Create app.py

app.py

@@ -0,0 +1,138 @@
+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
+
+# Initialize Summarizer
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Polygon API Key
+POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
+
+# Helper Functions
+def get_company_info(symbol):
+    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={POLYGON_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'),
+            'Market Cap': data.get('market_cap', 0),
+            'Total Revenue': data.get('total_employees', 0) * 100000  # Rough estimation
+        }
+    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 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, 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
+    return {
+        'P/E Ratio': pe_ratio,
+        'P/S Ratio': ps_ratio,
+        'P/B Ratio': pb_ratio,
+        'PEG Ratio': peg_ratio
+    }
+
+def generate_summary(info, ratios):
+    text = (f"{info['Name']} operates in the {info['Industry']} sector. It has a market capitalization of "
+            f"${info['Market Cap']:,.2f}. The company exhibits a P/E ratio of {ratios['P/E Ratio']:.2f}, "
+            f"P/S ratio of {ratios['P/S Ratio']:.2f}, and P/B ratio of {ratios['P/B Ratio']:.2f}. "
+            f"This suggests a {'potential undervaluation' if ratios['P/E Ratio'] < 20 else 'higher valuation'} relative to the market.")
+    summary = summarizer(text, max_length=120, min_length=30, do_sample=False)[0]['summary_text']
+    return summary
+
+def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
+    info = get_company_info(symbol)
+    price = get_current_price(symbol)
+    dividends = get_dividends(symbol)
+    dates, prices = get_historical_prices(symbol)
+
+    if not info or not price:
+        return "Error fetching stock information.", None, None, None
+
+    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, assumed_eps, growth_rate, book_value)
+    summary = generate_summary(info, ratios)
+
+    # Create historical price chart
+    fig, ax = plt.subplots()
+    ax.plot(dates, prices, label=f"{symbol} Price")
+    ax.set_title(f"{symbol} Historical Price (1 Year)")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("Price ($)")
+    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, fig
+
+iface = gr.Interface(
+    fn=stock_research,
+    inputs=[
+        gr.Textbox(label="Stock Symbol (e.g., AAPL)"),
+        gr.Number(label="Assumed EPS (default 5.0)"),
+        gr.Number(label="Assumed Growth Rate (e.g., 0.1 for 10%)"),
+        gr.Number(label="Assumed Book Value ($, default 500M)")
+    ],
+    outputs=[
+        gr.Textbox(label="AI Research Summary"),
+        gr.Dataframe(label="Company Snapshot"),
+        gr.Dataframe(label="Valuation Ratios"),
+        gr.Plot(label="Historical Price Chart")
+    ],
+    title="AI-Powered Stock Researcher",
+    description="Enter a stock symbol to get company info, valuation ratios, a 1-year price chart, and an AI-generated research summary based on live Polygon.io data."
+)
+
+if __name__ == "__main__":
+    iface.launch()