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": [
+       "<div><iframe src=\"http://127.0.0.1:7875/\" 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": 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",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}