{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import requests\n", "from dotenv import load_dotenv\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display\n", "from openai import OpenAI\n", "\n", "# If you get an error running this cell, then please head over to the troubleshooting notebook!" ] }, { "cell_type": "code", "execution_count": null, "id": "7b87cadb-d513-4303-baee-a37b6f938e4d", "metadata": {}, "outputs": [], "source": [ "# Load environment variables in a file called .env\n", "\n", "load_dotenv(override=True)\n", "api_key = os.getenv('OPENAI_API_KEY')\n", "\n", "# Check the key\n", "\n", "if not api_key:\n", " print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", "elif not api_key.startswith(\"sk-proj-\"):\n", " print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", "elif api_key.strip() != api_key:\n", " print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", "else:\n", " print(\"API key found and looks good so far!\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0d2d5441-2afe-41b9-8039-c367acd715f9", "metadata": {}, "outputs": [], "source": [ "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": null, "id": "c5e793b2-6775-426a-a139-4848291d0463", "metadata": {}, "outputs": [], "source": [ "# A class to represent a Webpage\n", "# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", "\n", "# Some websites need you to use proper headers when fetching them:\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", "class Website:\n", "\n", " def __init__(self, url):\n", " \"\"\"\n", " Create this Website object from the given url using the BeautifulSoup library\n", " \"\"\"\n", " self.url = url\n", " response = requests.get(url, headers=headers)\n", " soup = BeautifulSoup(response.content, '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)" ] }, { "cell_type": "code", "execution_count": null, "id": "7c7e0988-8f2d-4844-a847-eebec76b114a", "metadata": {}, "outputs": [], "source": [ "website = \"https://www.screener.in/company/CMSINFO/\"\n", "biz = Website(website)\n", "user_prompt = \"Give short summary of the business \" + biz.text +\" and recommend pros and cons of the business in bullet points alongwith recommendation to buy or sell\"\n", "print(user_prompt)" ] }, { "cell_type": "code", "execution_count": null, "id": "00743dac-0e70-45b7-879a-d7293a6f68a6", "metadata": {}, "outputs": [], "source": [ "# Step 1: Create your prompts\n", "website = \"https://www.screener.in/company/CMSINFO/\"\n", "biz = Website(website)\n", "\n", "system_prompt = \"You are an equity research analyst. Analyze the content of the website and give a summary of the business\"\n", "user_prompt = \"Give short summary of the business \" + biz.text +\" and recommend pros and cons of the business in bullet points alongwith recommendation to buy or sell\"\n", "\n", "# Step 2: Make the messages list\n", "\n", "messages = [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt}\n", "]\n", "# Step 3: Call OpenAI\n", "\n", "# To give you a preview -- calling OpenAI with system and user messages:\n", "\n", "response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", "# Step 4: print the result\n", "\n", "print(response.choices[0].message.content)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d9edf96e-1190-44fe-9261-405709fb39cd", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }