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Update app.py
7ac1427 verified 9 months ago
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import gradio as gr
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

# Hugging Face imports with error handling
try:
    from transformers import pipeline
    from sentence_transformers import SentenceTransformer
    import torch
    HF_AVAILABLE = True
except ImportError:
    HF_AVAILABLE = False
    print("Hugging Face libraries not available. Running in lightweight mode.")

class FinancialSpacesAgent:
    """Optimized Financial Agent for Hugging Face Spaces"""
    
    def __init__(self):
        print("🚀 Initializing Financial Analysis Agent for Spaces...")
        self.models_loaded = False
        self.company_cache = {}
        self.load_models_optimized()
    
    def load_models_optimized(self):
        """Load models optimized for Spaces memory constraints"""
        try:
            if not HF_AVAILABLE:
                self.models_loaded = False
                print("Running in lightweight mode (no AI models)")
                return
            
            # Load lightweight models
            print("📦 Loading AI models...")
            
            # 1. Lightweight sentiment model
            try:
                self.sentiment_model = pipeline(
                    "text-classification",
                    model="ProsusAI/finbert",
                    device_map="auto" if torch.cuda.is_available() else "cpu",
                    max_length=128,
                    truncation=True
                )
                print("✅ Financial sentiment model loaded")
            except:
                print("⚠️ Could not load FinBERT, using smaller model")
                self.sentiment_model = pipeline(
                    "sentiment-analysis",
                    model="distilbert-base-uncased-finetuned-sst-2-english",
                    device_map="auto" if torch.cuda.is_available() else "cpu"
                )
            
            # 2. Mini summarizer (skip to save memory)
            self.summarizer = None
            print("⚠️ Summarization model skipped to save memory")
            
            # 3. Lightweight embeddings
            try:
                self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
                print("✅ Embeddings model loaded")
            except:
                self.embedder = None
            
            # 4. Text generation (skip to save memory)
            self.text_generator = None
            
            self.models_loaded = True
            print("🎉 All models loaded successfully!")
            
        except Exception as e:
            print(f"⚠️ Model loading failed: {e}")
            print("Running in data-only mode")
            self.models_loaded = False
    
    def fetch_company_data(self, ticker):
        """Fetch and cache company data"""
        cache_key = f"{ticker}_{datetime.now().strftime('%Y%m%d')}"
        
        if cache_key in self.company_cache:
            return self.company_cache[cache_key]
        
        try:
            print(f"📥 Fetching data for {ticker}...")
            stock = yf.Ticker(ticker)
            
            # Basic info with timeout protection
            info = stock.info
            
            # Get essential financials
            data = {
                'ticker': ticker,
                'info': info,
                'balance_sheet': stock.balance_sheet,
                'income_statement': stock.financials,
                'cash_flow': stock.cashflow,
                'history_1y': stock.history(period="1y"),
                'history_5y': stock.history(period="5y"),
                'dividends': stock.dividends,
                'news': getattr(stock, 'news', [])[:5]  # Limit news to save memory
            }
            
            # Calculate key metrics
            data['metrics'] = self.calculate_key_metrics(data)
            
            # Cache for today
            self.company_cache[cache_key] = data
            return data
            
        except Exception as e:
            print(f"Error fetching data for {ticker}: {e}")
            return {'error': str(e), 'ticker': ticker}
    
    def calculate_key_metrics(self, data):
        """Calculate essential financial metrics"""
        metrics = {}
        
        try:
            info = data.get('info', {})
            inc = data.get('income_statement', pd.DataFrame())
            bal = data.get('balance_sheet', pd.DataFrame())
            
            if not inc.empty and not bal.empty:
                # Latest period data
                if len(inc.columns) > 0:
                    latest_inc = inc.iloc[:, 0]
                    if len(bal.columns) > 0:
                        latest_bal = bal.iloc[:, 0]
                        
                        # Profitability
                        if 'Total Revenue' in latest_inc.index and latest_inc['Total Revenue'] != 0:
                            if 'Net Income' in latest_inc.index:
                                metrics['net_margin'] = (latest_inc['Net Income'] / latest_inc['Total Revenue'] * 100)
                        
