{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "922bb144",
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870bdcd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables in a file called .env\n",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
    "\n",
    "# Check the key\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!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6146102",
   "metadata": {},
   "outputs": [],
   "source": [
    "openai = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f75573f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FinvizWebsite():\n",
    "    \"\"\"\n",
    "    Create this Website object from the given url using the BeautifulSoup library\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, ticker):\n",
    "        self.ticker = ticker.upper()\n",
    "        self.url = f\"https://finviz.com/quote.ashx?t={self.ticker}&p=d&ty=ea\"\n",
    "        self.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",
    "        response = requests.get(self.url, headers=self.headers)\n",
    "        soup = BeautifulSoup(response.content, \"html.parser\")\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        self.table = soup.find(\"table\", class_=\"snapshot-table2\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42c7ced6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def messages_for(website):\n",
    "    system_prompt = \"\"\"\n",
    "        You are a financial analysis assistant that analyzes the contents of HTML formated table.\n",
    "    and provides a summary of the stock's analysis with clear and professional language appropriate for financial research \n",
    "    with bulleted important list of **pros** and **cons** , ignoring text that might be navigation related. Repond in markdown.\n",
    "    \"\"\"\n",
    "    \n",
    "    user_prompt = f\"\"\"\n",
    "        You are looking at a website titled {website.title}.\\n\n",
    "        The contents of this website is as follows; please provide a summary of the stock's analysis from this website in markdown.\\n\\n\n",
    "        {website.table}\n",
    "    \"\"\"\n",
    "    \n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt}\n",
    "    ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bfaa6da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_summary(ticker):\n",
    "    website = FinvizWebsite(ticker)\n",
    "    response = openai.chat.completions.create(\n",
    "        model = \"gpt-4o-mini\",\n",
    "        messages = messages_for(website)\n",
    "    )\n",
    "    summary = response.choices[0].message.content\n",
    "    display(Markdown(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeeff6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"aapl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aed2001",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"tsla\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}