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| 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 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" | |
| # Much better structured prompt | |
| 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\n" | |
| f"Please provide a detailed financial analysis based on the information above." | |
| ) | |
| summary = summarizer(report, max_length=250, min_length=100, do_sample=False)[0]['summary_text'] | |
| return summary | |
| # (Rest of the code remains the same) | |
| 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 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 | |
| # Gradio UI | |
| 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() | |