app.py

import gradio as gr
import numpy as np
import pandas as pd
import yfinance as yf
import joblib
from tensorflow.keras.models import load_model
import plotly.graph_objects as go
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

class StockPredictorApp:
    def __init__(self, arima_path='arima_model.pkl', 
                 lstm_path='lstm_model.h5', 
                 scaler_path='scaler.pkl'):
        """
        Initialize the stock predictor with pre-trained models
        """
        try:
            # Load models
            self.arima_model = joblib.load(arima_path)
            self.lstm_model = load_model(lstm_path)
            self.scaler = joblib.load(scaler_path)
            self.lookback = 60  # Default lookback period for LSTM
            print("Models loaded successfully!")
        except Exception as e:
            print(f"Error loading models: {e}")
            self.arima_model = None
            self.lstm_model = None
            self.scaler = None
    
    def fetch_stock_data(self, ticker, days_back=365):
        """
        Fetch recent stock data for prediction
        """
        try:
            end_date = datetime.now()
            start_date = end_date - timedelta(days=days_back)
            
            # Download stock data
            stock_data = yf.download(ticker, 
                                    start=start_date.strftime('%Y-%m-%d'), 
                                    end=end_date.strftime('%Y-%m-%d'),
                                    progress=False)
            
            if stock_data.empty:
                return None, "No data found for this ticker"
            
            # Extract closing prices
            prices = stock_data[['Close']].copy()
            prices.columns = ['price']
            
            return prices, None
        except Exception as e:
            return None, f"Error fetching data: {str(e)}"
    
    def prepare_lstm_input(self, data):
        """
        Prepare data for LSTM prediction
        """
        # Scale the data
        scaled_data = self.scaler.transform(data[['price']])
        
        # Create sequences
        if len(scaled_data) < self.lookback:
            # Pad with the first value if not enough data
            padding = np.tile(scaled_data[0], (self.lookback - len(scaled_data), 1))
            scaled_data = np.vstack([padding, scaled_data])
        
        # Take the last lookback values
        sequence = scaled_data[-self.lookback:].reshape(1, self.lookback, 1)
        
        return sequence
    
    def predict_next_days(self, ticker, num_days):
        """
        Predict stock prices for the next n days
        """
        if not all([self.arima_model, self.lstm_model, self.scaler]):
            return None, None, "Models not loaded properly. Please check model files."
        
        # Fetch historical data
        historical_data, error = self.fetch_stock_data(ticker, days_back=365)
        
        if error:
            return None, None, error
        
        try:
            # ARIMA Predictions
            arima_forecast = self.arima_model.forecast(steps=num_days)
            
            # LSTM Predictions
            lstm_predictions = []
            current_data = historical_data.copy()
            
            for _ in range(num_days):
                # Prepare input
                lstm_input = self.prepare_lstm_input(current_data)
                
                # Make prediction
                scaled_pred = self.lstm_model.predict(lstm_input, verbose=0)
                pred = self.scaler.inverse_transform(scaled_pred)[0, 0]
                lstm_predictions.append(pred)
                
                # Add prediction to data for next iteration
                next_date = current_data.index[-1] + timedelta(days=1)
                new_row = pd.DataFrame({'price': [pred]}, index=[next_date])
                current_data = pd.concat([current_data, new_row])
            
            # Create future dates
            last_date = historical_data.index[-1]
            future_dates = pd.date_range(start=last_date + timedelta(days=1), 
                                        periods=num_days, freq='D')
            
            # Create prediction DataFrames
            arima_df = pd.DataFrame({
                'Date': future_dates,
                'ARIMA_Prediction': arima_forecast.values
            })
            
            lstm_df = pd.DataFrame({
                'Date': future_dates,
                'LSTM_Prediction': lstm_predictions
            })
            
            # Combine predictions
            predictions_df = pd.merge(arima_df, lstm_df, on='Date')
            predictions_df['Average_Prediction'] = (predictions_df['ARIMA_Prediction'] + 
                                                   predictions_df['LSTM_Prediction']) / 2
            
            return historical_data, predictions_df, None
            
        except Exception as e:
            return None, None, f"Prediction error: {str(e)}"
    
    def create_plot(self, historical_data, predictions_df, ticker):
        """
        Create an interactive plot using Plotly
        """
        fig = go.Figure()
        
        # Plot historical data
        fig.add_trace(go.Scatter(
            x=historical_data.index,
            y=historical_data['price'],
            mode='lines',
            name='Historical Price',
            line=dict(color='black', width=2)
        ))
        