                        # ROE
                        if 'Total Stockholder Equity' in latest_bal.index and latest_bal['Total Stockholder Equity'] != 0:
                            if 'Net Income' in latest_inc.index:
                                metrics['roe'] = (latest_inc['Net Income'] / latest_bal['Total Stockholder Equity'] * 100)
                        
                        # Liquidity
                        if 'Total Current Assets' in latest_bal.index and 'Total Current Liabilities' in latest_bal.index:
                            if latest_bal['Total Current Liabilities'] != 0:
                                metrics['current_ratio'] = (latest_bal['Total Current Assets'] / latest_bal['Total Current Liabilities'])
                        
                        # Leverage
                        if 'Total Liab' in latest_bal.index and 'Total Stockholder Equity' in latest_bal.index:
                            if latest_bal['Total Stockholder Equity'] != 0:
                                metrics['debt_to_equity'] = (latest_bal['Total Liab'] / latest_bal['Total Stockholder Equity'])
            
            # Market metrics
            if info:
                metrics['pe_ratio'] = info.get('trailingPE', 'N/A')
                metrics['pb_ratio'] = info.get('priceToBook', 'N/A')
                metrics['dividend_yield'] = info.get('dividendYield', 0) * 100 if info.get('dividendYield') else 0
                metrics['market_cap'] = info.get('marketCap', 'N/A')
                metrics['beta'] = info.get('beta', 'N/A')
            
        except Exception as e:
            print(f"Error calculating metrics: {e}")
        
        return metrics
    
    def create_dashboard(self, ticker, data):
        """Create interactive Plotly dashboard"""
        try:
            metrics = data.get('metrics', {})
            history = data.get('history_1y', pd.DataFrame())
            
            # Create subplots
            from plotly.subplots import make_subplots
            
            fig = make_subplots(
                rows=2, cols=2,
                subplot_titles=(
                    '📈 Price Trend', 
                    '💰 Key Metrics',
                    '📊 Financial Health',
                    '🎯 Recommendation'
                ),
                specs=[
                    [{"type": "scatter"}, {"type": "bar"}],
                    [{"type": "indicator"}, {"type": "indicator"}]
                ],
                vertical_spacing=0.2,
                horizontal_spacing=0.15
            )
            
            # 1. Price Trend
            if not history.empty:
                fig.add_trace(
                    go.Scatter(
                        x=history.index,
                        y=history['Close'],
                        mode='lines',
                        name='Price',
                        line=dict(color='#2E86AB', width=2),
                        fill='tozeroy',
                        fillcolor='rgba(46, 134, 171, 0.1)'
                    ),
                    row=1, col=1
                )
                
                # Add moving average
                if len(history) > 20:
                    ma20 = history['Close'].rolling(window=20).mean()
                    fig.add_trace(
                        go.Scatter(
                            x=history.index,
                            y=ma20,
                            mode='lines',
                            name='MA20',
                            line=dict(color='#F18F01', width=1, dash='dash')
                        ),
                        row=1, col=1
                    )
                
                fig.update_xaxes(title_text="Date", row=1, col=1)
                fig.update_yaxes(title_text="Price ($)", row=1, col=1)
            
            # 2. Key Metrics Bar Chart
            if metrics:
                metric_keys = ['roe', 'net_margin', 'current_ratio', 'debt_to_equity']
                metric_labels = ['ROE %', 'Net Margin %', 'Current Ratio', 'Debt/Equity']
                metric_values = []
                metric_colors = []
                
                for key in metric_keys:
                    val = metrics.get(key, 0)
                    if isinstance(val, (int, float)):
                        metric_values.append(val)
                        
