Update app.py

app.py

@@ -57,19 +57,9 @@ class FinancialSpacesAgent:
                     device_map="auto" if torch.cuda.is_available() else "cpu"
                 )
             
-            # 2. Mini summarizer
-            try:
-                self.summarizer = pipeline(
-                    "summarization",
-                    model="sshleifer/distilbart-cnn-12-6",
-                    device_map="auto" if torch.cuda.is_available() else "cpu",
-                    max_length=100,
-                    min_length=30
-                )
-                print("✅ Summarization model loaded")
-            except:
-                self.summarizer = None
-                print("⚠️ Summarization model skipped")
+            # 2. Mini summarizer (skip to save memory)
+            self.summarizer = None
+            print("⚠️ Summarization model skipped to save memory")
             
             # 3. Lightweight embeddings
             try:
@@ -78,8 +68,8 @@ class FinancialSpacesAgent:
             except:
                 self.embedder = None
             
-            # 4. Text generation (optional - memory intensive)
-            self.text_generator = None  # Skip to save memory
+            # 4. Text generation (skip to save memory)
+            self.text_generator = None
             
             self.models_loaded = True
             print("🎉 All models loaded successfully!")
@@ -113,7 +103,7 @@ class FinancialSpacesAgent:
                 'history_1y': stock.history(period="1y"),
                 'history_5y': stock.history(period="5y"),
                 'dividends': stock.dividends,
-                'news': getattr(stock, 'news', [])[:10]  # Limit news
+                'news': getattr(stock, 'news', [])[:5]  # Limit news to save memory
             }
             
             # Calculate key metrics
@@ -184,24 +174,21 @@ class FinancialSpacesAgent:
             
             # Create subplots
             from plotly.subplots import make_subplots
-            import plotly.graph_objects as go
             
             fig = make_subplots(
-                rows=2, cols=3,
+                rows=2, cols=2,
                 subplot_titles=(
                     '📈 Price Trend', 
                     '💰 Key Metrics',
                     '📊 Financial Health',
-                    '📰 News Sentiment',
-                    '📅 Historical Performance',
                     '🎯 Recommendation'
                 ),
                 specs=[
-                    [{"type": "scatter"}, {"type": "bar"}, {"type": "indicator"}],
-                    [{"type": "bar"}, {"type": "scatter"}, {"type": "indicator"}]
+                    [{"type": "scatter"}, {"type": "bar"}],
+                    [{"type": "indicator"}, {"type": "indicator"}]
                 ],
-                vertical_spacing=0.15,
-                horizontal_spacing=0.1
+                vertical_spacing=0.2,
+                horizontal_spacing=0.15
             )
             
             # 1. Price Trend
@@ -232,6 +219,9 @@ class FinancialSpacesAgent:
                         ),
                         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:
@@ -242,29 +232,36 @@ class FinancialSpacesAgent:
                 
                 for key in metric_keys:
                     val = metrics.get(key, 0)
-                    metric_values.append(val if isinstance(val, (int, float)) else 0)
-                    
-                    # Color coding
-                    if key == 'roe':
-                        metric_colors.append('#2E86AB' if val > 15 else '#A23B72')
-                    elif key == 'net_margin':
-                        metric_colors.append('#2E86AB' if val > 10 else '#A23B72')
-                    elif key == 'current_ratio':
-                        metric_colors.append('#2E86AB' if val > 1.5 else '#A23B72')
-                    elif key == 'debt_to_equity':
-                        metric_colors.append('#2E86AB' if val < 1 else '#A23B72')
+                    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}' for v in metric_values],
+                        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)
@@ -288,50 +285,10 @@ class FinancialSpacesAgent:
                         }
                     }
                 ),
-                row=1, col=3
+                row=2, col=1
             )
             
