Update app.py

app.py

@@ -1,325 +1,357 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
-import pickle
-import yfinance as yf
-from statsmodels.tsa.arima.model import ARIMA
-from prophet import Prophet
-from tensorflow import keras
-from sklearn.preprocessing import MinMaxScaler
-import warnings
-warnings.filterwarnings('ignore')
-
-# Load your saved models (update paths as needed)
-# For Hugging Face, these will be in the same directory as app.py
-def load_models():
-    """Load all three models"""
-    try:
-        # Load ARIMA model
-        with open('arima_model.pkl', 'rb') as f:
-            arima_model = pickle.load(f)
-        
-        # Load Prophet model
-        with open('prophet_model.pkl', 'rb') as f:
-            prophet_model = pickle.load(f)
-        
-        # Load LSTM model and scaler
-        lstm_model = keras.models.load_model('lstm_model.h5')
-        with open('lstm_scaler.pkl', 'rb') as f:
-            scaler = pickle.load(f)
-        
-        return arima_model, prophet_model, lstm_model, scaler
-    except Exception as e:
-        print(f"Error loading models: {e}")
-        return None, None, None, None
-
-# Global variables for models
-arima_model, prophet_model, lstm_model, scaler = load_models()
-SEQ_LENGTH = 60  # Should match your training
-
-def fetch_stock_data(ticker, days=365):
-    """Fetch stock data from Yahoo Finance"""
-    try:
-        end_date = datetime.now()
-        start_date = end_date - timedelta(days=days)
-        df = yf.download(ticker, start=start_date, end=end_date, progress=False)
-        if df.empty:
-            return None, f"No data found for ticker: {ticker}"
-        df = df[['Close']].copy()
-        df.columns = ['Price']
-        return df, None
-    except Exception as e:
-        return None, str(e)
-
-def make_arima_forecast(data, days):
-    """Make ARIMA forecast"""
-    try:
-        # Retrain ARIMA with recent data (or use loaded model)
-        model = ARIMA(data['Price'], order=(1, 1, 1))
-        fitted = model.fit()
-        forecast = fitted.forecast(steps=days)
-        return forecast.values
-    except Exception as e:
-        print(f"ARIMA Error: {e}")
-        return None
-
-def make_prophet_forecast(data, days):
-    """Make Prophet forecast"""
-    try:
-        # Prepare data for Prophet
-        prophet_data = pd.DataFrame({
-            'ds': data.index,
-            'y': data['Price'].values
-        })
-        
-        # Create and fit model
-        model = Prophet(
-            daily_seasonality=True,
-            weekly_seasonality=True,
-            yearly_seasonality=True,
-            changepoint_prior_scale=0.05
-        )
-        model.fit(prophet_data)
-        
-        # Make forecast
-        future = model.make_future_dataframe(periods=days)
-        forecast = model.predict(future)
-        return forecast['yhat'].tail(days).values
-    except Exception as e:
-        print(f"Prophet Error: {e}")
-        return None
-
-def make_lstm_forecast(data, days, model, scaler, seq_length=60):
-    """Make LSTM forecast"""
-    try:
-        # Scale the data
-        scaled_data = scaler.transform(data[['Price']])
-        
-        # Prepare the last sequence
-        last_sequence = scaled_data[-seq_length:].reshape(1, seq_length, 1)
-        
-        predictions = []
-        current_sequence = last_sequence.copy()
-        
-        # Generate predictions day by day
-        for _ in range(days):
-            pred = model.predict(current_sequence, verbose=0)
-            predictions.append(pred[0, 0])
-            
-            # Update sequence
-            current_sequence = np.append(current_sequence[:, 1:, :], 
-                                        pred.reshape(1, 1, 1), axis=1)
-        
-        # Inverse transform predictions
-        predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))
-        return predictions.flatten()
-    except Exception as e:
-        print(f"LSTM Error: {e}")
-        return None
-
-def create_forecast_plot(historical_data, forecasts, ticker, model_names):
-    """Create interactive plotly chart"""
-    fig = go.Figure()
-    
-    # Historical data
-    fig.add_trace(go.Scatter(
