import os
import requests
import gradio as gr
import openai

# 🔐 Load secrets from environment
client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
STOCK_API_KEY = os.getenv("STOCK_API_KEY")

# 🔍 Get stock symbol from company name
def get_stock_symbol(company_name):
    url = 'https://www.alphavantage.co/query'
    params = {
        'function': 'SYMBOL_SEARCH',
        'keywords': company_name,
        'apikey': STOCK_API_KEY
    }
    r = requests.get(url, params=params).json()
    try:
        return r['bestMatches'][0]['1. symbol']
    except (KeyError, IndexError):
        return None

# 📈 Get real-time stock price and last trading date
def get_stock_quote(symbol):
    url = 'https://www.alphavantage.co/query'
    params = {
        'function': 'GLOBAL_QUOTE',
        'symbol': symbol,
        'apikey': STOCK_API_KEY
    }
    r = requests.get(url, params=params).json()
    try:
        quote = r['Global Quote']
        return quote['05. price'], quote['07. latest trading day']
    except KeyError:
        return None, None

# 🧾 Get company financial info (overview)
def get_financial_overview(symbol):
    url = 'https://www.alphavantage.co/query'
    params = {
        'function': 'OVERVIEW',
        'symbol': symbol,
        'apikey': STOCK_API_KEY
    }
    r = requests.get(url, params=params).json()
    try:
        return {
            "P/E Ratio": r.get("PERatio", "N/A"),
            "EPS": r.get("EPS", "N/A"),
            "Market Cap": r.get("MarketCapitalization", "N/A"),
            "Description": r.get("Description", "No description available.")
        }
    except KeyError:
        return None

# 💬 Ask GPT for investment insight
def ask_gpt_summary(company_name, financial_data):
    prompt = (
        f"Provide a short summary and investment insight for {company_name}.\n"
        f"Here is some financial data:\n"
        f"P/E Ratio: {financial_data['P/E Ratio']}\n"
        f"EPS: {financial_data['EPS']}\n"
        f"Market Cap: {financial_data['Market Cap']}"
    )
    try:
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a stock market assistant providing brief insights."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=250,
            temperature=0.3
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"GPT Error: {e}"

# 🎯 Main function
def stock_insight(company_name):
    symbol = get_stock_symbol(company_name)
    if not symbol:
        return f"❌ Could not find stock symbol for '{company_name}'.", "", "", "", "", ""

    price, last_trade = get_stock_quote(symbol)
    if not price:
        return f"❌ Could not fetch price for symbol {symbol}.", "", "", "", "", ""

    overview = get_financial_overview(symbol)
    if not overview:
        return f"❌ Could not fetch financial data for {symbol}.", "", "", "", "", ""

    summary = ask_gpt_summary(company_name, overview)

    return (
        f"✅ Symbol: {symbol}",
        f"💲 Price: ${price}",
        f"🗓️ Last Trading Day: {last_trade}",
        f"📊 P/E Ratio: {overview['P/E Ratio']}\nEPS: {overview['EPS']}\nMarket Cap: ${overview['Market Cap']}",
        f"📝 Company Summary: {overview['Description'][:300]}...",
        f"🤖 GPT Insight: {summary}"
    )

# 🖥️ Gradio Interface
demo = gr.Interface(
    fn=stock_insight,
    inputs=gr.Textbox(label="Enter Company Name"),
    outputs=[
        gr.Textbox(label="Symbol"),
        gr.Textbox(label="Stock Price"),
        gr.Textbox(label="Last Trading Day"),
        gr.Textbox(label="Financial Overview"),
        gr.Textbox(label="Company Summary"),
        gr.Textbox(label="AI Insight")
    ],
    title="📈 Stock Market Assistant",
    description="Search by company name to get stock symbol, live price, financial info, last trade date, and AI-based insight."
)

demo.launch()