Update app.py

app.py

@@ -35,4 +35,130 @@ 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
+        '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': 'Igbm_speed_scaler_3H.pkl',
+        '15 hours': 'Igbm_speed_scaler_15H.pkl',
+        '27 hours': 'Igbm_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_value):
+        return gr.update(choices=time_intervals[prediction_type_value])
+    
+    # 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")
+    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")
+    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()