-        x=historical_data.index,
-        y=historical_data['Price'],
-        mode='lines',
-        name='Historical Price',
-        line=dict(color='blue', width=2)
-    ))
-    
-    # Generate future dates
-    last_date = historical_data.index[-1]
-    future_dates = pd.date_range(start=last_date + timedelta(days=1), 
-                                  periods=len(forecasts[0]))
-    
-    # Plot forecasts
-    colors = ['red', 'purple', 'orange']
-    for i, (forecast, name) in enumerate(zip(forecasts, model_names)):
-        if forecast is not None:
-            fig.add_trace(go.Scatter(
-                x=future_dates,
-                y=forecast,
-                mode='lines+markers',
-                name=f'{name} Forecast',
-                line=dict(color=colors[i], width=2, dash='dash'),
-                marker=dict(size=6)
-            ))
-    
-    fig.update_layout(
-        title=f'{ticker} Stock Price Forecast',
-        xaxis_title='Date',
-        yaxis_title='Price ($)',
-        hovermode='x unified',
-        template='plotly_white',
-        height=600,
-        showlegend=True,
-        legend=dict(
-            yanchor="top",
-            y=0.99,
-            xanchor="left",
-            x=0.01
-        )
-    )
-    
-    return fig
-
-def predict_stock(ticker, forecast_days, model_choice):
-    """Main prediction function"""
-    # Validate inputs
-    if not ticker:
-        return None, "Please enter a stock ticker symbol", None
-    
-    ticker = ticker.upper().strip()
-    
-    # Fetch data
-    data, error = fetch_stock_data(ticker, days=730)  # 2 years of data
-    if error:
-        return None, f"Error: {error}", None
-    
-    # Make forecasts based on model choice
-    forecasts = []
-    model_names = []
-    
-    if model_choice in ["All Models", "ARIMA"]:
-        arima_forecast = make_arima_forecast(data, forecast_days)
-        if arima_forecast is not None:
-            forecasts.append(arima_forecast)
-            model_names.append("ARIMA")
-    
-    if model_choice in ["All Models", "Prophet"]:
-        prophet_forecast = make_prophet_forecast(data, forecast_days)
-        if prophet_forecast is not None:
-            forecasts.append(prophet_forecast)
-            model_names.append("Prophet")
-    
-    if model_choice in ["All Models", "LSTM"] and lstm_model is not None:
-        lstm_forecast = make_lstm_forecast(data, forecast_days, lstm_model, scaler, SEQ_LENGTH)
-        if lstm_forecast is not None:
-            forecasts.append(lstm_forecast)
-            model_names.append("LSTM")
-    
-    if not forecasts:
-        return None, "Failed to generate forecasts. Please try again.", None
-    
-    # Create plot
-    fig = create_forecast_plot(data, forecasts, ticker, model_names)
-    
-    # Create forecast table
-    future_dates = pd.date_range(
-        start=data.index[-1] + timedelta(days=1), 
-        periods=forecast_days
-    )
-    
-    forecast_df = pd.DataFrame({'Date': future_dates.strftime('%Y-%m-%d')})
-    for forecast, name in zip(forecasts, model_names):
-        forecast_df[f'{name} Prediction ($)'] = np.round(forecast, 2)
-    
-    # Summary statistics
-    summary = f"""
-    📊 **Forecast Summary for {ticker}**
-    
-    - Current Price: ${data['Price'].iloc[-1]:.2f}
-    - Forecast Period: {forecast_days} days
-    - Models Used: {', '.join(model_names)}
-    
-    **Predicted Price Range (Day {forecast_days}):**
-    """
-    
-    for forecast, name in zip(forecasts, model_names):
-        final_price = forecast[-1]
-        change = ((final_price - data['Price'].iloc[-1]) / data['Price'].iloc[-1]) * 100
-        summary += f"\n- {name}: ${final_price:.2f} ({change:+.2f}%)"
-    
-    return fig, summary, forecast_df
-
-# Create Gradio Interface
-with gr.Blocks(title="Stock Price Forecasting", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 📈 Stock Price Forecasting App
-        
-        Predict future stock prices using ARIMA, Prophet, and LSTM models.
-        Enter a stock ticker symbol and select forecast parameters below.
-        
-        **Note:** Predictions are for educational purposes only. Not financial advice.
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            ticker_input = gr.Textbox(
-                label="Stock Ticker Symbol",
-                placeholder="e.g., AAPL, GOOGL, TSLA",
-                value="AAPL"
-            )
-            
-            forecast_days = gr.Slider(
-                minimum=1,
-                maximum=90,
-                value=30,
-                step=1,
-                label="Forecast Days"
-            )
-            
-            model_choice = gr.Radio(
-                choices=["All Models", "ARIMA", "Prophet", "LSTM"],
-                value="All Models",
-                label="Select Model(s)"
-            )
-            
-            predict_btn = gr.Button("🔮 Generate Forecast", variant="primary", size="lg")
-        
-        with gr.Column(scale=2):
-            output_plot = gr.Plot(label="Forecast Visualization")
-    
-    with gr.Row():
-        output_summary = gr.Markdown(label="Forecast Summary")
-    
-    with gr.Row():
-        output_table = gr.Dataframe(
-            label="Detailed Forecast",
-            wrap=True,
-            interactive=False
-        )
-    
-    # Examples
-    gr.Examples(
-        examples=[
-            ["AAPL", 30, "All Models"],
-            ["GOOGL", 14, "Prophet"],
-            ["TSLA", 60, "LSTM"],
-            ["MSFT", 45, "ARIMA"],
-        ],
-        inputs=[ticker_input, forecast_days, model_choice],
-    )
-    
-    # Connect the button to the function
-    predict_btn.click(
-        fn=predict_stock,
-        inputs=[ticker_input, forecast_days, model_choice],
-        outputs=[output_plot, output_summary, output_table]
-    )
-    
-    gr.Markdown(
-        """
-        ---
-        ### 📚 About the Models
-        
-        - **ARIMA**: Statistical model for time series forecasting
-        - **Prophet**: Facebook's forecasting tool, excellent for seasonality
-        - **LSTM**: Deep learning model that captures complex patterns
-        
-        ### ⚠️ Disclaimer
-        This tool is for educational and research purposes only. Stock market predictions are inherently uncertain. 
-        Always conduct thorough research and consult with financial advisors before making investment decisions.
-        """
-    )
-
-# Launch the app
-if __name__ == "__main__":
+import gradio as gr
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import pickle
+import yfinance as yf
+from statsmodels.tsa.arima.model import ARIMA
+from prophet import Prophet
+from tensorflow import keras
+from sklearn.preprocessing import MinMaxScaler
+import warnings
+warnings.filterwarnings('ignore')
+
+# Load your saved models (update paths as needed)
+# For Hugging Face, these will be in the same directory as app.py
+def load_models():
+    """Load all three models"""
+    try:
+        # Load ARIMA model
+        with open('arima_model.pkl', 'rb') as f:
+            arima_model = pickle.load(f)
+        
+        # Load Prophet model
+        with open('prophet_model.pkl', 'rb') as f:
+            prophet_model = pickle.load(f)
+        
+        # Load LSTM model and scaler
+        lstm_model = keras.models.load_model('lstm_model.h5')
+        with open('lstm_scaler.pkl', 'rb') as f:
+            scaler = pickle.load(f)
+        
+        return arima_model, prophet_model, lstm_model, scaler
+    except Exception as e:
+        print(f"Error loading models: {e}")
+        return None, None, None, None
+
+# Global variables for models
+arima_model, prophet_model, lstm_model, scaler = load_models()
+SEQ_LENGTH = 60  # Should match your training
+
+def fetch_stock_data(ticker, days=365):
+    """Fetch stock data from Yahoo Finance"""
+    try:
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=days)
+        
+        # Add retry logic and better error handling
+        import time
+        max_retries = 3
+        for attempt in range(max_retries):
+            try:
+                df = yf.download(ticker, start=start_date, end=end_date, progress=False, auto_adjust=True)
+                if not df.empty:
+                    break
+                time.sleep(1)  # Wait before retry
+            except Exception as e:
+                if attempt == max_retries - 1:
+                    raise e
+                time.sleep(1)
+        
+        if df.empty:
+            return None, f"No data found for ticker: {ticker}. Please verify the ticker symbol is correct."
+        
+        # Handle both multi-level and single-level columns
+        if isinstance(df.columns, pd.MultiIndex):
+            df = df['Close'].to_frame()
+        elif 'Close' in df.columns:
+            df = df[['Close']].copy()
+        else:
+            # Try to find a price column
+            price_col = [col for col in df.columns if 'close' in col.lower()]
+            if price_col:
+                df = df[[price_col[0]]].copy()
+            else:
+                return None, f"Could not find price data for {ticker}"
+        
+        df.columns = ['Price']
+        df = df.dropna()
+        
+        if len(df) < 100:
+            return None, f"Insufficient data for {ticker}. Only {len(df)} days available."
+        
+        return df, None
+    except Exception as e:
+        return None, f"Error fetching data for {ticker}: {str(e)}"
+
+def make_arima_forecast(data, days):
+    """Make ARIMA forecast"""
+    try:
+        # Retrain ARIMA with recent data (or use loaded model)
+        model = ARIMA(data['Price'], order=(1, 1, 1))
+        fitted = model.fit()
+        forecast = fitted.forecast(steps=days)
+        return forecast.values
+    except Exception as e:
+        print(f"ARIMA Error: {e}")
+        return None
+
+def make_prophet_forecast(data, days):
+    """Make Prophet forecast"""
+    try:
+        # Prepare data for Prophet
+        prophet_data = pd.DataFrame({
+            'ds': data.index,
+            'y': data['Price'].values
+        })
+        
+        # Create and fit model
+        model = Prophet(
+            daily_seasonality=True,
+            weekly_seasonality=True,
+            yearly_seasonality=True,
+            changepoint_prior_scale=0.05
+        )
+        model.fit(prophet_data)
+        
+        # Make forecast
+        future = model.make_future_dataframe(periods=days)
+        forecast = model.predict(future)
+        return forecast['yhat'].tail(days).values
+    except Exception as e:
+        print(f"Prophet Error: {e}")
+        return None
+
+def make_lstm_forecast(data, days, model, scaler, seq_length=60):
+    """Make LSTM forecast"""
+    try:
+        # Scale the data
+        scaled_data = scaler.transform(data[['Price']])
+        
+        # Prepare the last sequence
+        last_sequence = scaled_data[-seq_length:].reshape(1, seq_length, 1)
+        
+        predictions = []
+        current_sequence = last_sequence.copy()
+        
+        # Generate predictions day by day
+        for _ in range(days):
+            pred = model.predict(current_sequence, verbose=0)
+            predictions.append(pred[0, 0])
+            
+            # Update sequence
+            current_sequence = np.append(current_sequence[:, 1:, :], 
+                                        pred.reshape(1, 1, 1), axis=1)
+        
+        # Inverse transform predictions
+        predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))
+        return predictions.flatten()
+    except Exception as e:
+        print(f"LSTM Error: {e}")
+        return None
+
+def create_forecast_plot(historical_data, forecasts, ticker, model_names):
+    """Create interactive plotly chart"""
+    fig = go.Figure()
+    
+    # Historical data
+    fig.add_trace(go.Scatter(
+        x=historical_data.index,
+        y=historical_data['Price'],
+        mode='lines',
+        name='Historical Price',
+        line=dict(color='blue', width=2)
+    ))
+    
+    # Generate future dates
+    last_date = historical_data.index[-1]
+    future_dates = pd.date_range(start=last_date + timedelta(days=1), 
+                                  periods=len(forecasts[0]))
+    
+    # Plot forecasts
+    colors = ['red', 'purple', 'orange']
+    for i, (forecast, name) in enumerate(zip(forecasts, model_names)):
+        if forecast is not None:
+            fig.add_trace(go.Scatter(
+                x=future_dates,
+                y=forecast,
+                mode='lines+markers',
+                name=f'{name} Forecast',
+                line=dict(color=colors[i], width=2, dash='dash'),
+                marker=dict(size=6)
+            ))
+    
+    fig.update_layout(
+        title=f'{ticker} Stock Price Forecast',
+        xaxis_title='Date',
+        yaxis_title='Price ($)',
+        hovermode='x unified',
+        template='plotly_white',
+        height=600,
+        showlegend=True,
+        legend=dict(
+            yanchor="top",
+            y=0.99,
+            xanchor="left",
+            x=0.01
+        )
+    )
+    
+    return fig
+
+def predict_stock(ticker, forecast_days, model_choice):
+    """Main prediction function"""
+    # Validate inputs
+    if not ticker:
+        return None, "Please enter a stock ticker symbol", None
+    
+    ticker = ticker.upper().strip()
+    
+    # Fetch data
+    data, error = fetch_stock_data(ticker, days=730)  # 2 years of data
+    if error:
+        return None, f"Error: {error}", None
+    
+    # Make forecasts based on model choice
+    forecasts = []
+    model_names = []
+    
+    if model_choice in ["All Models", "ARIMA"]:
+        arima_forecast = make_arima_forecast(data, forecast_days)
+        if arima_forecast is not None:
+            forecasts.append(arima_forecast)
