160 lines
5.1 KiB
Plaintext
160 lines
5.1 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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"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import requests\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\n",
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"from openai import OpenAI\n",
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"\n",
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"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
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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": "7b87cadb-d513-4303-baee-a37b6f938e4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\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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"# Check the key\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
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"elif not api_key.startswith(\"sk-proj-\"):\n",
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" 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",
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"elif api_key.strip() != api_key:\n",
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" 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",
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"else:\n",
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" print(\"API key found and looks good so far!\")\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": "0d2d5441-2afe-41b9-8039-c367acd715f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()"
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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": "c5e793b2-6775-426a-a139-4848291d0463",
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"metadata": {},
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"outputs": [],
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"source": [
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"# A class to represent a Webpage\n",
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"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
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"\n",
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"# Some websites need you to use proper headers when fetching them:\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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"class Website:\n",
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"\n",
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" def __init__(self, url):\n",
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" \"\"\"\n",
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" Create this Website object from the given url using the BeautifulSoup library\n",
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" \"\"\"\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.content, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\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)"
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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": "7c7e0988-8f2d-4844-a847-eebec76b114a",
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"metadata": {},
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"outputs": [],
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"source": [
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"website = \"https://www.screener.in/company/CMSINFO/\"\n",
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"biz = Website(website)\n",
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"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",
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"print(user_prompt)"
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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": "00743dac-0e70-45b7-879a-d7293a6f68a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 1: Create your prompts\n",
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"website = \"https://www.screener.in/company/CMSINFO/\"\n",
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"biz = Website(website)\n",
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"\n",
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"system_prompt = \"You are an equity research analyst. Analyze the content of the website and give a summary of the business\"\n",
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"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",
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"\n",
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"# Step 2: Make the messages list\n",
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"\n",
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"messages = [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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"]\n",
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"# Step 3: Call OpenAI\n",
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"\n",
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"# To give you a preview -- calling OpenAI with system and user messages:\n",
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"\n",
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"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
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"# Step 4: print the result\n",
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"\n",
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"print(response.choices[0].message.content)\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": "d9edf96e-1190-44fe-9261-405709fb39cd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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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