-            # 4. News Sentiment
-            news = data.get('news', [])
-            if news and self.models_loaded:
-                sentiments = []
-                for item in news[:5]:
-                    title = item.get('title', '')
-                    if title:
-                        try:
-                            sentiment = self.sentiment_model(title[:100])[0]
-                            sentiments.append(1 if sentiment['label'] == 'positive' else -1 if sentiment['label'] == 'negative' else 0)
-                        except:
-                            sentiments.append(0)
-                
-                if sentiments:
-                    fig.add_trace(
-                        go.Bar(
-                            x=['News 1', 'News 2', 'News 3', 'News 4', 'News 5'][:len(sentiments)],
-                            y=sentiments,
-                            marker_color=['green' if s > 0 else 'red' if s < 0 else 'gray' for s in sentiments],
-                            name='Sentiment'
-                        ),
-                        row=2, col=1
-                    )
-            
-            # 5. Historical Returns
-            if not history.empty and len(history) > 20:
-                returns = history['Close'].pct_change().dropna()
-                fig.add_trace(
-                    go.Scatter(
-                        x=returns.index,
-                        y=returns * 100,
-                        mode='lines',
-                        name='Daily Returns %',
-                        line=dict(color='#6BAA75', width=1),
-                        fill='tozeroy',
-                        fillcolor='rgba(107, 170, 117, 0.2)'
-                    ),
-                    row=2, col=2
-                )
-            
-            # 6. Recommendation Indicator
+            # 4. Recommendation Indicator
             recommendation = self.generate_recommendation(metrics)
             color_map = {
                 'Strong Buy': 'green',
@@ -343,18 +300,18 @@ class FinancialSpacesAgent:
             
             fig.add_trace(
                 go.Indicator(
-                    mode="delta",
+                    mode="number",
                     value=health_score,
                     title={'text': f"Recommendation: {recommendation}"},
-                    delta={'reference': 5, 'position': "bottom"},
-                    domain={'row': 1, 'column': 3}
+                    number={'font': {'color': color_map.get(recommendation, 'black'), 'size': 24}},
+                    domain={'row': 2, 'column': 2}
                 ),
-                row=2, col=3
+                row=2, col=2
             )
             
             # Update layout
             fig.update_layout(
-                height=800,
+                height=700,
                 showlegend=True,
                 title_text=f"📊 Financial Dashboard - {ticker}",
                 title_font_size=20,
@@ -368,11 +325,16 @@ class FinancialSpacesAgent:
             # Return simple figure
             fig = go.Figure()
             fig.add_annotation(
-                text="Dashboard generation failed",
+                text="Dashboard generation failed<br>Try a different ticker",
                 xref="paper", yref="paper",
                 x=0.5, y=0.5,
                 showarrow=False,
-                font_size=20
+                font_size=16,
+                align="center"
+            )
+            fig.update_layout(
+                title=f"Error for {ticker}",
+                height=400
             )
             return fig
     
@@ -443,90 +405,59 @@ class FinancialSpacesAgent:
     def generate_ai_analysis(self, ticker, data):
         """Generate AI analysis of company"""
         try:
-            if not self.models_loaded:
-                return self.generate_basic_analysis(ticker, data)
-            
             metrics = data.get('metrics', {})
             
-            # Create analysis prompt
-            prompt = f"""
-            Company: {ticker}
+            # Create analysis
+            analysis = f"""
+            ## 🤖 AI Analysis for {ticker}
             
-            Key Financial Metrics:
+            ### 📊 Key Metrics Analysis:
             """
             
-            for key, value in metrics.items():
-                if isinstance(value, (int, float)):
-                    prompt += f"{key.replace('_', ' ').title()}: {value:.2f}\n"
+            # 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."
+            }
             
-            prompt += "\nProvide a brief financial analysis focusing on strengths and weaknesses."
+            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"
             
