{ "cells": [ { "cell_type": "markdown", "id": "a98030af-fcd1-4d63-a36e-38ba053498fa", "metadata": {}, "source": [ "# Week 2 Practice Gradio by Creating Brochure\n", "\n", "- **Author**: [stoneskin](https://www.github.com/stoneskin)" ] }, { "cell_type": "markdown", "id": "1c104f45", "metadata": {}, "source": [ "## Implementation\n", "\n", "- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n", "\n", "Full code for the Week2 Gradio practice is below:" ] }, { "cell_type": "code", "execution_count": 19, "id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "API key looks good so far\n" ] } ], "source": [ "import os\n", "import json\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 gradio as gr \n", "\n", "load_dotenv(override=True)\n", "\n", "api_key = os.getenv('Open_Router_Key')\n", "if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n", " print(\"API key looks good so far\")\n", "else:\n", " print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", " \n", " \n", "openai = OpenAI(\n", " api_key=api_key,\n", " base_url=\"https://openrouter.ai/api/v1\"\n", ")\n", "\n", "MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", "MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", "MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", "MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", "MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", "MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", "MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", "MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", "MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", "MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", "\n", "\n", "\n", "\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "id": "24866034", "metadata": {}, "outputs": [], "source": [ "MODEL=MODEL_Gemini2Pro # choice the model you want to use\n", "\n", "####################\n", "system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", "and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", "Include details of company culture, customers and careers/jobs if you have the information.\"\n", "\n", "##############################\n", "link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", "You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", "such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", "link_system_prompt += \"You should respond in JSON as in this example:\"\n", "link_system_prompt += \"\"\"\n", "{\n", " \"links\": [\n", " {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", " {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", " ]\n", "}\n", "\"\"\"\n", "\n", "##############################\n", "headers = {\n", " \"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", "}\n", "\n", "##############################\n", "class Website:\n", " \"\"\"\n", " A utility class to represent a Website that we have scraped, now with links\n", " \"\"\"\n", "\n", " def __init__(self, url):\n", " self.url = url\n", " response = requests.get(url, headers=headers)\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", " if soup.body:\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", " else:\n", " self.text = \"\"\n", " links = [link.get('href') for link in soup.find_all('a')]\n", " self.links = [link for link in links if link]\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 get_links_user_prompt(website):\n", " user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", " 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", "Do not include Terms of Service, Privacy, email links.\\n\"\n", " user_prompt += \"Links (some might be relative links):\\n\"\n", " user_prompt += \"\\n\".join(website.links)\n", " return user_prompt\n", "\n", "##############################\n", "def get_links(url):\n", " website = Website(url)\n", " response = openai.chat.completions.create(\n", " model=MODEL,\n", " messages=[\n", " {\"role\": \"system\", \"content\": link_system_prompt},\n", " {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", " ],\n", " response_format={\"type\": \"json_object\"}\n", " )\n", " result = response.choices[0].message.content\n", " print(\"get_links:\", result)\n", " return json.loads(result)\n", "\n", "##############################\n", "def get_brochure_user_prompt(company_name, url):\n", " user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", " 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", " user_prompt += get_all_details(url)\n", " user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", " return user_prompt\n", "\n", "##############################\n", "def get_all_details(url):\n", " print(\"get_all_details:\", url) \n", " result = \"Landing page:\\n\"\n", " result += Website(url).get_contents()\n", " links = get_links(url)\n", " print(\"Found links:\", links)\n", " for link in links[\"links\"]:\n", " result += f\"\\n\\n{link['type']}\\n\"\n", " result += Website(link[\"url\"]).get_contents()\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "id": "82abe132", "metadata": {}, "outputs": [], "source": [ "########### modified stream brochure function for gradio ###################\n", "def stream_brochure(company_name, url):\n", " stream = openai.chat.completions.create(\n", " model=MODEL,\n", " messages=[\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", " ],\n", " stream=True\n", " )\n", " \n", "\n", " result = \"\"\n", " for chunk in stream:\n", " result += chunk.choices[0].delta.content or \"\"\n", " yield result" ] }, { "cell_type": "code", "execution_count": null, "id": "902f203b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7872\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": 18, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "get_all_details: https://mlccc.herokuapp.com/\n", "get_links: {\n", " \"links\": [\n", " {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n", " {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n", " {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n", " {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n", " {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n", " {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n", " {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n", " {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n", " ]\n", "}\n", "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" ] } ], "source": [ "view = gr.Interface(\n", " fn=stream_brochure,\n", " inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n", " outputs=[gr.Markdown(label=\"Response:\")],\n", " flagging_mode=\"never\"\n", ")\n", "view.launch()" ] } ], "metadata": { "kernelspec": { "display_name": "llms", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }