app.py

import pandas as pd
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
import requests
import os
from transformers import pipeline
import datetime
import tempfile

# Initialize Summarizer
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Polygon API Key
POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")

# Sector Averages (Hardcoded for now)
sector_averages = {
    "Technology": {"P/E Ratio": 25, "P/S Ratio": 5, "P/B Ratio": 6},
    "Healthcare": {"P/E Ratio": 20, "P/S Ratio": 4, "P/B Ratio": 3},
    "Financials": {"P/E Ratio": 15, "P/S Ratio": 2, "P/B Ratio": 1.5},
    "Energy": {"P/E Ratio": 12, "P/S Ratio": 1.2, "P/B Ratio": 1.3},
}

# Helper Functions
def get_company_info(symbol):
    api_key = os.getenv("POLYGON_API_KEY")
    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={api_key}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()['results']
        return {
            'Name': data.get('name', 'N/A'),
            'Industry': data.get('sic_description', 'N/A'),
            'Sector': data.get('market', 'N/A'),
            'Market Cap': data.get('market_cap', 0),
            'Total Revenue': data.get('total_employees', 0) * 100000
        }
    except Exception as e:
        print(f"DEBUG: Error fetching company info: {e}")
        return None

def get_current_price(symbol):
    url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={POLYGON_API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()['results'][0]
        return float(data['c'])
    except Exception as e:
        print(f"DEBUG: Error fetching current price: {e}")
        return None

def get_dividends(symbol):
    url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={POLYGON_API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()['results'][0]
        return {
            'Dividend Amount': data.get('cash_amount', 0),
            'Ex-Dividend Date': data.get('ex_dividend_date', 'N/A')
        }
    except Exception as e:
        print(f"DEBUG: Error fetching dividends: {e}")
        return {'Dividend Amount': 0, 'Ex-Dividend Date': 'N/A'}

def get_historical_prices(symbol):
    end = datetime.date.today()
    start = end - datetime.timedelta(days=365)
    url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/range/1/day/{start}/{end}?adjusted=true&sort=asc&apiKey={POLYGON_API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        results = response.json()['results']
        dates = [datetime.datetime.fromtimestamp(r['t']/1000) for r in results]
        prices = [r['c'] for r in results]
        return dates, prices
    except Exception as e:
        print(f"DEBUG: Error fetching historical prices: {e}")
        return [], []

def calculate_ratios(market_cap, total_revenue, price, dividend_amount, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
    pe_ratio = price / assumed_eps if assumed_eps else 0
    ps_ratio = market_cap / total_revenue if total_revenue else 0
    pb_ratio = market_cap / book_value if book_value else 0
    peg_ratio = pe_ratio / (growth_rate * 100) if growth_rate else 0
    dividend_yield = (dividend_amount / price) * 100 if price else 0
    return {
        'P/E Ratio': pe_ratio,
        'P/S Ratio': ps_ratio,
        'P/B Ratio': pb_ratio,
        'PEG Ratio': peg_ratio,
        'Dividend Yield (%)': dividend_yield
    }

def compare_to_sector(sector, ratios):
    averages = sector_averages.get(sector, None)
    if not averages:
        return pd.DataFrame({"Metric": ["Sector data not available"], "Value": ["N/A"]})

    comparison = {}
    for key in averages:
        stock_value = ratios.get(key, 0)
        sector_value = averages[key]
        comparison[key] = f"{stock_value:.2f} vs Sector Avg {sector_value:.2f}"
    return pd.DataFrame({"Ratio": list(comparison.keys()), "Comparison": list(comparison.values())})

def generate_summary(info, ratios):
    text = (f"{info['Name']} operates in the {info['Industry']} sector. It has a market capitalization of "
            f"${info['Market Cap']:,.2f}. The company exhibits a P/E ratio of {ratios['P/E Ratio']:.2f}, "
            f"P/S ratio of {ratios['P/S Ratio']:.2f}, and P/B ratio of {ratios['P/B Ratio']:.2f}. "
            f"Its dividend yield is {ratios['Dividend Yield (%)']:.2f}%. "
            f"This suggests a {'potential undervaluation' if ratios['P/E Ratio'] < 20 else 'higher valuation'} relative to the market.")
    summary = summarizer(text, max_length=120, min_length=30, do_sample=False)[0]['summary_text']
    return summary

def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
    if assumed_eps is None:
        assumed_eps = 5.0
    if growth_rate is None:
        growth_rate = 0.1
    if book_value is None:
        book_value = 500000000

    info = get_company_info(symbol)
    price = get_current_price(symbol)
    dividends = get_dividends(symbol)
    dates, prices = get_historical_prices(symbol)

    if not info or not price:
        return "Error fetching stock information.", None, None, None, None, None

    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends['Dividend Amount'], assumed_eps, growth_rate, book_value)
    summary = generate_summary(info, ratios)
    sector_comp = compare_to_sector(info['Sector'], ratios)

    fig, ax = plt.subplots()
    ax.plot(dates, prices, label=f"{symbol} Price")
    ax.set_title(f"{symbol} Historical Price (1 Year)")
    ax.set_xlabel("Date")
    ax.set_ylabel("Price ($)")
    ax.legend()
    ax.grid(True)

    info_table = pd.DataFrame({"Metric": list(info.keys()), "Value": list(info.values())})
    ratios_table = pd.DataFrame({"Ratio": list(ratios.keys()), "Value": list(ratios.values())})

    return summary, info_table, ratios_table, sector_comp, fig

def download_report(info_table, ratios_table, sector_comp, summary):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w') as f:
        info_table.to_csv(f, index=False)
        f.write("\n")
        ratios_table.to_csv(f, index=False)
        f.write("\n")
        sector_comp.to_csv(f, index=False)
        f.write("\nSummary\n")
        f.write(summary)
        file_path = f.name
    return file_path

with gr.Blocks() as iface:
    with gr.Row():
        symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)", info="Ticker symbol of the company to analyze.")
        eps = gr.Number(label="Assumed EPS", value=5.0, info="Earnings Per Share (EPS) for P/E calculation.")
        growth = gr.Number(label="Assumed Growth Rate", value=0.1, info="Expected annual growth rate for PEG.")
        book = gr.Number(label="Assumed Book Value", value=500000000, info="Total net assets for P/B calculation.")

    with gr.Tabs():
        with gr.Tab("AI Research Summary"):
            output_summary = gr.Textbox()
        with gr.Tab("Company Snapshot"):
            output_info = gr.Dataframe()
        with gr.Tab("Valuation Ratios"):
            output_ratios = gr.Dataframe()
        with gr.Tab("Sector Comparison"):
            output_sector = gr.Dataframe()
        with gr.Tab("Historical Price Chart"):
            output_chart = gr.Plot()

    submit_btn = gr.Button("Run Analysis")
    download_btn = gr.Button("Download Report")
    file_output = gr.File()

    submit_btn.click(
        fn=stock_research,
        inputs=[symbol, eps, growth, book],
        outputs=[output_summary, output_info, output_ratios, output_sector, output_chart]
    )

    download_btn.click(
        fn=download_report,
        inputs=[output_info, output_ratios, output_sector, output_summary],
        outputs=file_output
    )

if __name__ == "__main__":
    iface.launch()