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Contribution: Adding Llama3.2 in streaming mode to enhance the Company Brochure Generator (using Gradio)

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Batikan Iscan 5 months ago
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  1. 192
      week2/community-contributions/day2-gradio-company-brochure-with-llama.ipynb

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week2/community-contributions/day2-gradio-company-brochure-with-llama.ipynb

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"cells": [
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"cell_type": "markdown",
"id": "1c14de02-8bd2-4f75-bcd8-d4f2e58e2a24",
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"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>"
]
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"metadata": {},
"output_type": "display_data"
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"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",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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