Update app.py

app.py

@@ -1,13 +1,11 @@
 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):
+def forecast_stock(ticker, period, future_days):
     # Fetch data
     data = yf.Ticker(ticker)
     df = data.history(period=period)
@@ -21,33 +19,25 @@ def forecast_stock(ticker, period, future_days, use_arima):
     df['ds'] = pd.to_datetime(df['ds']).dt.tz_localize(None)
     df = df.dropna()
     
-    # Prophet forecast
+    # Fit Prophet model
     model = Prophet()
     model.fit(df)
+    
+    # Make future dataframe
     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')
+    fig_forecast.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], name='Forecast'))
+    fig_forecast.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], name='Upper Bound', line=dict(dash='dash')))
+    fig_forecast.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], name='Lower Bound', line=dict(dash='dash')))
+    fig_forecast.update_layout(title=f'Prophet Forecast for {ticker}', xaxis_title='Date', yaxis_title='Stock Price')
     
-    # Create Plotly figure for Prophet components
+    # Create Plotly figure for components
     fig_components = px.line(forecast, x='ds', y=['trend', 'yearly', 'weekly'])
-    fig_components.update_layout(title='Prophet Forecast Components')
+    fig_components.update_layout(title='Forecast Components')
     
     return fig_forecast, fig_components
 
@@ -57,15 +47,14 @@ iface = gr.Interface(
     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")
+        gr.Slider(minimum=30, maximum=365, step=30, label="Days to Forecast")
     ],
     outputs=[
         gr.Plot(label="Forecast"),
-        gr.Plot(label="Prophet Forecast Components")
+        gr.Plot(label="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."
+    title="Stock Price Forecasting with Prophet",
+    description="Select a stock, historical data period, and forecast horizon to predict future stock prices."
 )
 
 # Launch the interface