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app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from statsmodels.tsa.arima.model import ARIMA
+from tensorflow import keras  # Use full TensorFlow Keras for custom_objects
+import joblib
+from datetime import datetime, timedelta
+import warnings
+warnings.filterwarnings('ignore')
+
+# Load your saved models/data (upload these files to Colab if needed via !wget or upload)
+arima_model = ARIMA(pd.read_pickle('final_df.pkl')['Target'].tail(750), order=(5,1,0)).fit()  # Re-fit or load saved
+
+# Fixed: Load LSTM with custom_objects to resolve 'mse' deserialization issue
+lstm_model = keras.models.load_model('lstm_model.h5', 
+                                     custom_objects={'mse': keras.losses.MeanSquaredError()})
+
+target_scaler = joblib.load('target_scaler.pkl')
+final_df = pd.read_pickle('final_df.pkl')
+
+def forecast_aapl(start_date_str, model_type='LSTM', days=30):
+    try:
+        start_date = pd.to_datetime(start_date_str)
+        # Simple forecast logic (adapt from your notebook)
+        if model_type == 'ARIMA':
+            forecast = arima_model.forecast(steps=days)
+        else:  # LSTM
+            # Use last window for input (simplified)
+            recent_data = final_df.tail(30).values  # n_lags=30
+            X_input = recent_data.reshape(1, 30, recent_data.shape[1])
+            preds_scaled = lstm_model.predict(X_input)[0][0]
+            forecast = np.full(days, target_scaler.inverse_transform([[preds_scaled]])[0][0])  # Placeholder; extend for multi-step
+
+        future_dates = pd.date_range(start=start_date, periods=days, freq='D')
+        results_df = pd.DataFrame({'Date': future_dates, 'Forecast': forecast})
+
+        # Plot
+        fig, ax = plt.subplots(figsize=(10, 5))
+        ax.plot(results_df['Date'], results_df['Forecast'], marker='o', label=f'{model_type} Forecast')
+        ax.set_title(f'AAPL {days}-Day Forecast from {start_date_str}')
+        ax.set_xlabel('Date')
+        ax.set_ylabel('Price ($)')
+        ax.legend()
+        ax.grid(True)
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        return fig, results_df.to_string(index=False)
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=forecast_aapl,
+    inputs=[
+        gr.Textbox(label="Start Date (YYYY-MM-DD)", value="2023-01-01"),
+        gr.Dropdown(choices=['LSTM', 'ARIMA'], label="Model", value='LSTM'),
+        gr.Slider(1, 90, value=30, label="Forecast Days")
+    ],
+    outputs=[gr.Plot(label="Forecast Plot"), gr.Textbox(label="Forecast Table")],
+    title="AAPL Stock Price Forecaster",
+    description="Enter a start date to get future AAPL price forecasts using ARIMA or LSTM."
+)
+
+# Launch locally first (test in Colab)
+iface.launch(share=True, debug=True)  # This gives a public link for testing