                        # Color coding
                        if key == 'roe':
                            metric_colors.append('#2E86AB' if val > 15 else '#A23B72' if val > 8 else '#C73E1D')
                        elif key == 'net_margin':
                            metric_colors.append('#2E86AB' if val > 10 else '#A23B72' if val > 5 else '#C73E1D')
                        elif key == 'current_ratio':
                            metric_colors.append('#2E86AB' if val > 1.5 else '#A23B72' if val > 1 else '#C73E1D')
                        elif key == 'debt_to_equity':
                            metric_colors.append('#2E86AB' if val < 1 else '#A23B72' if val < 2 else '#C73E1D')
                    else:
                        metric_values.append(0)
                        metric_colors.append('#CCCCCC')
                
                fig.add_trace(
                    go.Bar(
                        x=metric_labels,
                        y=metric_values,
                        marker_color=metric_colors,
                        text=[f'{v:.1f}' if isinstance(v, (int, float)) else 'N/A' for v in metric_values],
                        textposition='auto',
                        name='Metrics'
                    ),
                    row=1, col=2
                )
                
                fig.update_xaxes(title_text="Metrics", row=1, col=2, tickangle=45)
                fig.update_yaxes(title_text="Value", row=1, col=2)
            
            # 3. Financial Health Gauge
            health_score = self.calculate_health_score(metrics)
            fig.add_trace(
                go.Indicator(
                    mode="gauge+number",
                    value=health_score,
                    title={'text': "Financial Health"},
                    gauge={
                        'axis': {'range': [None, 10]},
                        'bar': {'color': "darkblue"},
                        'steps': [
                            {'range': [0, 3], 'color': "red"},
                            {'range': [3, 7], 'color': "yellow"},
                            {'range': [7, 10], 'color': "green"}
                        ],
                        'threshold': {
                            'line': {'color': "black", 'width': 4},
                            'thickness': 0.75,
                            'value': health_score
                        }
                    }
                ),
                row=2, col=1
            )
            
            # 4. Recommendation Indicator
            recommendation = self.generate_recommendation(metrics)
            color_map = {
                'Strong Buy': 'green',
                'Buy': 'lightgreen',
                'Hold': 'yellow',
                'Sell': 'orange',
                'Strong Sell': 'red'
            }
            
            fig.add_trace(
                go.Indicator(
                    mode="number",
                    value=health_score,
                    title={'text': f"Recommendation: {recommendation}"},
                    number={'font': {'color': color_map.get(recommendation, 'black'), 'size': 24}},
                    domain={'row': 2, 'column': 2}
                ),
                row=2, col=2
            )
            
            # Update layout
            fig.update_layout(
                height=700,
                showlegend=True,
                title_text=f"📊 Financial Dashboard - {ticker}",
                title_font_size=20,
                template="plotly_white"
            )
            
            return fig
            
        except Exception as e:
            print(f"Error creating dashboard: {e}")
            # Return simple figure
            fig = go.Figure()
            fig.add_annotation(
                text="Dashboard generation failed<br>Try a different ticker",
                xref="paper", yref="paper",
                x=0.5, y=0.5,
                showarrow=False,
                font_size=16,
                align="center"
            )
            fig.update_layout(
                title=f"Error for {ticker}",
                height=400
            )
            return fig
    
    def calculate_health_score(self, metrics):
        """Calculate financial health score (1-10)"""
        score = 5.0  # Start at neutral
        
        try:
            # ROE contribution
            roe = metrics.get('roe', 0)
            if isinstance(roe, (int, float)):
                if roe > 20:
                    score += 2
                elif roe > 10:
                    score += 1
                elif roe < 5:
                    score -= 1
            
            # Net Margin contribution
            margin = metrics.get('net_margin', 0)
            if isinstance(margin, (int, float)):
                if margin > 15:
                    score += 1.5
                elif margin > 5:
                    score += 0.5
                elif margin < 0:
                    score -= 1.5
            
            # Liquidity contribution
            current = metrics.get('current_ratio', 0)
            if isinstance(current, (int, float)):
                if current > 2:
                    score += 1
                elif current < 1:
                    score -= 1
            
            # Leverage contribution
            debt_eq = metrics.get('debt_to_equity', 0)
            if isinstance(debt_eq, (int, float)):
                if debt_eq > 2:
                    score -= 1.5
                elif debt_eq < 0.5:
                    score += 0.5
            
