Update app.py

app.py

@@ -17,6 +17,11 @@ model_paths = {
         '30 hours': 'lr_30H_lat_lon.pkl',
         '33 hours': 'lr_33H_lat_lon.pkl',
         '36 hours': 'lr_36H_lat_lon.pkl'
+    },
+    'Speed': {
+        '3 hours': 'Igbm_3H_speed.pkl',
+        '15 hours': 'Igbm_15H_speed.pkl',
+        '27 hours': 'Igbm_27H_speed.pkl'
     }
 }
 
@@ -29,17 +34,34 @@ scaler_paths = {
         '12 hours': 'lr_12H_lat_lon_scaler.pkl',
         '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': 'Igbm_speed_scaler_3H.pkl',
+        '15 hours': 'Igbm_speed_scaler_15H.pkl',
+        '27 hours': 'Igbm_speed_scaler_27H.pkl'
     }
 }
 
-def process_input(input_data, scaler):
+# 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)
-    processed_data = input_data[:2].reshape(1, -1)
+    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
 
@@ -50,31 +72,44 @@ def load_model_and_predict(prediction_type, time_interval, input_data):
         scaler = joblib.load(scaler_paths[prediction_type][time_interval])
         
         # Process input and predict
-        processed_data = process_input(input_data, scaler)
+        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 Prediction App")
+    gr.Markdown("# Cyclone Path and Speed Prediction App")
     
     # Dropdown for Prediction Type
     prediction_type = gr.Dropdown(
-        choices=['Path'],
+        choices=['Path', 'Speed'],
         value='Path',
         label="Select Prediction Type"
     )
     
     # Dropdown for Time Interval
     time_interval = gr.Dropdown(
-        choices=['3 hours', '6 hours', '9 hours', '12 hours', '15 hours', '18 hours', '21 hours', '24 hours', '27 hours', '30 hours', '33 hours', '36 hours'],
+        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)",
@@ -115,7 +150,7 @@ with gr.Blocks() as cyclone_predictor:
         except Exception as e:
             return str(e)
 
-    predict_button = gr.Button("Predict Path")
+    predict_button = gr.Button("Predict")
     
     # Linking function to UI elements
     predict_button.click(