209 lines
9.0 KiB
Plaintext
209 lines
9.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"import json\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 bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display, update_display, clear_output\n",
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"import openai\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"if api_key and api_key.startswith('sk-proj-') 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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"MODEL = 'gpt-4o-mini'\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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"# Prompt user for company name and URL\n",
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"company_name = input(\"Enter the company name: \")\n",
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"url = input(\"Enter the company URL: \")\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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"# multi-shot prompt\n",
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"link_system_prompt = \"You are provided with a list of links found on a webpage. \\You are able to decide which of the links would be most relevant to include in a brochure about the company, \\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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" EXAMPLE 1:\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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" EXAMPLE 2:\n",
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" {\n",
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" \"links\": [\n",
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" {\"type\": \"company blog\", \"url\": \"https://blog.example.com\"},\n",
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" {\"type\": \"our story\", \"url\": \"https://example.com/our-story\"}\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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"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. \\ 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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" return json.loads(result)\n",
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"\n",
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"def 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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"\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\n",
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"\n",
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"# set format to json_object\n",
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"system_prompt = (\n",
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" \"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 tempered, irritated, disappointed in the world type of brochure about the company for prospective customers, investors, and recruits. \"\n",
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" \"Respond in markdown. Include details of company culture, customers, and careers/jobs if you have the information. Add emoticons where ever possible.\\n\\n\"\n",
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"\n",
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" \"Please structure the brochure using the following sections:\\n\"\n",
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" \"1. **Introduction**: A brief overview of the company.\\n\"\n",
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" \"2. **Company Culture**: Emphasize fun, atmosphere, and any unique cultural elements.\\n\"\n",
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" \"3. **Customers**: Mention notable customers or industries.\\n\"\n",
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" \"4. **Careers/Jobs**: Highlight career opportunities.\\n\"\n",
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" \"5. **Conclusion**: Wrap up with a final lighthearted message.\\n\"\n",
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" \"6. Finish the brochure with a very sarcastic and pun-intended mission statement.\\n\"\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[:20_000]\n",
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" return user_prompt\n",
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"\n",
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"def stream_brochure():\n",
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" global brochure_text # Access the global variable\n",
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" brochure_text = \"\" # Initialize\n",
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" \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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" response = \"\"\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" for chunk in stream: \n",
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" content = chunk.choices[0].delta.content or ''\n",
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" response += content\n",
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" brochure_text += content # Accumulate the text\n",
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" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
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" update_display(Markdown(response), display_id=display_handle.display_id)\n",
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"\n",
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"def user_translate_brochure(lang):\n",
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" # Clear previous output\n",
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" clear_output(wait=True)\n",
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" \n",
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" # Stream #2: translate accumulated text\n",
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" translation_stream = openai.chat.completions.create( # Changed from ChatCompletion\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"user\", \"content\": f\"Translate the following to {lang}:\\n\\n{brochure_text}\"}\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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" # Setup display for streaming translation\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" translated_text = \"\"\n",
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" \n",
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" for chunk in translation_stream:\n",
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" content = chunk.choices[0].delta.content or \"\"\n",
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" if content:\n",
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" translated_text += content\n",
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" update_display(Markdown(translated_text), display_id=display_handle.display_id)\n",
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"\n",
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"# stream the brochure in english\n",
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"stream_brochure()\n",
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"\n",
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"# prompt user for language choice\n",
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"language_choice = input(\"Enter the language to translate the brochure into (e.g., 'French'): \")\n",
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"\n",
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"# translate the brochure and stream the translation\n",
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"user_translate_brochure(language_choice)"
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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": 4
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}
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