Update app.py

app.py

@@ -1,71 +1,32 @@
 import gradio as gr
 import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
 import yfinance as yf
 from datetime import datetime, timedelta
 import warnings
 warnings.filterwarnings('ignore')
 
-# Set matplotlib style
-plt.style.use('default')
-
 def analyze_stock(symbol):
     """
-    Stock analysis with matplotlib charts
+    Stock analysis with safe data handling
     """
     try:
         # Download stock data
         end_date = datetime.now()
-        start_date = end_date - timedelta(days=180)  # 6 months for better charts
+        start_date = end_date - timedelta(days=180)
         
         data = yf.download(symbol, start=start_date, end=end_date, progress=False)
         
         if data.empty or len(data) < 5:
             return None, None, "❌ No data found for this symbol. Try AAPL, GOOGL, TSLA, etc."
         
-        # Create matplotlib charts
-        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
-        
-        # Chart 1: Price trend
-        ax1.plot(data.index, data['Close'], linewidth=2, color='#1f77b4')
-        ax1.set_title(f'{symbol} Stock Price Trend', fontsize=14, fontweight='bold')
-        ax1.set_ylabel('Price ($)')
-        ax1.grid(True, alpha=0.3)
-        ax1.tick_params(axis='x', rotation=45)
-        
-        # Chart 2: Daily returns distribution
-        returns = data['Close'].pct_change().dropna()
-        ax2.hist(returns, bins=50, alpha=0.7, color='#2ca02c', edgecolor='black')
-        ax2.set_title('Daily Returns Distribution', fontsize=14, fontweight='bold')
-        ax2.set_xlabel('Daily Returns')
-        ax2.set_ylabel('Frequency')
-        ax2.grid(True, alpha=0.3)
-        
-        # Chart 3: Trading volume
-        ax3.bar(data.index, data['Volume'], alpha=0.7, color='#ff7f0e')
-        ax3.set_title('Trading Volume', fontsize=14, fontweight='bold')
-        ax3.set_ylabel('Volume')
-        ax3.tick_params(axis='x', rotation=45)
-        ax3.grid(True, alpha=0.3)
-        
-        # Chart 4: Model performance comparison
-        models = ['Naive', 'LSTM', 'ARIMA', 'Prophet']
-        rmse_scores = [1.77, 6.44, 6.65, 58.52]
-        colors = ['#2ca02c', '#ff7f0e', '#1f77b4', '#d62728']
-        
-        bars = ax4.bar(models, rmse_scores, color=colors, alpha=0.8)
-        ax4.set_title('Model Performance (RMSE)', fontsize=14, fontweight='bold')
-        ax4.set_ylabel('RMSE Score')
-        ax4.tick_params(axis='x', rotation=45)
-        ax4.grid(True, alpha=0.3)
-        
-        # Add value labels on bars
-        for bar, value in zip(bars, rmse_scores):
-            height = bar.get_height()
-            ax4.text(bar.get_x() + bar.get_width()/2, height + 1, 
-                    f'{value}', ha='center', va='bottom', fontweight='bold')
-        
+        # Create simple chart
+        fig, ax = plt.subplots(figsize=(10, 5))
+        ax.plot(data.index, data['Close'], linewidth=2, color='blue')
+        ax.set_title(f'{symbol} Stock Price', fontsize=14, fontweight='bold')
+        ax.set_ylabel('Price ($)')
+        ax.grid(True, alpha=0.3)
+        ax.tick_params(axis='x', rotation=45)
         plt.tight_layout()
         
         # Create performance summary
@@ -78,17 +39,11 @@ def analyze_stock(symbol):
         }
         performance_df = pd.DataFrame(performance_data)
         
-        # Extract values properly - handle both Series and scalar values
-        current_price = float(data['Close'].iloc[-1]) if hasattr(data['Close'].iloc[-1], 'item') else data['Close'].iloc[-1]
-        start_price = float(data['Close'].iloc[0]) if hasattr(data['Close'].iloc[0], 'item') else data['Close'].iloc[0]
-        high_price = float(data['Close'].max()) if hasattr(data['Close'].max(), 'item') else data['Close'].max()
-        low_price = float(data['Close'].min()) if hasattr(data['Close'].min(), 'item') else data['Close'].min()
-        
-        # Calculate volatility safely
-        if len(returns) > 0:
-            volatility = float(returns.std()) * 100
-        else:
-            volatility = 0.0
+        # Extract values safely - convert to native Python types
+        current_price = float(data['Close'].iloc[-1])
+        start_price = float(data['Close'].iloc[0])
+        high_price = float(data['Close'].max())
+        low_price = float(data['Close'].min())
         
         total_return = ((current_price / start_price) - 1) * 100
         price_change = current_price - start_price
@@ -101,13 +56,12 @@ def analyze_stock(symbol):
         - **Price Change**: ${price_change:+.2f} ({total_return:+.2f}%)
         - **Period High**: ${high_price:.2f}
         - **Period Low**: ${low_price:.2f}
-        - **Volatility**: {volatility:.2f}%
         - **Data Points**: {len(data)} trading days
         
         ## 🎯 Model Performance
         - **🏆 Best Model**: Naive (Baseline)
-        - **💡 Key Insight**: Simple models often outperform complex ones in efficient markets
-        - **📈 Recommendation**: Use ensemble methods for better accuracy
+        - **💡 Key Insight**: Simple models often outperform complex ones
+        - **📈 Recommendation**: Use ensemble methods
         
         **Analysis Period**: {data.index.min().strftime('%Y-%m-%d')} to {data.index.max().strftime('%Y-%m-%d')}
         """
@@ -115,7 +69,7 @@ def analyze_stock(symbol):
         return fig, performance_df, stats_text
         
     except Exception as e:
-        error_msg = f"❌ Error: {str(e)}\n\n💡 Try a different stock symbol like AAPL, TSLA, or GOOGL"
+        error_msg = f"❌ Error: {str(e)}\n\n💡 Try symbols like: AAPL, TSLA, GOOGL, MSFT"
         return None, None, error_msg
 
 # Create Gradio interface
@@ -138,15 +92,14 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
             analyze_btn = gr.Button("🚀 Analyze Stock", variant="primary", size="lg")
         
         with gr.Column():
-            output_plot = gr.Plot(label="📊 Analysis Charts")
+            output_plot = gr.Plot(label="📊 Price Chart")
     
     with gr.Row():
         output_stats = gr.Markdown(label="📈 Analysis Summary")
     
     output_table = gr.Dataframe(
         label="🎯 Model Performance Comparison",
-        headers=["Model", "RMSE", "MAE", "MAPE (%)", "Status"],
-        datatype=["str", "number", "number", "number", "str"]
+        headers=["Model", "RMSE", "MAE", "MAPE (%)", "Status"]
     )
     
     # Examples section
@@ -156,11 +109,9 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
             ["AAPL"],
             ["GOOGL"], 
             ["TSLA"],
-            ["MSFT"],
-            ["AMZN"]
+            ["MSFT"]
         ],
-        inputs=[symbol_input],
-        label="Click any example to load it"
+        inputs=[symbol_input]
     )
     
     # Footer
@@ -174,7 +125,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
     - **Prophet** (Facebook's Model)
     - **Naive** (Baseline)
     
-    **Key Finding:** The Naive model (simplest approach) outperformed all complex models, demonstrating that in efficient markets, simple models often generalize better.
+    **Key Finding:** Simple models often outperform complex ones in efficient markets.
     
     **Deployment:** Hugging Face Spaces + Gradio
     """)