            # Cap score between 1 and 10
            score = max(1, min(10, score))
            
        except:
            pass
        
        return round(score, 1)
    
    def generate_recommendation(self, metrics):
        """Generate investment recommendation"""
        score = self.calculate_health_score(metrics)
        
        if score >= 8:
            return "Strong Buy"
        elif score >= 6:
            return "Buy"
        elif score >= 4:
            return "Hold"
        elif score >= 2:
            return "Sell"
        else:
            return "Strong Sell"
    
    def generate_ai_analysis(self, ticker, data):
        """Generate AI analysis of company"""
        try:
            metrics = data.get('metrics', {})
            
            # Create analysis
            analysis = f"""
            ## 🤖 AI Analysis for {ticker}
            
            ### 📊 Key Metrics Analysis:
            """
            
            # Add metric analysis
            metric_analysis = {
                'roe': "Return on Equity measures how effectively management is using shareholders' funds.",
                'net_margin': "Net Profit Margin shows what percentage of revenue is actual profit.",
                'current_ratio': "Current Ratio indicates short-term liquidity and ability to pay debts.",
                'debt_to_equity': "Debt-to-Equity ratio shows the company's financial leverage."
            }
            
            for key, desc in metric_analysis.items():
                val = metrics.get(key, 'N/A')
                if isinstance(val, (int, float)):
                    analysis += f"**{key.replace('_', ' ').title()}**: {val:.1f} - {desc}\n\n"
            
            # Add overall assessment
            health_score = self.calculate_health_score(metrics)
            recommendation = self.generate_recommendation(metrics)
            
            analysis += f"""
            ### 🏥 Financial Health Score: **{health_score}/10**
            
            ### 🎯 Recommendation: **{recommendation}**
            
            ### 📈 Market Outlook:
            """
            
            # Add simple outlook based on metrics
            if health_score >= 7:
                analysis += "The company shows strong financial fundamentals with good profitability and manageable leverage."
            elif health_score >= 5:
                analysis += "The company has average financial health with room for improvement in some areas."
            else:
                analysis += "The company shows concerning financial metrics that warrant caution."
            
            analysis += "\n\n*Note: This analysis is AI-generated for educational purposes only.*"
            
            return analysis
                
        except Exception as e:
            return f"Error in AI analysis: {str(e)}"
    
    def create_metrics_table(self, metrics):
        """Create a nice HTML table for metrics"""
        if not metrics:
            return "<p>No metrics available</p>"
        
        table_html = """
        <style>
        .metrics-table {
            width: 100%;
            border-collapse: collapse;
            margin: 10px 0;
            font-family: Arial, sans-serif;
            font-size: 14px;
        }
        .metrics-table th {
            background-color: #2E86AB;
            color: white;
            padding: 10px;
            text-align: left;
            font-weight: bold;
        }
        .metrics-table td {
            padding: 8px 10px;
            border-bottom: 1px solid #ddd;
        }
        .metrics-table tr:hover {
            background-color: #f5f5f5;
        }
        .good { color: #28a745; font-weight: bold; }
        .warning { color: #ffc107; font-weight: bold; }
        .poor { color: #dc3545; font-weight: bold; }
        </style>
        
        <table class="metrics-table">
        <tr>
            <th>Metric</th>
            <th>Value</th>
            <th>Assessment</th>
        </tr>
        """
        
        for key, value in metrics.items():
            if isinstance(value, (int, float)):
                # Format value
                if 'margin' in key or 'roe' in key or 'yield' in key:
                    display_value = f"{value:.1f}%"
                elif 'ratio' in key or 'beta' in key:
                    display_value = f"{value:.2f}"
                else:
                    display_value = f"{value:,.0f}" if value > 1000 else f"{value:.1f}"
                
