Update app.py

app.py

@@ -2,12 +2,11 @@ import gradio as gr
 import pandas as pd
 import numpy as np
 from statsmodels.tsa.arima.model import ARIMA
-from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
-from sklearn.preprocessing import MinMaxScaler
 import plotly.graph_objects as go
 from tensorflow import keras
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, Dense, Dropout
+from sklearn.preprocessing import MinMaxScaler
 
 def forecast_arima(days_ahead):
     df = pd.read_excel("Microsoft_stock_data.xlsx")
@@ -39,6 +38,7 @@ def forecast_lstm(days_ahead):
     scaler = MinMaxScaler(feature_range=(0, 1))
     scaled_data = scaler.fit_transform(data)
     
+
     lookback = 60
     X_train, y_train = [], []
     for i in range(lookback, len(scaled_data)):
@@ -47,7 +47,7 @@ def forecast_lstm(days_ahead):
     
     X_train, y_train = np.array(X_train), np.array(y_train)
     X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
-    
+
     model = Sequential([
         LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)),
         Dropout(0.2),
@@ -60,6 +60,7 @@ def forecast_lstm(days_ahead):
     model.compile(optimizer='adam', loss='mean_squared_error')
     model.fit(X_train, y_train, batch_size=32, epochs=10, verbose=0)
     
+
     last_sequence = scaled_data[-lookback:]
     forecast = []
     
@@ -70,6 +71,7 @@ def forecast_lstm(days_ahead):
     
     forecast = scaler.inverse_transform(np.array(forecast).reshape(-1, 1))
     
+ 
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:, 0], name='Historical', line=dict(color='blue')))
     future_dates = pd.date_range(start=df['Date'].iloc[-1], periods=int(days_ahead)+1)[1:]
@@ -85,11 +87,15 @@ def forecast_comparison(days_ahead):
     
     data = df['Close'].values
     
+
     arima_model = ARIMA(data, order=(1,1,1))
     arima_fitted = arima_model.fit()
     arima_forecast = arima_fitted.forecast(steps=int(days_ahead))
     
-    scaled_data = MinMaxScaler(feature_range=(0, 1)).fit_transform(data.reshape(-1, 1))
+
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data.reshape(-1, 1))
+    
     lookback = 60
     X_train, y_train = [], []
     for i in range(lookback, len(scaled_data)):
@@ -111,7 +117,6 @@ def forecast_comparison(days_ahead):
     lstm_model.compile(optimizer='adam', loss='mean_squared_error')
     lstm_model.fit(X_train, y_train, batch_size=32, epochs=10, verbose=0)
     
-    scaler = MinMaxScaler(feature_range=(0, 1)).fit(data.reshape(-1, 1))
     last_sequence = scaled_data[-lookback:]
     lstm_forecast = []
     
@@ -122,15 +127,17 @@ def forecast_comparison(days_ahead):
     
     lstm_forecast = scaler.inverse_transform(np.array(lstm_forecast).reshape(-1, 1)).flatten()
     
+
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:], name='Historical', line=dict(color='blue')))
     future_dates = pd.date_range(start=df['Date'].iloc[-1], periods=int(days_ahead)+1)[1:]
     fig.add_trace(go.Scatter(x=future_dates, y=arima_forecast, name='ARIMA Forecast', line=dict(color='red', dash='dash')))
     fig.add_trace(go.Scatter(x=future_dates, y=lstm_forecast, name='LSTM Forecast', line=dict(color='green', dash='dot')))
-    fig.update_layout(title='ARIMA vs LSTM: Stock Price Forecast Comparison', xaxis_title='Date', yaxis_title='Price')
+    fig.update_layout(title='ARIMA vs LSTM: Comparison', xaxis_title='Date', yaxis_title='Price')
     
     return fig
 
+
 with gr.Blocks() as demo:
     gr.Markdown("# 📈 Time Series Forecasting: ARIMA vs LSTM")
     gr.Markdown("**Microsoft Stock Price Forecasting** - Compare ARIMA and LSTM models.")
@@ -140,15 +147,22 @@ with gr.Blocks() as demo:
     with gr.Tabs():
         with gr.Tab("ARIMA Model"):
             arima_plot = gr.Plot()
-            days.change(forecast_arima, days, arima_plot)
-            demo.load(forecast_arima, days, arima_plot)
+            arima_btn = gr.Button("Generate ARIMA Forecast")
+            arima_btn.click(forecast_arima, inputs=days, outputs=arima_plot)
+     
+            demo.load(forecast_arima, inputs=days, outputs=arima_plot)
         
         with gr.Tab("LSTM Model"):
             lstm_plot = gr.Plot()
-            days.change(forecast_lstm, days, lstm_plot)
+            lstm_btn = gr.Button("Generate LSTM Forecast")
+            lstm_btn.click(forecast_lstm, inputs=days, outputs=lstm_plot)
         
         with gr.Tab("Comparison"):
             comparison_plot = gr.Plot()
-            days.change(forecast_comparison, days, comparison_plot)
+            compare_btn = gr.Button("Compare Both Models")
+            compare_btn.click(forecast_comparison, inputs=days, outputs=comparison_plot)
+    
+
+    days.change(forecast_arima, inputs=days, outputs=arima_plot)
 
 demo.launch()