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")
    print(f"DEBUG: Using API Key: {api_key}")
    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={api_key}"
    print(f"DEBUG: Fetching company info from URL: {url}")
    try:
        response = requests.get(url)
        print(f"DEBUG: Company Info Status Code: {response.status_code}")
        print(f"DEBUG: Company Info Response: {response.text}")
        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}"
    print(f"DEBUG: Fetching current price from URL: {url}")
    try:
        response = requests.get(url)
        print(f"DEBUG: Current Price Status Code: {response.status_code}")
        print(f"DEBUG: Current Price Response: {response.text}")
        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)
        print(f"DEBUG: Historical Prices Status Code: {response.status_code}")
        print(f"DEBUG: Historical Prices Response: {response.text}")
        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 generate_summary(info, ratios):
    recommendation = "Hold"
    if ratios['P/E Ratio'] < 15 and ratios['P/B Ratio'] < 2:
        recommendation = "Buy"
    elif ratios['P/E Ratio'] > 30 and ratios['P/B Ratio'] > 5:
        recommendation = "Sell"

    prompt = (
        f"Write a professional financial analysis about the company {info['Name']}. "
        f"{info['Name']} operates in the {info['Industry']} industry within the {info['Sector']} sector. "
        f"It has a market capitalization of approximately ${info['Market Cap']:,.2f}. "
        f"The Price-to-Earnings (P/E) ratio is {ratios['P/E Ratio']:.2f}, "
        f"Price-to-Sales (P/S) ratio is {ratios['P/S Ratio']:.2f}, "
        f"Price-to-Book (P/B) ratio is {ratios['P/B Ratio']:.2f}, "
        f"PEG ratio is {ratios['PEG Ratio']:.2f}, and dividend yield is {ratios['Dividend Yield (%)']:.2f}%. "
        f"Based on these metrics, the recommended investment action is: {recommendation}."
    )
    summary = summarizer(prompt, max_length=180, min_length=80, do_sample=False)[0]['summary_text']
    return summary

# (Rest of the code remains the same)