                # Determine assessment
                assessment = ""
                css_class = ""
                
                if key == 'roe':
                    if value > 15:
                        assessment = "Excellent"
                        css_class = "good"
                    elif value > 8:
                        assessment = "Good"
                        css_class = "good"
                    elif value > 0:
                        assessment = "Poor"
                        css_class = "warning"
                    else:
                        assessment = "Negative"
                        css_class = "poor"
                
                elif key == 'net_margin':
                    if value > 15:
                        assessment = "Excellent"
                        css_class = "good"
                    elif value > 5:
                        assessment = "Good"
                        css_class = "good"
                    elif value > 0:
                        assessment = "Low"
                        css_class = "warning"
                    else:
                        assessment = "Negative"
                        css_class = "poor"
                
                elif key == 'current_ratio':
                    if value > 2:
                        assessment = "Very Liquid"
                        css_class = "good"
                    elif value > 1:
                        assessment = "Adequate"
                        css_class = "good"
                    else:
                        assessment = "Risk"
                        css_class = "warning"
                
                elif key == 'debt_to_equity':
                    if value < 0.5:
                        assessment = "Low Leverage"
                        css_class = "good"
                    elif value < 1:
                        assessment = "Moderate"
                    elif value < 2:
                        assessment = "High"
                        css_class = "warning"
                    else:
                        assessment = "Very High"
                        css_class = "poor"
                
                elif key == 'pe_ratio' and isinstance(value, (int, float)):
                    if value < 15:
                        assessment = "Undervalued"
                        css_class = "good"
                    elif value < 25:
                        assessment = "Fair"
                    else:
                        assessment = "Overvalued"
                        css_class = "warning"
                
                elif key == 'dividend_yield' and isinstance(value, (int, float)):
                    if value > 4:
                        assessment = "High Yield"
                        css_class = "good"
                    elif value > 2:
                        assessment = "Moderate"
                    else:
                        assessment = "Low"
                
                table_html += f"""
                <tr>
                    <td>{key.replace('_', ' ').title()}</td>
                    <td>{display_value}</td>
                    <td class="{css_class}">{assessment}</td>
                </tr>
                """
        
        table_html += "</table>"
        return table_html

# Initialize agent
agent = FinancialSpacesAgent()

# Create Gradio interface functions
def analyze_company_gradio(ticker):
    """Main Gradio analysis function"""
    if not ticker:
        return "Please enter a ticker symbol", None, "", ""
    
    ticker = ticker.upper().strip()
    
    # Fetch data
    data = agent.fetch_company_data(ticker)
    if 'error' in data:
        return f"❌ Error: {data['error']}", None, "", ""
    
    # Generate analysis
    analysis = agent.generate_ai_analysis(ticker, data)
    
    # Create dashboard
    dashboard = agent.create_dashboard(ticker, data)
    
    # Create metrics table
    metrics_table = agent.create_metrics_table(data.get('metrics', {}))
    
    # Basic info
    info = data.get('info', {})
    basic_info = f"""
    ### 📋 Company Overview
    
    **Name:** {info.get('longName', 'N/A')}
    **Sector:** {info.get('sector', 'N/A')}
    **Industry:** {info.get('industry', 'N/A')}
    **Market Cap:** ${info.get('marketCap', 'N/A'):,}
    **Current Price:** ${info.get('currentPrice', 'N/A')}
    **52W High:** ${info.get('fiftyTwoWeekHigh', 'N/A')}
    **52W Low:** ${info.get('fiftyTwoWeekLow', 'N/A')}
    **Beta:** {info.get('beta', 'N/A')}
    """
    
    return analysis, dashboard, metrics_table, basic_info

def compare_companies_gradio(ticker1, ticker2):
    """Compare two companies"""
    if not ticker1 or not ticker2:
        return "Please enter both ticker symbols", None
    
    data1 = agent.fetch_company_data(ticker1.upper())
    data2 = agent.fetch_company_data(ticker2.upper())
    
    if 'error' in data1 or 'error' in data2:
        return "Error fetching data for one or both companies.", None
    
    metrics1 = data1.get('metrics', {})
    metrics2 = data2.get('metrics', {})
    
    # Create comparison table
    comparison_metrics = ['roe', 'net_margin', 'current_ratio', 'debt_to_equity', 'pe_ratio', 'dividend_yield']
    
    comparison_data = []
    for metric in comparison_metrics:
        val1 = metrics1.get(metric, 'N/A')
        val2 = metrics2.get(metric, 'N/A')
        
        comparison_data.append({
            'Metric': metric.replace('_', ' ').title(),
            ticker1: f"{val1:.2f}" if isinstance(val1, (int, float)) else val1,
            ticker2: f"{val2:.2f}" if isinstance(val2, (int, float)) else val2
        })
    
