Add week2 with openrouter.ai and day 2 brochure practice
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wsun
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
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"metadata": {},
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"source": [
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"# Week 2 Practice Gradio by Creating Brochure\n",
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"\n",
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"- **Author**: [stoneskin](https://www.github.com/stoneskin)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c104f45",
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"metadata": {},
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"source": [
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"## Implementation\n",
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"\n",
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"- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n",
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"\n",
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"Full code for the Week2 Gradio practice is below:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"API key looks good so far\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import json\n",
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"import requests\n",
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"from bs4 import BeautifulSoup\n",
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"from typing import List\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import gradio as gr \n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"api_key = os.getenv('Open_Router_Key')\n",
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"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n",
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" print(\"API key looks good so far\")\n",
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"else:\n",
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" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
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" \n",
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" \n",
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"openai = OpenAI(\n",
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" api_key=api_key,\n",
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" base_url=\"https://openrouter.ai/api/v1\"\n",
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")\n",
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"\n",
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"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n",
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"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n",
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"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n",
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"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n",
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"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n",
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"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n",
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"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n",
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"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n",
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"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n",
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"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "24866034",
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL=MODEL_Gemini2Pro # choice the model you want to use\n",
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"\n",
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"####################\n",
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"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
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"and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
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"Include details of company culture, customers and careers/jobs if you have the information.\"\n",
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"\n",
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"##############################\n",
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"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
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"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
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"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
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"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
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"link_system_prompt += \"\"\"\n",
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"{\n",
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" \"links\": [\n",
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" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
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" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
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" ]\n",
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"}\n",
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"\"\"\"\n",
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"\n",
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"##############################\n",
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"headers = {\n",
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" \"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",
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"}\n",
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"\n",
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"##############################\n",
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"class Website:\n",
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" \"\"\"\n",
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" A utility class to represent a Website that we have scraped, now with links\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, url):\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" self.body = response.content\n",
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" soup = BeautifulSoup(self.body, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" if soup.body:\n",
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
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" irrelevant.decompose()\n",
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
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" else:\n",
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" self.text = \"\"\n",
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" links = [link.get('href') for link in soup.find_all('a')]\n",
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" self.links = [link for link in links if link]\n",
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"\n",
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" def get_contents(self):\n",
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" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n",
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" \n",
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"##############################\n",
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"def get_links_user_prompt(website):\n",
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" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
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" 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",
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"Do not include Terms of Service, Privacy, email links.\\n\"\n",
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" user_prompt += \"Links (some might be relative links):\\n\"\n",
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" user_prompt += \"\\n\".join(website.links)\n",
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" return user_prompt\n",
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"\n",
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"##############################\n",
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"def get_links(url):\n",
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" website = Website(url)\n",
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" response = openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": link_system_prompt},\n",
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" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
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" ],\n",
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" response_format={\"type\": \"json_object\"}\n",
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" )\n",
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" result = response.choices[0].message.content\n",
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" print(\"get_links:\", result)\n",
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" return json.loads(result)\n",
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"\n",
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"##############################\n",
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"def get_brochure_user_prompt(company_name, url):\n",
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" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
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" 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",
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" user_prompt += get_all_details(url)\n",
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" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
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" return user_prompt\n",
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"\n",
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"##############################\n",
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"def get_all_details(url):\n",
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" print(\"get_all_details:\", url) \n",
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" result = \"Landing page:\\n\"\n",
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" result += Website(url).get_contents()\n",
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" links = get_links(url)\n",
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" print(\"Found links:\", links)\n",
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" for link in links[\"links\"]:\n",
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" result += f\"\\n\\n{link['type']}\\n\"\n",
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" result += Website(link[\"url\"]).get_contents()\n",
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" return result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "82abe132",
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"metadata": {},
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"outputs": [],
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"source": [
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"########### modified stream brochure function for gradio ###################\n",
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"def stream_brochure(company_name, url):\n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" \n",
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"\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk.choices[0].delta.content or \"\"\n",
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" yield result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "902f203b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"* Running on local URL: http://127.0.0.1:7872\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": []
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"get_all_details: https://mlccc.herokuapp.com/\n",
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"get_links: {\n",
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" \"links\": [\n",
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" {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n",
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" {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n",
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" {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n",
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" {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n",
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" {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n",
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" {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n",
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" {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n",
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" {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n",
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" ]\n",
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"}\n",
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"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"
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]
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}
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],
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"source": [
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"view = gr.Interface(\n",
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" fn=stream_brochure,\n",
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" inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n",
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" outputs=[gr.Markdown(label=\"Response:\")],\n",
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" flagging_mode=\"never\"\n",
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")\n",
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"view.launch()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llms",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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