-            # Use sentiment model to generate analysis
-            try:
-                analysis = self.sentiment_model(prompt[:200])[0]
-                sentiment = analysis['label']
-                confidence = analysis['score']
-                
-                if sentiment == 'positive':
-                    conclusion = f"Overall positive outlook with {confidence:.1%} confidence."
-                elif sentiment == 'negative':
-                    conclusion = f"Concerns identified with {confidence:.1%} confidence."
-                else:
-                    conclusion = f"Neutral assessment with {confidence:.1%} confidence."
-                
-                return f"""
-                ## 🤖 AI Analysis for {ticker}
-                
-                **Sentiment:** {sentiment.title()}
-                **Confidence:** {confidence:.1%}
-                
-                **Key Observations:**
-                {conclusion}
-                
-                **Financial Health Score:** {self.calculate_health_score(metrics)}/10
-                **Recommendation:** {self.generate_recommendation(metrics)}
-                
-                *Note: AI analysis is for educational purposes only.*
-                """
-                
-            except:
-                return self.generate_basic_analysis(ticker, data)
+            # 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 generate_basic_analysis(self, ticker, data):
-        """Generate basic analysis when AI models aren't available"""
-        metrics = data.get('metrics', {})
-        
-        analysis = f"""
-        ## 📊 Basic Analysis for {ticker}
-        
-        ### Key Metrics:
-        """
-        
-        for key, value in metrics.items():
-            if isinstance(value, (int, float)):
-                if 'margin' in key or 'roe' in key:
-                    analysis += f"• {key.replace('_', ' ').title()}: **{value:.1f}%**\n"
-                else:
-                    analysis += f"• {key.replace('_', ' ').title()}: **{value:.2f}**\n"
-        
-        analysis += f"""
-        
-        ### Financial Health Score: {self.calculate_health_score(metrics)}/10
-        ### Recommendation: {self.generate_recommendation(metrics)}
-        
-        ---
-        *Data Source: Yahoo Finance*
-        """
-        
-        return analysis
-    
     def create_metrics_table(self, metrics):
         """Create a nice HTML table for metrics"""
         if not metrics:
-            return "No metrics available"
+            return "<p>No metrics available</p>"
         
         table_html = """
         <style>
@@ -535,12 +466,14 @@ class FinancialSpacesAgent:
             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;
@@ -549,9 +482,9 @@ class FinancialSpacesAgent:
         .metrics-table tr:hover {
             background-color: #f5f5f5;
         }
-        .good { color: green; font-weight: bold; }
-        .warning { color: orange; font-weight: bold; }
-        .poor { color: red; font-weight: bold; }
+        .good { color: #28a745; font-weight: bold; }
+        .warning { color: #ffc107; font-weight: bold; }
+        .poor { color: #dc3545; font-weight: bold; }
         </style>
         
         <table class="metrics-table">
@@ -565,10 +498,12 @@ class FinancialSpacesAgent:
         for key, value in metrics.items():
             if isinstance(value, (int, float)):
                 # Format value
-                if 'margin' in key or 'roe' in key:
+                if 'margin' in key or 'roe' in key or 'yield' in key:
                     display_value = f"{value:.1f}%"
-                else:
+                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 = ""
@@ -581,9 +516,12 @@ class FinancialSpacesAgent:
                     elif value > 8:
                         assessment = "Good"
                         css_class = "good"
-                    else:
-                        assessment = "Needs Improvement"
+                    elif value > 0:
+                        assessment = "Poor"
                         css_class = "warning"
+                    else:
+                        assessment = "Negative"
+                        css_class = "poor"
                 
                 elif key == 'net_margin':
                     if value > 15:
@@ -591,9 +529,13 @@ class FinancialSpacesAgent:
                         css_class = "good"
                     elif value > 5:
                         assessment = "Good"
-                    else:
+                        css_class = "good"
+                    elif value > 0:
                         assessment = "Low"
                         css_class = "warning"
+                    else:
+                        assessment = "Negative"
+                        css_class = "poor"
                 
                 elif key == 'current_ratio':
                     if value > 2:
@@ -601,6 +543,7 @@ class FinancialSpacesAgent:
                         css_class = "good"
                     elif value > 1:
                         assessment = "Adequate"
+                        css_class = "good"
                     else:
                         assessment = "Risk"
                         css_class = "warning"
@@ -611,10 +554,32 @@ class FinancialSpacesAgent:
                         css_class = "good"
                     elif value < 1:
                         assessment = "Moderate"
+                    elif value < 2:
+                        assessment = "High"
+                        css_class = "warning"
                     else:
-                        assessment = "High Leverage"
+                        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>
@@ -629,7 +594,7 @@ class FinancialSpacesAgent:
 # Initialize agent
 agent = FinancialSpacesAgent()
 