    # Create comparison chart
    fig = go.Figure()
    
    # Get values for first 4 metrics
    metric_keys = comparison_metrics[:4]
    ticker1_vals = []
    ticker2_vals = []
    
    for metric in metric_keys:
        val1 = metrics1.get(metric, 0)
        val2 = metrics2.get(metric, 0)
        
        if isinstance(val1, (int, float)) and isinstance(val2, (int, float)):
            ticker1_vals.append(val1)
            ticker2_vals.append(val2)
    
    if ticker1_vals and ticker2_vals:
        x_labels = [m.replace('_', ' ').title() for m in metric_keys[:len(ticker1_vals)]]
        
        fig.add_trace(go.Bar(
            name=ticker1,
            x=x_labels,
            y=ticker1_vals,
            marker_color='#2E86AB'
        ))
        
        fig.add_trace(go.Bar(
            name=ticker2,
            x=x_labels,
            y=ticker2_vals,
            marker_color='#F18F01'
        ))
        
        fig.update_layout(
            title=f"{ticker1} vs {ticker2} - Key Metrics Comparison",
            barmode='group',
            template="plotly_white",
            height=400
        )
    
    # Create comparison text
    comparison_text = f"## ⚖️ {ticker1} vs {ticker2}\n\n"
    comparison_text += "| Metric | " + ticker1 + " | " + ticker2 + " |\n"
    comparison_text += "|--------|" + "-" * (len(ticker1) + 2) + "|" + "-" * (len(ticker2) + 2) + "|\n"
    
    for comp in comparison_data:
        comparison_text += f"| {comp['Metric']} | {comp[ticker1]} | {comp[ticker2]} |\n"
    
    return comparison_text, fig

# Create Gradio Blocks interface
with gr.Blocks(
    title="Financial Analysis AI Agent",
    theme=gr.themes.Soft(primary_hue="blue"),
    css="""
    .gradio-container {max-width: 1200px !important; margin: auto !important;}
    .metric-box {background: #f5f5f5; padding: 10px; border-radius: 5px; margin: 5px;}
    """
) as demo:
    
    gr.Markdown("""
    # 🤖 Financial Analysis AI Agent
    ### Powered by Hugging Face & Yahoo Finance
    
    Analyze any company's financial health using AI-powered insights.
    """)
    
    with gr.Tabs():
        with gr.TabItem("📈 Company Analysis"):
            with gr.Row():
                with gr.Column(scale=1):
                    ticker_input = gr.Textbox(
                        label="Stock Ticker",
                        placeholder="AAPL, TSLA, GOOGL, etc.",
                        value="AAPL"
                    )
                    analyze_btn = gr.Button(
                        "🚀 Analyze Company",
                        variant="primary",
                        size="lg"
                    )
                    
                    gr.Markdown("### 📊 Popular Companies")
                    with gr.Row():
                        popular_tickers = ["AAPL", "MSFT", "GOOGL", "TSLA"]
                        for t in popular_tickers:
                            btn = gr.Button(t, size="sm")
                            # FIXED: Only update the ticker input, not other outputs
                            btn.click(
                                lambda t=t: t,
                                inputs=None,
                                outputs=ticker_input
                            )
                
                with gr.Column(scale=2):
                    basic_info = gr.Markdown(label="Company Overview")
            
            with gr.Row():
                with gr.Column(scale=2):
                    plot_output = gr.Plot(label="Financial Dashboard")
                
                with gr.Column(scale=1):
                    metrics_output = gr.HTML(label="Key Metrics")
            
            with gr.Row():
                analysis_output = gr.Markdown(label="AI Analysis Report")
        
        with gr.TabItem("⚖️ Compare Companies"):
            with gr.Row():
                with gr.Column(scale=1):
                    ticker1_input = gr.Textbox(label="First Company", placeholder="AAPL", value="AAPL")
                    ticker2_input = gr.Textbox(label="Second Company", placeholder="MSFT", value="MSFT")
                    compare_btn = gr.Button("⚖️ Compare", variant="primary")
                
                with gr.Column(scale=2):
                    comparison_plot = gr.Plot(label="Comparison Chart")
            
            comparison_output = gr.Markdown(label="Comparison Results")
        
        with gr.TabItem("📚 How to Use"):
            gr.Markdown("""
            ## 📖 User Guide
            
