new text to the response\n",
+ " response += chunk['message']['content']\n",
+ " # Yield the incrementally built response\n",
+ " yield response\n",
+ "\n",
+ "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",
+ " elif model==\"Llama\":\n",
+ " result = stream_llama(prompt)\n",
+ " else:\n",
+ " raise ValueError(\"Unknown model\")\n",
+ " yield from result\n",
+ "\n",
+ "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\", \"Llama\"], label=\"Select model\")],\n",
+ " outputs=[gr.Markdown(label=\"Brochure:\")],\n",
+ " flagging_mode=\"never\"\n",
+ ")\n",
+ "view.launch()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72809781-98f7-4bd4-a2e7-72a005f4513d",
+ "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",
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+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
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