Delete app.py

app.py

@@ -1,406 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
-import yfinance as yf
-from statsmodels.tsa.arima.model import ARIMA
-from prophet import Prophet
-import warnings
-warnings.filterwarnings('ignore')
-
-# NO PRE-TRAINED MODELS - Train on demand with user's data
-# This avoids the 50GB storage limit issue
-
-def fetch_stock_data(ticker, days=730):
-    """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']
-        df = df.dropna()
-        return df, None
-    except Exception as e:
-        return None, str(e)
-
-def make_arima_forecast(data, days):
-    """Train ARIMA and make forecast"""
-    try:
-        # Train ARIMA model on-the-fly
-        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):
-    """Train Prophet and make forecast"""
-    try:
-        # Prepare data for Prophet
-        prophet_data = pd.DataFrame({
-            'ds': data.index,
-            'y': data['Price'].values
-        })
-        
-        # Create and train model on-the-fly
-        model = Prophet(
-            daily_seasonality=False,
-            weekly_seasonality=True,
-            yearly_seasonality=True,
-            changepoint_prior_scale=0.05,
-            seasonality_mode='multiplicative'
-        )
-        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_simple_ml_forecast(data, days):
-    """Simple exponential smoothing forecast (lightweight alternative to LSTM)"""
-    try:
-        from statsmodels.tsa.holtwinters import ExponentialSmoothing
-        
-        # Train exponential smoothing model
-        model = ExponentialSmoothing(
-            data['Price'], 
-            seasonal_periods=30,
-            trend='add',
-            seasonal='add'
-        )
-        fitted = model.fit()
-        forecast = fitted.forecast(steps=days)
-        return forecast.values
-    except Exception as e:
-        print(f"ML Forecast Error: {e}")
-        return None
-
-def calculate_moving_average_forecast(data, days, window=20):
-    """Simple moving average forecast"""
-    try:
-        ma = data['Price'].rolling(window=window).mean().iloc[-1]
-        trend = (data['Price'].iloc[-1] - data['Price'].iloc[-window]) / window
-        forecast = [ma + trend * i for i in range(1, days + 1)]
-        return np.array(forecast)
-    except Exception as e:
-        print(f"MA Error: {e}")
-        return None
-
-def create_forecast_plot(historical_data, forecasts, ticker, model_names):
-    """Create interactive plotly chart"""
-    fig = go.Figure()
-    
-    # Show last 90 days of historical data for clarity
-    recent_data = historical_data.tail(90)
-    
-    # Historical data
-    fig.add_trace(go.Scatter(
-        x=recent_data.index,
-        y=recent_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', 'green']
-    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=4)
-            ))
-    
-    # Add vertical line at prediction start
-    fig.add_vline(
-        x=last_date, 
-        line_dash="dash", 
-        line_color="gray",
-        annotation_text="Forecast Start"
-    )
-    
-    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,
-            bgcolor="rgba(255, 255, 255, 0.8)"
-        )
-    )
-    
-    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()
-    
-    # Show loading message
-    status_msg = f"🔄 Fetching data for {ticker}..."
-    
-    # Fetch data (2 years for better training)
-    data, error = fetch_stock_data(ticker, days=730)
-    if error:
-        return None, f"❌ Error: {error}", None
-    
-    if len(data) < 60:
-        return None, f"❌ Insufficient data for {ticker}. Need at least 60 days of history.", None
-    
-    status_msg += f"\n✅ Found {len(data)} days of data\n🔄 Training models..."
-    
-    # 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", "Exp. Smoothing"]:
-        ml_forecast = make_simple_ml_forecast(data, forecast_days)
-        if ml_forecast is not None:
-            forecasts.append(ml_forecast)
-            model_names.append("Exp. Smoothing")
-    
-    if model_choice in ["All Models", "Moving Average"]:
-        ma_forecast = calculate_moving_average_forecast(data, forecast_days)
-        if ma_forecast is not None:
-            forecasts.append(ma_forecast)
-            model_names.append("Moving Average")
-    
-    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} ($)'] = np.round(forecast, 2)
-    
-    # Calculate statistics
-    current_price = data['Price'].iloc[-1]
-    avg_forecast = np.mean([f[-1] for f in forecasts])
-    avg_change = ((avg_forecast - current_price) / current_price) * 100
-    
-    # Summary statistics
-    summary = f"""
-## 📊 Forecast Summary for **{ticker}**
-
-### Current Information
-- **Current Price**: ${current_price:.2f}
-- **Data Points**: {len(data)} days
-- **Last Updated**: {data.index[-1].strftime('%Y-%m-%d')}
-
-### Forecast Details
-- **Forecast Period**: {forecast_days} days
-- **Models Used**: {', '.join(model_names)}
-- **End Date**: {future_dates[-1].strftime('%Y-%m-%d')}
-
-### Predicted Prices (Day {forecast_days})
-"""
-    
-    for forecast, name in zip(forecasts, model_names):
-        final_price = forecast[-1]
-        change = ((final_price - current_price) / current_price) * 100
-        emoji = "📈" if change > 0 else "📉"
-        summary += f"\n{emoji} **{name}**: ${final_price:.2f} ({change:+.2f}%)"
-    
-    summary += f"""
-
-### Average Prediction
-- **Average Price**: ${avg_forecast:.2f}
-- **Expected Change**: {avg_change:+.2f}%
-
----
-⚠️ **Risk Warning**: Past performance does not guarantee future results. Use for research only.