        # Plot ARIMA predictions
        fig.add_trace(go.Scatter(
            x=predictions_df['Date'],
            y=predictions_df['ARIMA_Prediction'],
            mode='lines+markers',
            name='ARIMA Forecast',
            line=dict(color='blue', width=2, dash='dash'),
            marker=dict(size=6)
        ))
        
        # Plot LSTM predictions
        fig.add_trace(go.Scatter(
            x=predictions_df['Date'],
            y=predictions_df['LSTM_Prediction'],
            mode='lines+markers',
            name='LSTM Forecast',
            line=dict(color='red', width=2, dash='dash'),
            marker=dict(size=6)
        ))
        
        # Plot average predictions
        fig.add_trace(go.Scatter(
            x=predictions_df['Date'],
            y=predictions_df['Average_Prediction'],
            mode='lines+markers',
            name='Ensemble (Average)',
            line=dict(color='green', width=2, dash='dot'),
            marker=dict(size=8)
        ))
        
        # Update layout
        fig.update_layout(
            title=f'{ticker} Stock Price Forecast',
            xaxis_title='Date',
            yaxis_title='Price ($)',
            hovermode='x unified',
            showlegend=True,
            template='plotly_white',
            height=600
        )
        
        # Add a vertical line to separate historical and predicted
        fig.add_vline(x=historical_data.index[-1], 
                     line_dash="solid", 
                     line_color="gray",
                     annotation_text="Forecast Start")
        
        return fig

# Initialize the app
predictor = StockPredictorApp()

def predict_stock_price(ticker, num_days):
    """
    Main prediction function for Gradio interface
    """
    # Create empty dataframe for error cases
    empty_df = pd.DataFrame()
    
    if not ticker:
        return None, "Please enter a stock ticker symbol", empty_df
    
    # Convert ticker to uppercase
    ticker = ticker.upper()
    
    # Validate number of days
    if num_days < 1 or num_days > 90:
        return None, "Please enter a number of days between 1 and 90", empty_df
    
    # Get predictions
    historical_data, predictions_df, error = predictor.predict_next_days(ticker, num_days)
    
    if error:
        return None, error, empty_df
    
    # Create plot
    fig = predictor.create_plot(historical_data, predictions_df, ticker)
    
    # Format predictions table
    predictions_display = predictions_df.copy()
    predictions_display['Date'] = predictions_display['Date'].dt.strftime('%Y-%m-%d')
    predictions_display = predictions_display.round(2)
    
    # Calculate summary statistics
    summary = f"""
    ### Prediction Summary for {ticker}
    
    **Forecast Period**: {num_days} days
    
    **ARIMA Model**:
    - First Day: ${predictions_df['ARIMA_Prediction'].iloc[0]:.2f}
    - Last Day: ${predictions_df['ARIMA_Prediction'].iloc[-1]:.2f}
    - Average: ${predictions_df['ARIMA_Prediction'].mean():.2f}
    - Trend: {'📈 Upward' if predictions_df['ARIMA_Prediction'].iloc[-1] > predictions_df['ARIMA_Prediction'].iloc[0] else '📉 Downward'}
    
    **LSTM Model**:
    - First Day: ${predictions_df['LSTM_Prediction'].iloc[0]:.2f}
    - Last Day: ${predictions_df['LSTM_Prediction'].iloc[-1]:.2f}
    - Average: ${predictions_df['LSTM_Prediction'].mean():.2f}
    - Trend: {'📈 Upward' if predictions_df['LSTM_Prediction'].iloc[-1] > predictions_df['LSTM_Prediction'].iloc[0] else '📉 Downward'}
    
    **Ensemble (Average)**:
    - First Day: ${predictions_df['Average_Prediction'].iloc[0]:.2f}
    - Last Day: ${predictions_df['Average_Prediction'].iloc[-1]:.2f}
    - Average: ${predictions_df['Average_Prediction'].mean():.2f}
    
    **Current Price**: ${historical_data['price'].iloc[-1]:.2f}
    **Expected Change**: {'+' if predictions_df['Average_Prediction'].iloc[-1] > historical_data['price'].iloc[-1] else ''}{((predictions_df['Average_Prediction'].iloc[-1] / historical_data['price'].iloc[-1] - 1) * 100):.2f}%
    """
    
    return fig, summary, predictions_display

# Create demo mode for when models aren't available
def create_demo_predictions(ticker, num_days):
    """
    Create demo predictions when models aren't loaded
    """
    # Create fake historical data
    dates = pd.date_range(end=datetime.now(), periods=100, freq='D')
    base_price = 150.0
    historical_data = pd.DataFrame({
        'price': base_price + np.cumsum(np.random.randn(100) * 2)
    }, index=dates)
    
