From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
154 lines
5.3 KiB
154 lines
5.3 KiB
{ |
|
"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": null, |
|
"id": "2e02ac9c-7034-4aa1-9626-a7049168f096", |
|
"metadata": {}, |
|
"outputs": [], |
|
"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()" |
|
] |
|
} |
|
], |
|
"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 |
|
}
|
|
|