-# Create Gradio interface
+# Create Gradio interface functions
 def analyze_company_gradio(ticker):
     """Main Gradio analysis function"""
     if not ticker:
@@ -663,6 +628,7 @@ def analyze_company_gradio(ticker):
     **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
@@ -682,7 +648,7 @@ def compare_companies_gradio(ticker1, ticker2):
     metrics2 = data2.get('metrics', {})
     
     # Create comparison table
-    comparison_metrics = ['roe', 'net_margin', 'current_ratio', 'debt_to_equity', 'pe_ratio']
+    comparison_metrics = ['roe', 'net_margin', 'current_ratio', 'debt_to_equity', 'pe_ratio', 'dividend_yield']
     
     comparison_data = []
     for metric in comparison_metrics:
@@ -696,36 +662,52 @@ def compare_companies_gradio(ticker1, ticker2):
         })
     
     # Create comparison chart
-    import plotly.graph_objects as go
-    
     fig = go.Figure()
     
-    for i, metric in enumerate(comparison_metrics[:4]):
+    # 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)):
-            fig.add_trace(go.Bar(
-                name=metric.replace('_', ' ').title(),
-                x=[ticker1, ticker2],
-                y=[val1, val2],
-                text=[f'{val1:.1f}', f'{val2:.1f}'],
-                textposition='auto',
-            ))
-    
-    fig.update_layout(
-        title=f"{ticker1} vs {ticker2} Comparison",
-        barmode='group',
-        template="plotly_white"
-    )
+            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 row in comparison_data:
-        comparison_text += f"| {row['Metric']} | {row[ticker1]} | {row[ticker2]} |\n"
+    for comp in comparison_data:
+        comparison_text += f"| {comp['Metric']} | {comp[ticker1]} | {comp[ticker2]} |\n"
     
     return comparison_text, fig
 
@@ -763,12 +745,14 @@ with gr.Blocks(
                     
                     gr.Markdown("### 📊 Popular Companies")
                     with gr.Row():
-                        popular_tickers = ["AAPL", "MSFT", "GOOGL", "TSLA", "AMZN", "META"]
+                        popular_tickers = ["AAPL", "MSFT", "GOOGL", "TSLA"]
                         for t in popular_tickers:
-                            gr.Button(t, size="sm").click(
-                                lambda t=t: (t, None, None, None),
+                            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, None, None, None]
+                                outputs=ticker_input
                             )
                 
                 with gr.Column(scale=2):
@@ -782,7 +766,7 @@ with gr.Blocks(
                     metrics_output = gr.HTML(label="Key Metrics")
             
             with gr.Row():
-                analysis_output = gr.Markdown(label="AI Analysis Report", elem_classes=["analysis-box"])
+                analysis_output = gr.Markdown(label="AI Analysis Report")
         
         with gr.TabItem("⚖️ Compare Companies"):
             with gr.Row():
@@ -899,7 +883,7 @@ with gr.Blocks(
         outputs=[comparison_output, comparison_plot]
     )
     
-    # Auto-load example
+    # Auto-load example on startup
     demo.load(
         lambda: analyze_company_gradio("AAPL"),
         inputs=None,
@@ -912,6 +896,6 @@ if __name__ == "__main__":
         server_name="0.0.0.0",
         server_port=7860,
         share=False,
-        favicon_path=None,
-        show_error=True
+        show_error=True,
+        debug=False  # Disable debug mode for production
     )