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| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| import datetime | |
| import tempfile | |
| import os | |
| 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 | |
| data["Ratio"].append(key) | |
| data["Stock Value"].append(round(stock_value, 2)) | |
| data["Sector Average"].append(round(sector_value, 2)) | |
| data["Difference"].append(round(diff, 2)) | |
| 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: ${info['Market Cap']:,.2f}\n\n" | |
| f"Financial Metrics:\n" | |
| f"P/E Ratio: {ratios['P/E Ratio']:.2f}\n" | |
| f"P/S Ratio: {ratios['P/S Ratio']:.2f}\n" | |
| f"P/B Ratio: {ratios['P/B Ratio']:.2f}\n" | |
| f"PEG Ratio: {ratios['PEG Ratio']:.2f}\n" | |
| f"Dividend Yield: {ratios['Dividend Yield']:.2f}%\n\n" | |
| f"Recommended Investment Action: {recommendation}.\n" | |
| ) | |
| return summarizer(report, max_length=250, min_length=100, do_sample=False)[0]['summary_text'] | |
| def answer_investing_question(question): | |
| prompt = ( | |
| f"Explain the following investing question clearly and simply for a beginner:\n\n" | |
| f"{question}\n\n" | |
| f"Be conversational and encouraging." | |
| ) | |
| return chat_model(prompt)[0]['generated_text'] | |
| def stock_research(symbol, eps=5.0, growth=0.1, book=500000000): | |
| info = get_company_info(symbol) | |
| price = get_current_price(symbol) | |
| dividends = get_dividends(symbol) | |
| dates, prices = get_historical_prices(symbol) | |
| if not info or not price: | |
| return "⚠️ Error fetching stock info", None, None, None, None | |
| ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends, eps, growth, book) | |
| summary = generate_summary(info, ratios) | |
| sector_comp = compare_to_sector(info['Sector'], ratios) | |
| fig, ax = plt.subplots() | |
| ax.plot(dates, prices) | |
| ax.set_title(f"{symbol} Historical Price (1Y)") | |
| ax.set_xlabel("Date") | |
| ax.set_ylabel("Price ($)") | |
| 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() | |