-"""
-    
-    return fig, summary, forecast_df
-
-# Create Gradio Interface
-with gr.Blocks(title="Stock Price Forecasting", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 📈 AI Stock Price Forecasting
-        
-        ### Predict future stock prices using multiple time-series models
-        
-        This app trains models **in real-time** using the latest stock data. No pre-trained models needed!
-        
-        **✨ Features:**
-        - Real-time data from Yahoo Finance
-        - Multiple forecasting algorithms
-        - Interactive visualizations
-        - No storage limits - models train on demand
-        
-        ---
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 🎯 Input Parameters")
-            
-            ticker_input = gr.Textbox(
-                label="📊 Stock Ticker Symbol",
-                placeholder="e.g., AAPL, GOOGL, TSLA, MSFT",
-                value="AAPL",
-                info="Enter any valid stock ticker"
-            )
-            
-            forecast_days = gr.Slider(
-                minimum=7,
-                maximum=90,
-                value=30,
-                step=1,
-                label="📅 Forecast Period (Days)",
-                info="Number of days to forecast"
-            )
-            
-            model_choice = gr.Radio(
-                choices=["All Models", "ARIMA", "Prophet", "Exp. Smoothing", "Moving Average"],
-                value="All Models",
-                label="🤖 Select Model(s)",
-                info="Choose which forecasting model to use"
-            )
-            
-            predict_btn = gr.Button(
-                "🔮 Generate Forecast", 
-                variant="primary", 
-                size="lg",
-                scale=1
-            )
-            
-            gr.Markdown(
-                """
-                ### 💡 Quick Tips
-                - Use 30 days for short-term
-                - Use 60-90 days for trends
-                - "All Models" shows comparison
-                """
-            )
-        
-        with gr.Column(scale=2):
-            output_plot = gr.Plot(label="📈 Forecast Visualization")
-    
-    with gr.Row():
-        with gr.Column():
-            output_summary = gr.Markdown(label="📋 Analysis Summary")
-    
-    with gr.Row():
-        output_table = gr.Dataframe(
-            label="📊 Detailed Forecast Table",
-            wrap=True,
-            interactive=False,
-            height=400
-        )
-    
-    # Examples
-    gr.Markdown("### 🎯 Try These Examples")
-    gr.Examples(
-        examples=[
-            ["AAPL", 30, "All Models"],
-            ["GOOGL", 14, "Prophet"],
-            ["TSLA", 60, "ARIMA"],
-            ["MSFT", 45, "Exp. Smoothing"],
-            ["NVDA", 30, "All Models"],
-        ],
-        inputs=[ticker_input, forecast_days, model_choice],
-        label="Popular Stocks"
-    )
-    
-    # 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
-        
-        | Model | Best For | Speed | Accuracy |
-        |-------|----------|-------|----------|
-        | **ARIMA** | Short-term, stationary data | ⚡⚡⚡ Fast | ⭐⭐⭐ |
-        | **Prophet** | Seasonality, trends | ⚡⚡ Medium | ⭐⭐⭐⭐ |
-        | **Exp. Smoothing** | Smooth trends | ⚡⚡⚡ Fast | ⭐⭐⭐ |
-        | **Moving Average** | Simple baseline | ⚡⚡⚡⚡ Very Fast | ⭐⭐ |
-        
-        ## ⚠️ Important Disclaimer
-        
-        **This tool is for educational and research purposes only.**
-        
-        - Stock predictions are inherently uncertain
-        - Past performance ≠ future results
-        - Always do your own research
-        - Consult financial advisors before investing
-        - Never invest more than you can afford to lose
-        
-        ## 🔒 Privacy & Data
-        
-        - No data is stored permanently
-        - Models train fresh for each prediction
-        - Stock data fetched from Yahoo Finance API
-        - No personal information collected
-        
-        ---
-        
-        **Made with ❤️ using Gradio & Python**
-        """
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(
-        share=False,
-        show_error=True,
-        server_name="0.0.0.0",
-        server_port=7860
-    )