Create app.py

app.py

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+import yfinance as yf
+import pandas as pd
+import numpy as np
+from prophet import Prophet
+import plotly.graph_objs as go
+import plotly.express as px
+import gradio as gr
+from pmdarima import auto_arima
+
+def forecast_stock(ticker, period, future_days, use_arima):
+    # Fetch data
+    data = yf.Ticker(ticker)
+    df = data.history(period=period)
+    
+    if df.empty:
+        return "Could not retrieve data for the selected ticker."
+    
+    df = df.reset_index()
+    df = df[['Date', 'Close']]
+    df.columns = ['ds', 'y']
+    df['ds'] = pd.to_datetime(df['ds']).dt.tz_localize(None)
+    df = df.dropna()
+    
+    # Prophet forecast
+    model = Prophet()
+    model.fit(df)
+    future_dates = model.make_future_dataframe(periods=future_days)
+    forecast = model.predict(future_dates)
+    
+    # Create Plotly figure for forecast
+    fig_forecast = go.Figure()
+    fig_forecast.add_trace(go.Scatter(x=df['ds'], y=df['y'], name='Historical'))
+    fig_forecast.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], name='Prophet Forecast'))
+    fig_forecast.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], name='Prophet Upper Bound', line=dict(dash='dash')))
+    fig_forecast.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], name='Prophet Lower Bound', line=dict(dash='dash')))
+    
+    if use_arima:
+        # ARIMA forecast with automatic order selection
+        model_arima = auto_arima(df['y'], seasonal=False, trace=True)
+        results_arima = model_arima.fit(df['y'])
+        arima_forecast = results_arima.predict(n_periods=future_days)
+        future_dates_arima = pd.date_range(start=df['ds'].iloc[-1] + pd.Timedelta(days=1), periods=future_days)
+        
+        fig_forecast.add_trace(go.Scatter(x=future_dates_arima, y=arima_forecast, name='ARIMA Forecast'))
+    
+    fig_forecast.update_layout(title=f'Stock Price Forecast for {ticker}', xaxis_title='Date', yaxis_title='Stock Price')
+    
+    # Create Plotly figure for Prophet components
+    fig_components = px.line(forecast, x='ds', y=['trend', 'yearly', 'weekly'])
+    fig_components.update_layout(title='Prophet Forecast Components')
+    
+    return fig_forecast, fig_components
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=forecast_stock,
+    inputs=[
+        gr.Dropdown(choices=['AAPL', 'GOOGL', 'MSFT', 'AMZN'], label="Stock Ticker"),
+        gr.Dropdown(choices=['1y', '2y', '5y', '10y', 'max'], label="Historical Data Period"),
+        gr.Slider(minimum=30, maximum=365, step=30, label="Days to Forecast"),
+        gr.Checkbox(label="Include ARIMA Forecast")
+    ],
+    outputs=[
+        gr.Plot(label="Forecast"),
+        gr.Plot(label="Prophet Forecast Components")
+    ],
+    title="Stock Price Forecasting with Prophet and ARIMA",
+    description="Select a stock, historical data period, forecast horizon, and whether to include ARIMA forecast."
+)
+
+# Launch the interface
+iface.launch()