Update app.py

app.py

@@ -19,9 +19,9 @@ model_paths = {
         '36 hours': 'lr_36H_lat_lon.pkl'
     },
     'Speed': {
-        '3 hours': 'lgbm_3H_speed.pkl',
-        '15 hours': 'lgbm_15H_speed.pkl',
-        '27 hours': 'lgbm_27H_speed.pkl'
+        '3 hours': 'Igbm_3H_speed.pkl',
+        '15 hours': 'Igbm_15H_speed.pkl',
+        '27 hours': 'Igbm_27H_speed.pkl'
     }
 }
 
@@ -35,130 +35,4 @@ scaler_paths = {
         '15 hours': 'lr_15H_lat_lon_scaler.pkl',
         '18 hours': 'lr_18H_lat_lon_scaler.pkl',
         '21 hours': 'lr_21H_lat_lon_scaler.pkl',
-        '24 hours': 'lr_24H_lat_lon_scaler.pkl',
-        '27 hours': 'lr_27H_lat_lon_scaler.pkl',
-        '30 hours': 'lr_30H_lat_lon_scaler.pkl',
-        '33 hours': 'lr_33H_lat_lon_scaler.pkl',
-        '36 hours': 'lr_36H_lat_lon_scaler.pkl'
-    },
-    'Speed': {
-        '3 hours': 'lgbm_speed_scaler_3H.pkl',
-        '15 hours': 'lgbm_speed_scaler_15H.pkl',
-        '27 hours': 'lgbm_speed_scaler_27H.pkl'
-    }
-}
-
-# Define time intervals for each prediction type
-time_intervals = {
-    'Path': ['3 hours', '6 hours', '9 hours', '12 hours', '15 hours', '18 hours', '21 hours', '24 hours', '27 hours', '30 hours', '33 hours', '36 hours'],
-    'Speed': ['3 hours', '15 hours', '27 hours']
-}
-
-def process_input(input_data, scaler, prediction_type):
-    input_data = np.array(input_data).reshape(-1, 7)
-    if prediction_type == 'Speed':
-        # For speed prediction, reshape accordingly
-        input_data = input_data[:2].reshape(1, 2, 7)
-        processed_data = input_data.reshape(-1, 14)
-    else:  # Path
-        processed_data = input_data[:2].reshape(1, -1)
-    processed_data = scaler.transform(processed_data)
-    return processed_data
-
-def load_model_and_predict(prediction_type, time_interval, input_data):
-    try:
-        # Load the model and scaler based on user selection
-        model = joblib.load(model_paths[prediction_type][time_interval])
-        scaler = joblib.load(scaler_paths[prediction_type][time_interval])
-        
-        # Process input and predict
-        processed_data = process_input(input_data, scaler, prediction_type)
-        prediction = model.predict(processed_data)
-        
-        if prediction_type == 'Path':
-            return f"Predicted Path after {time_interval}: Latitude: {prediction[0][0]}, Longitude: {prediction[0][1]}"
-        elif prediction_type == 'Speed':
-            return f"Predicted Speed after {time_interval}: {prediction[0]}"
-    except Exception as e:
-        return str(e)
-
-# Gradio interface components
-with gr.Blocks() as cyclone_predictor:
-    gr.Markdown("# Cyclone Path and Speed Prediction App")
-    
-    # Dropdown for Prediction Type
-    prediction_type = gr.Dropdown(
-        choices=['Path', 'Speed'],
-        value='Path',
-        label="Select Prediction Type"
-    )
-    
-    # Dropdown for Time Interval
-    time_interval = gr.Dropdown(
-        choices=time_intervals['Path'],
-        label="Select Time Interval"
-    )
-    
-    # Function to update time intervals based on prediction type
-    def update_time_intervals(prediction_type):
-        return gr.Dropdown.update(choices=time_intervals[prediction_type])
-    
-    # Update time intervals when prediction type changes
-    prediction_type.change(
-        fn=update_time_intervals,
-        inputs=prediction_type,
-        outputs=time_interval
-    )
-    
-    # Input fields for user data
-    previous_lat_lon = gr.Textbox(
-        placeholder="Enter previous 3-hour lat/lon (e.g., 15.54,90.64)",
-        label="Previous 3-hour Latitude/Longitude"
-    )
-    previous_speed = gr.Number(label="Previous 3-hour Speed")  # Removed placeholder
-    previous_timestamp = gr.Textbox(
-        placeholder="Enter previous 3-hour timestamp (e.g., 2024,10,23,0)",
-        label="Previous 3-hour Timestamp (year, month, day, hour)"
-    )
-
-    present_lat_lon = gr.Textbox(
-        placeholder="Enter present 3-hour lat/lon (e.g., 15.71,90.29)",
-        label="Present 3-hour Latitude/Longitude"
-    )
-    present_speed = gr.Number(label="Present 3-hour Speed")  # Removed placeholder
-    present_timestamp = gr.Textbox(
-        placeholder="Enter present 3-hour timestamp (e.g., 2024,10,23,3)",
-        label="Present 3-hour Timestamp (year, month, day, hour)"
-    )
-
-    # Output prediction
-    prediction_output = gr.Textbox(label="Prediction Output")
-    
-    # Predict button
-    def get_input_data(previous_lat_lon, previous_speed, previous_timestamp, present_lat_lon, present_speed, present_timestamp):
-        try:
-            # Parse inputs into required format
-            prev_lat, prev_lon = map(float, previous_lat_lon.split(','))
-            prev_time = list(map(int, previous_timestamp.split(',')))
-            previous_data = [prev_lat, prev_lon, previous_speed] + prev_time
-
-            present_lat, present_lon = map(float, present_lat_lon.split(','))
-            present_time = list(map(int, present_timestamp.split(',')))
-            present_data = [present_lat, present_lon, present_speed] + present_time
-
-            return [previous_data, present_data]
-        except Exception as e:
-            return str(e)
-
-    predict_button = gr.Button("Predict")
-    
-    # Linking function to UI elements
-    predict_button.click(
-        fn=lambda pt, ti, p_lat_lon, p_speed, p_time, c_lat_lon, c_speed, c_time: load_model_and_predict(
-            pt, ti, get_input_data(p_lat_lon, p_speed, p_time, c_lat_lon, c_speed, c_time)
-        ),
-        inputs=[prediction_type, time_interval, previous_lat_lon, previous_speed, previous_timestamp, present_lat_lon, present_speed, present_timestamp],
-        outputs=prediction_output
-    )
-
-cyclone_predictor.launch()
+        '24 hours': 'lr_24H_lat