    # Create fake predictions
    future_dates = pd.date_range(start=dates[-1] + timedelta(days=1), 
                                periods=num_days, freq='D')
    
    last_price = historical_data['price'].iloc[-1]
    arima_pred = last_price + np.cumsum(np.random.randn(num_days) * 1.5)
    lstm_pred = last_price + np.cumsum(np.random.randn(num_days) * 1.5)
    
    predictions_df = pd.DataFrame({
        'Date': future_dates,
        'ARIMA_Prediction': arima_pred,
        'LSTM_Prediction': lstm_pred,
        'Average_Prediction': (arima_pred + lstm_pred) / 2
    })
    
    return historical_data, predictions_df

# Modified predict function with fallback to demo mode
def predict_stock_price_safe(ticker, num_days):
    """
    Safe prediction function with demo fallback
    """
    empty_df = pd.DataFrame()
    
    if not ticker:
        return None, "Please enter a stock ticker symbol", empty_df
    
    ticker = ticker.upper()
    
    if num_days < 1 or num_days > 90:
        return None, "Please enter a number of days between 1 and 90", empty_df
    
    # Check if models are loaded
    if not all([predictor.arima_model, predictor.lstm_model, predictor.scaler]):
        # Use demo mode
        demo_msg = f"""
        ### ⚠️ Demo Mode Active
        
        **Note**: Pre-trained models are not available. Showing demo predictions with random data.
        
        To use real predictions, ensure you have:
        1. `arima_model.pkl` - ARIMA model file
        2. `lstm_model.h5` - LSTM model file  
        3. `scaler.pkl` - Data scaler file
        
        Place these files in the same directory as the app.
        """
        
        historical_data, predictions_df = create_demo_predictions(ticker, num_days)
        fig = predictor.create_plot(historical_data, predictions_df, f"{ticker} (DEMO)")
        
        predictions_display = predictions_df.copy()
        predictions_display['Date'] = predictions_display['Date'].dt.strftime('%Y-%m-%d')
        predictions_display = predictions_display.round(2)
        
        return fig, demo_msg, predictions_display
    
    # Normal prediction flow
    return predict_stock_price(ticker, num_days)

# Create Gradio interface
with gr.Blocks(title="Stock Price Forecaster", theme=gr.themes.Soft()) as app:
    gr.Markdown(
        """
        # 📈 Stock Price Forecaster
        
        This app uses pre-trained ARIMA and LSTM models to predict stock prices.
        Enter a stock ticker symbol and the number of days to forecast.
        
        **Models:**
        - 🔵 ARIMA: Statistical time series model
        - 🔴 LSTM: Deep learning sequential model
        - 🟢 Ensemble: Average of both models
        """
    )
    
    with gr.Row():
        with gr.Column(scale=1):
            ticker_input = gr.Textbox(
                label="Stock Ticker Symbol",
                placeholder="AAPL",
                value="AAPL"
            )
            
            days_input = gr.Slider(
                minimum=1,
                maximum=30,
                value=7,
                step=1,
                label="Number of Days to Forecast"
            )
            
            predict_button = gr.Button("🚀 Generate Forecast", variant="primary")
            

    
    with gr.Row():
        with gr.Column(scale=2):
            plot_output = gr.Plot(label="Price Forecast Chart")
    
    with gr.Row():
        summary_output = gr.Markdown(label="Forecast Summary")
    
    with gr.Row():
        predictions_table = gr.Dataframe(
            label="Detailed Predictions",
            headers=["Date", "ARIMA_Prediction", "LSTM_Prediction", "Average_Prediction"],
            datatype=["str", "number", "number", "number"]
        )
    
    # Add examples (use safe function)
    gr.Examples(
        examples=[
            ["AAPL", 7],
        ],
        inputs=[ticker_input, days_input],
        outputs=[plot_output, summary_output, predictions_table],
        fn=predict_stock_price_safe,
        cache_examples=False
    )
    
    # Connect the safe prediction function
    predict_button.click(
        fn=predict_stock_price_safe,
        inputs=[ticker_input, days_input],
        outputs=[plot_output, summary_output, predictions_table]
    )
    
    gr.Markdown(
        """
        ---
        ### 📊 About the Models
        - **ARIMA**: Auto-Regressive Integrated Moving Average model trained on historical price data
        - **LSTM**: Long Short-Term Memory neural network with 3 layers and dropout regularization
        - **Training Data**: Historical stock prices from Yahoo Finance
        
        ### ⚠️ Disclaimer
        This is for educational purposes only. Stock predictions are inherently uncertain and should not be used as the sole basis for investment decisions.
        """
    )

# Launch the app
if __name__ == "__main__":
    app.launch(share=True)