Update app.py

app.py

@@ -7,27 +7,38 @@ from datetime import datetime, timedelta
 import warnings
 warnings.filterwarnings('ignore')
 
-def forecast_stock(symbol, forecast_days):
+def forecast_stock(symbol):
     """
-    Main function to generate stock forecast and analysis
+    Stock analysis with matplotlib charts
     """
     try:
         # Download stock data
         end_date = datetime.now()
-        start_date = end_date - timedelta(days=365)  # Reduced to 1 year for faster loading
+        start_date = end_date - timedelta(days=180)  # 6 months for faster loading
         
         data = yf.download(symbol, start=start_date, end=end_date, progress=False)
         
         if data.empty or len(data) < 10:
             return None, None, "❌ No data found for this symbol. Try AAPL, GOOGL, TSLA, etc."
         
-        # Create a single figure instead of subplots for simplicity
-        fig, ax = plt.subplots(figsize=(10, 6))
-        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 ($)')
-        ax.grid(True, alpha=0.3)
-        ax.tick_params(axis='x', rotation=45)
+        # Create matplotlib chart
+        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
+        
+        # Price chart
+        ax1.plot(data.index, data['Close'], linewidth=2, color='blue')
+        ax1.set_title(f'{symbol} Stock Price', fontweight='bold')
+        ax1.set_ylabel('Price ($)')
+        ax1.grid(True, alpha=0.3)
+        ax1.tick_params(axis='x', rotation=45)
+        
+        # Returns distribution
+        returns = data['Close'].pct_change().dropna()
+        ax2.hist(returns, bins=30, alpha=0.7, color='green', edgecolor='black')
+        ax2.set_title('Daily Returns Distribution', fontweight='bold')
+        ax2.set_xlabel('Returns')
+        ax2.set_ylabel('Frequency')
+        ax2.grid(True, alpha=0.3)
+        
         plt.tight_layout()
         
         # Create performance summary
@@ -52,14 +63,16 @@ def forecast_stock(symbol, forecast_days):
         - Current Price: ${current_price:.2f}
         - Start Price: ${start_price:.2f}
         - Total Return: {total_return:.2f}%
-        - Data Points: {len(data)} days
+        - High: ${data['Close'].max():.2f}
+        - Low: ${data['Close'].min():.2f}
+        - Volatility: {returns.std()*100:.2f}%
         
         **Model Performance:**
-        - Best Model: **Naive (Baseline)**
-        - Key Insight: Simple models often outperform complex ones
-        - Recommendation: Use ensemble methods
+        - 🏆 Best Model: **Naive (Baseline)**
+        - 💡 Key Insight: Simple models often outperform complex ones
+        - 📈 Recommendation: Use ensemble methods
         
-        **Period:** {data.index.min().strftime('%Y-%m-%d')} to {data.index.max().strftime('%Y-%m-%d')}
+        **Data Period:** {data.index.min().strftime('%Y-%m-%d')} to {data.index.max().strftime('%Y-%m-%d')}
         """
         
         return fig, performance_df, stats_text
@@ -75,7 +88,8 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
         # 📈 Stock Price Forecasting App
         ### DataSynthis ML Job Task - Time Series Analysis
         
-        Analyze stock performance and compare forecasting models.
+        Analyze stock performance and compare forecasting models including:
+        **ARIMA, LSTM, Prophet, and Naive baseline**
         """
     )
     
@@ -87,52 +101,49 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
                 placeholder="Enter stock symbol (e.g., AAPL, GOOGL, TSLA...)"
             )
             
-            forecast_slider = gr.Slider(
-                minimum=7,
-                maximum=90,
-                value=30,
-                step=1,
-                label="Forecast Horizon (Days)"
-            )
-            
             analyze_btn = gr.Button("Analyze Stock", variant="primary")
         
         with gr.Column():
-            output_plot = gr.Plot(label="Stock Price Chart")
+            output_plot = gr.Plot(label="Stock Analysis Charts")
     
     with gr.Row():
         output_stats = gr.Markdown(label="Analysis Summary")
     
-    with gr.Row():
-        output_table = gr.Dataframe(
-            label="Model Performance Comparison",
-            headers=["Model", "RMSE", "MAE", "MAPE (%)", "Status"]
-        )
+    output_table = gr.Dataframe(
+        label="Model Performance Comparison",
+        headers=["Model", "RMSE", "MAE", "MAPE (%)", "Status"]
+    )
     
     # Examples section
     gr.Markdown("### 💡 Try These Examples:")
     gr.Examples(
         examples=[
-            ["AAPL", 30],
-            ["GOOGL", 30],
-            ["TSLA", 30],
-            ["MSFT", 30]
+            ["AAPL"],
+            ["GOOGL"], 
+            ["TSLA"],
+            ["MSFT"],
+            ["AMZN"]
         ],
-        inputs=[symbol_input, forecast_slider]
+        inputs=[symbol_input]
     )
     
     # Footer
     gr.Markdown(
         """
         ---
-        **About:** Stock forecasting models comparison | **Deployment:** Hugging Face Spaces
+        ### 🚀 About This Project
+        - **Models**: ARIMA, LSTM, Prophet, Naive
+        - **Evaluation**: Rolling Window Validation  
+        - **Best Model**: Naive (Baseline)
+        - **Deployment**: Hugging Face Spaces + Gradio
+        - **Insight**: In efficient markets, simple models often generalize better
         """
     )
     
     # Connect button to function
     analyze_btn.click(
         fn=forecast_stock,
-        inputs=[symbol_input, forecast_slider],
+        inputs=[symbol_input],
         outputs=[output_plot, output_table, output_stats]
     )