import gradio as gr import pandas as pd import requests import datetime import tempfile import os import matplotlib.pyplot as plt from transformers import pipeline # Initialize Models summarizer = pipeline("summarization", model="facebook/bart-large-cnn") chat_model = pipeline("text-generation", model="google/flan-t5-large", max_length=256) # API Key POLYGON_API_KEY = os.getenv("POLYGON_API_KEY") # Sector Averages 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}, } # Tooltip dictionary tooltips = { "P/E Ratio": "Price/Earnings: Lower can indicate better value.", "P/S Ratio": "Price/Sales: Lower can indicate better value relative to sales.", "P/B Ratio": "Price/Book: Lower can indicate undervaluation.", "PEG Ratio": "Price/Earnings to Growth: Closer to 1 is ideal.", "Dividend Yield": "Annual dividend income relative to price." } # Helper Functions def safe_request(url): try: response = requests.get(url) response.raise_for_status() return response except: return None def get_company_info(symbol): url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={POLYGON_API_KEY}" response = safe_request(url) if response: data = response.json().get('results', {}) sector = data.get('market', 'Technology') # Dynamic Guess if sector.lower() == 'stocks': sector = "Technology" return { 'Name': data.get('name', 'N/A'), 'Industry': data.get('sic_description', 'N/A'), 'Sector': sector, 'Market Cap': data.get('market_cap', 0), 'Total Revenue': data.get('total_employees', 0) * 100000 } return None def get_current_price(symbol): url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={POLYGON_API_KEY}" response = safe_request(url) if response: return response.json()['results'][0]['c'] return None def get_dividends(symbol): url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={POLYGON_API_KEY}" response = safe_request(url) if response: return response.json()['results'][0].get('cash_amount', 0) return 0 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}" response = safe_request(url) if response: 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 return [], [] def calculate_ratios(market_cap, total_revenue, price, dividend_amount, eps=5.0, growth=0.1, book_value=500000000): pe = price / eps if eps else 0 ps = market_cap / total_revenue if total_revenue else 0 pb = market_cap / book_value if book_value else 0 peg = pe / (growth * 100) if growth else 0 div_yield = (dividend_amount / price) * 100 if price else 0 return { 'P/E Ratio': pe, 'P/S Ratio': ps, 'P/B Ratio': pb, 'PEG Ratio': peg, 'Dividend Yield': div_yield } def compare_to_sector(sector, ratios): if sector.lower() == 'stocks': sector = 'Technology' averages = sector_averages.get(sector, {}) if not averages: return pd.DataFrame({"Metric": ["Sector data not available"], "Value": ["N/A"]}) data = { "Ratio": [], "Stock Value": [], "Sector Average": [], "Difference": [] } for key in averages: stock_value = ratios.get(key, 0) sector_value = averages.get(key, 0) diff = stock_value - sector_value # Add emoji based on difference if diff < 0: diff_display = f"{diff:.2f} 🟢" elif diff > 0: diff_display = f"{diff:.2f} 🔴" else: diff_display = f"{diff:.2f} ⚪" data["Ratio"].append(key) data["Stock Value"].append(round(stock_value, 2)) data["Sector Average"].append(round(sector_value, 2)) data["Difference"].append(diff_display) return pd.DataFrame(data) def generate_summary(info, ratios): recommendation = "Hold" if ratios['P/E Ratio'] < 15 and ratios['P/B Ratio'] < 2 and ratios['PEG Ratio'] < 1.0 and ratios['Dividend Yield'] > 2: recommendation = "Buy" elif ratios['P/E Ratio'] > 30 and ratios['P/B Ratio'] > 5 and ratios['PEG Ratio'] > 2.0: recommendation = "Sell" report = ( f"Company Overview:\n" f"Name: {info['Name']}\n" f"Industry: {info['Industry']}\n" f"Sector: {info['Sector']}\n" f"Market Cap: ${info['Market Cap']:,.2f}\n\n" f"Financial Metrics:\n" f"P/E Ratio: {ratios['P/E Ratio']:.2f}\n" f"P/S Ratio: {ratios['P/S Ratio']:.2f}\n" f"P/B Ratio: {ratios['P/B Ratio']:.2f}\n" f"PEG Ratio: {ratios['PEG Ratio']:.2f}\n" f"Dividend Yield: {ratios['Dividend Yield']:.2f}%\n\n" f"Recommended Investment Action: {recommendation}.\n" ) return summarizer(report, max_length=250, min_length=100, do_sample=False)[0]['summary_text'] def answer_investing_question(question): prompt = f"Answer simply and encouragingly: {question}" response = chat_model(prompt)[0]['generated_text'] return response def stock_research(symbol, eps=5.0, growth=0.1, book=500000000): info = get_company_info(symbol) price = get_current_price(symbol) dividends = get_dividends(symbol) dates, prices = get_historical_prices(symbol) if not info or not price: return "⚠️ Error fetching stock info", None, None, None, None ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends, eps, growth, book) summary = generate_summary(info, ratios) sector_comp = compare_to_sector(info['Sector'], ratios) fig, ax = plt.subplots() ax.plot(dates, prices) ax.set_title(f"{symbol} Historical Price (1Y)") ax.set_xlabel("Date") ax.set_ylabel("Price ($)") ax.grid(True) info_table = pd.DataFrame(info.items(), columns=["Metric", "Value"]) ratios_table = pd.DataFrame(ratios.items(), columns=["Ratio", "Value"]) return summary, info_table, ratios_table, sector_comp, fig # --- Gradio UI --- with gr.Blocks(theme="soft") as iface: with gr.Row(): symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)") eps = gr.Number(label="Assumed EPS", value=5.0) growth = gr.Number(label="Assumed Growth Rate", value=0.1) book = gr.Number(label="Assumed Book Value", value=500000000) 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() with gr.Tab("Ask About Investing"): user_question = gr.Textbox(label="Ask about investing...") answer_box = gr.Textbox() ask_button = gr.Button("Get Answer") ask_button.click(fn=answer_investing_question, inputs=[user_question], outputs=[answer_box]) 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]) # Sector Comparison Color Highlight def style_sector(df): def highlight(val): if isinstance(val, (int, float)): if val < 0: return 'color: green' elif val > 0: return 'color: red' return '' return df.style.applymap(highlight, subset=['Difference']) output_sector.style_fn = style_sector if __name__ == "__main__": iface.launch()