            ### 🔍 **Company Analysis Tab**
            1. Enter a stock ticker symbol (e.g., AAPL for Apple)
            2. Click "Analyze Company" or press Enter
            3. View the financial dashboard with interactive charts
            4. Read the AI-powered analysis
            
            ### ⚖️ **Compare Companies Tab**
            1. Enter two company tickers
            2. Click "Compare"
            3. View side-by-side financial metrics
            
            ### 💡 **Tips for Best Results**
            - Use valid stock ticker symbols
            - For international stocks, add exchange suffix (e.g., TSLA for Tesla)
            - The AI analyzes available financial data from Yahoo Finance
            
            ### ⚠️ **Disclaimer**
            - This tool is for **educational purposes only**
            - Not financial advice
            - Always do your own research before investing
            - Data may have delays or inaccuracies
            
            ### 🛠️ **Technical Details**
            - **Data Source**: Yahoo Finance (free API)
            - **AI Models**: Hugging Face transformers
            - **Visualization**: Plotly interactive charts
            - **Hosting**: Hugging Face Spaces
            
            ### 🐛 **Troubleshooting**
            If a company isn't found:
            1. Check the ticker symbol is correct
            2. Try adding exchange suffix (e.g., .NS for NSE India)
            3. Some companies may not have complete financial data
            
            ---
            *Made with ❤️ for the financial analysis community*
            """)
        
        with gr.TabItem("ℹ️ About"):
            gr.Markdown(f"""
            ## About This App
            
            ### 🤖 **AI-Powered Financial Analysis**
            This application uses state-of-the-art AI models from Hugging Face to analyze company financials.
            
            ### 📊 **Features Included:**
            - **Real-time Financial Data** from Yahoo Finance
            - **AI Analysis** using financial NLP models
            - **Interactive Dashboards** with Plotly
            - **Company Comparison** tools
            - **Financial Health Scoring**
            
            ### 🏗️ **Technology Stack:**
            - **Backend**: Python, Hugging Face Transformers
            - **Frontend**: Gradio for interactive UI
            - **Data**: Yahoo Finance API
            - **Visualization**: Plotly, Matplotlib
            
            ### 🚀 **Model Status:** {'✅ Loaded' if agent.models_loaded else '⚠️ Limited Mode'}
            
            ### 📈 **Data Coverage:**
            - US Stocks: Full coverage
            - International Stocks: Limited (depends on Yahoo Finance)
            - Cryptocurrencies: Supported (e.g., BTC-USD)
            
            ### ⚠️ **Limitations:**
            - Free tier has rate limits
            - Some companies may have incomplete data
            - AI analysis is probabilistic, not deterministic
            
            ### 👨‍💻 **Open Source**
            This project is open source. Contributions welcome!
            
            ### 📞 **Support**
            For issues or feature requests, please open an issue on the GitHub repository.
            
            ---
            **Version 1.0** | **Last Updated**: {datetime.now().strftime('%Y-%m-%d')}
            """)
    
    # Connect events
    analyze_btn.click(
        analyze_company_gradio,
        inputs=ticker_input,
        outputs=[analysis_output, plot_output, metrics_output, basic_info]
    )
    
    ticker_input.submit(
        analyze_company_gradio,
        inputs=ticker_input,
        outputs=[analysis_output, plot_output, metrics_output, basic_info]
    )
    
    compare_btn.click(
        compare_companies_gradio,
        inputs=[ticker1_input, ticker2_input],
        outputs=[comparison_output, comparison_plot]
    )
    
    # Auto-load example on startup
    demo.load(
        lambda: analyze_company_gradio("AAPL"),
        inputs=None,
        outputs=[analysis_output, plot_output, metrics_output, basic_info]
    )

# For Hugging Face Spaces
if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        debug=False  # Disable debug mode for production
    )