Update app.py

app.py

@@ -7,9 +7,6 @@ from datetime import datetime, timedelta
 import warnings
 warnings.filterwarnings('ignore')
 
-# Set matplotlib style
-plt.style.use('seaborn-v0_8')
-
 def forecast_stock(symbol, forecast_days):
     """
     Main function to generate stock forecast and analysis
@@ -17,80 +14,50 @@ def forecast_stock(symbol, forecast_days):
     try:
         # Download stock data
         end_date = datetime.now()
-        start_date = end_date - timedelta(days=365*2)  # 2 years of data
+        start_date = end_date - timedelta(days=365)  # Reduced to 1 year for faster loading
         
         data = yf.download(symbol, start=start_date, end=end_date, progress=False)
         
-        if data.empty:
+        if data.empty or len(data) < 10:
             return None, None, "❌ No data found for this symbol. Try AAPL, GOOGL, TSLA, etc."
         
-        # Create analysis plots
-        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
-        
-        # Plot 1: Price chart
-        ax1.plot(data.index, data['Close'], linewidth=2, color='blue')
-        ax1.set_title(f'{symbol} Stock Price', fontsize=14, fontweight='bold')
-        ax1.set_ylabel('Price ($)')
-        ax1.grid(True, alpha=0.3)
-        ax1.tick_params(axis='x', rotation=45)
-        
-        # Plot 2: Daily returns
-        returns = data['Close'].pct_change().dropna()
-        ax2.hist(returns, bins=50, alpha=0.7, color='green', edgecolor='black')
-        ax2.set_title('Daily Returns Distribution', fontsize=14, fontweight='bold')
-        ax2.set_xlabel('Returns')
-        ax2.set_ylabel('Frequency')
-        ax2.grid(True, alpha=0.3)
-        
-        # Plot 3: Volume
-        ax3.bar(data.index, data['Volume'], alpha=0.7, color='orange')
-        ax3.set_title('Trading Volume', fontsize=14, fontweight='bold')
-        ax3.set_ylabel('Volume')
-        ax3.tick_params(axis='x', rotation=45)
-        ax3.grid(True, alpha=0.3)
-        
-        # Plot 4: Model performance comparison
-        models = ['Naive', 'LSTM', 'ARIMA', 'Prophet']
-        rmse_scores = [1.77, 6.44, 6.65, 58.52]
-        colors = ['green', 'orange', 'blue', 'red']
-        
-        bars = ax4.bar(models, rmse_scores, color=colors, alpha=0.7)
-        ax4.set_title('Model Performance (RMSE)', fontsize=14, fontweight='bold')
-        ax4.set_ylabel('RMSE Score')
-        ax4.tick_params(axis='x', rotation=45)
-        
-        # Add value labels on bars
-        for bar, value in zip(bars, rmse_scores):
-            ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5, 
-                    f'{value}', ha='center', va='bottom', fontweight='bold')
-        
-        ax4.grid(True, alpha=0.3)
-        
+        # Create a single figure instead of subplots for simplicity
+        fig, ax = plt.subplots(figsize=(10, 6))
+        ax.plot(data.index, data['Close'], linewidth=2, color='blue')
+        ax.set_title(f'{symbol} Stock Price', fontsize=14, fontweight='bold')
+        ax.set_ylabel('Price ($)')
+        ax.grid(True, alpha=0.3)
+        ax.tick_params(axis='x', rotation=45)
         plt.tight_layout()
         
         # Create performance summary
-        performance_df = pd.DataFrame({
+        performance_data = {
             'Model': ['Naive', 'LSTM', 'ARIMA', 'Prophet'],
             'RMSE': [1.77, 6.44, 6.65, 58.52],
             'MAE': [1.36, 5.30, 4.98, 34.89],
             'MAPE (%)': [1.24, 4.82, 4.46, 32.81],
             'Status': ['✅ Best', '⚠️ Needs Tuning', '⚠️ Needs Tuning', '❌ Poor']
-        })
+        }
+        performance_df = pd.DataFrame(performance_data)
         
         # Create stats summary
+        current_price = data['Close'].iloc[-1]
+        start_price = data['Close'].iloc[0]
+        total_return = ((current_price / start_price) - 1) * 100
+        
         stats_text = f"""
         📊 **Stock Analysis Summary for {symbol}**
         
         **Price Statistics:**
-        - Current Price: ${data['Close'].iloc[-1]:.2f}
-        - 52-Week High: ${data['Close'].max():.2f}
-        - 52-Week Low: ${data['Close'].min():.2f}
-        - Total Return: {((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100:.2f}%
+        - Current Price: ${current_price:.2f}
+        - Start Price: ${start_price:.2f}
+        - Total Return: {total_return:.2f}%
+        - Data Points: {len(data)} days
         
-        **Model Insights:**
+        **Model Performance:**
         - Best Model: **Naive (Baseline)**
-        - Key Finding: Simple models often outperform complex ones in efficient markets
-        - Recommendation: Use ensemble methods for improved accuracy
+        - 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')}
         """
@@ -98,7 +65,8 @@ def forecast_stock(symbol, forecast_days):
         return fig, performance_df, stats_text
         
     except Exception as e:
-        return None, None, f"❌ Error: {str(e)}"
+        error_msg = f"❌ Error: {str(e)}. Try a different stock symbol like AAPL or TSLA."
+        return None, None, error_msg
 
 # Create Gradio interface
 with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
@@ -107,8 +75,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
         # 📈 Stock Price Forecasting App
         ### DataSynthis ML Job Task - Time Series Analysis
         
-        This app analyzes stock performance and compares forecasting models including:
-        **ARIMA, LSTM, Prophet, and Naive baseline**
+        Analyze stock performance and compare forecasting models.
         """
     )
     
@@ -131,7 +98,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
             analyze_btn = gr.Button("Analyze Stock", variant="primary")
         
         with gr.Column():
-            output_plot = gr.Plot(label="Stock Analysis Charts")
+            output_plot = gr.Plot(label="Stock Price Chart")
     
     with gr.Row():
         output_stats = gr.Markdown(label="Analysis Summary")
@@ -139,8 +106,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
     with gr.Row():
         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
@@ -150,8 +116,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
             ["AAPL", 30],
             ["GOOGL", 30],
             ["TSLA", 30],
-            ["MSFT", 30],
-            ["AMZN", 30]
+            ["MSFT", 30]
         ],
         inputs=[symbol_input, forecast_slider]
     )
@@ -160,12 +125,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
     gr.Markdown(
         """
         ---
-        ### 🚀 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
+        **About:** Stock forecasting models comparison | **Deployment:** Hugging Face Spaces
         """
     )
     
@@ -178,4 +138,4 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo:
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()