+            model_names.append("ARIMA")
+    
+    if model_choice in ["All Models", "Prophet"]:
+        prophet_forecast = make_prophet_forecast(data, forecast_days)
+        if prophet_forecast is not None:
+            forecasts.append(prophet_forecast)
+            model_names.append("Prophet")
+    
+    if model_choice in ["All Models", "LSTM"] and lstm_model is not None:
+        lstm_forecast = make_lstm_forecast(data, forecast_days, lstm_model, scaler, SEQ_LENGTH)
+        if lstm_forecast is not None:
+            forecasts.append(lstm_forecast)
+            model_names.append("LSTM")
+    
+    if not forecasts:
+        return None, "Failed to generate forecasts. Please try again.", None
+    
+    # Create plot
+    fig = create_forecast_plot(data, forecasts, ticker, model_names)
+    
+    # Create forecast table
+    future_dates = pd.date_range(
+        start=data.index[-1] + timedelta(days=1), 
+        periods=forecast_days
+    )
+    
+    forecast_df = pd.DataFrame({'Date': future_dates.strftime('%Y-%m-%d')})
+    for forecast, name in zip(forecasts, model_names):
+        forecast_df[f'{name} Prediction ($)'] = np.round(forecast, 2)
+    
+    # Summary statistics
+    summary = f"""
+    📊 **Forecast Summary for {ticker}**
+    
+    - Current Price: ${data['Price'].iloc[-1]:.2f}
+    - Forecast Period: {forecast_days} days
+    - Models Used: {', '.join(model_names)}
+    
+    **Predicted Price Range (Day {forecast_days}):**
+    """
+    
+    for forecast, name in zip(forecasts, model_names):
+        final_price = forecast[-1]
+        change = ((final_price - data['Price'].iloc[-1]) / data['Price'].iloc[-1]) * 100
+        summary += f"\n- {name}: ${final_price:.2f} ({change:+.2f}%)"
+    
+    return fig, summary, forecast_df
+
+# Create Gradio Interface
+with gr.Blocks(title="Stock Price Forecasting", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 📈 Stock Price Forecasting App
+        
+        Predict future stock prices using ARIMA, Prophet, and LSTM models.
+        Enter a stock ticker symbol and select forecast parameters below.
+        
+        **Note:** Predictions are for educational purposes only. Not financial advice.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            ticker_input = gr.Textbox(
+                label="Stock Ticker Symbol",
+                placeholder="e.g., AAPL, GOOGL, TSLA",
+                value="AAPL"
+            )
+            
+            forecast_days = gr.Slider(
+                minimum=1,
+                maximum=90,
+                value=30,
+                step=1,
+                label="Forecast Days"
+            )
+            
+            model_choice = gr.Radio(
+                choices=["All Models", "ARIMA", "Prophet", "LSTM"],
+                value="All Models",
+                label="Select Model(s)"
+            )
+            
+            predict_btn = gr.Button("🔮 Generate Forecast", variant="primary", size="lg")
+        
+        with gr.Column(scale=2):
+            output_plot = gr.Plot(label="Forecast Visualization")
+    
+    with gr.Row():
+        output_summary = gr.Markdown(label="Forecast Summary")
+    
+    with gr.Row():
+        output_table = gr.Dataframe(
+            label="Detailed Forecast",
+            wrap=True,
+            interactive=False
+        )
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["AAPL", 30, "All Models"],
+            ["GOOGL", 14, "Prophet"],
+            ["TSLA", 60, "LSTM"],
+            ["MSFT", 45, "ARIMA"],
+        ],
+        inputs=[ticker_input, forecast_days, model_choice],
+    )
+    
+    # Connect the button to the function
+    predict_btn.click(
+        fn=predict_stock,
+        inputs=[ticker_input, forecast_days, model_choice],
+        outputs=[output_plot, output_summary, output_table]
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### 📚 About the Models
+        
+        - **ARIMA**: Statistical model for time series forecasting
+        - **Prophet**: Facebook's forecasting tool, excellent for seasonality
+        - **LSTM**: Deep learning model that captures complex patterns
+        
+        ### ⚠️ Disclaimer
+        This tool is for educational and research purposes only. Stock market predictions are inherently uncertain. 
+        Always conduct thorough research and consult with financial advisors before making investment decisions.
+        """
+    )
+
+# Launch the app
+if __name__ == "__main__":
     demo.launch()