Update app.py

app.py

@@ -1,44 +1,39 @@
 import gradio as gr
 import numpy as np
 import joblib
-from sklearn.preprocessing import StandardScaler
-import os
 
 # Define model paths
 model_paths = {
     'Path': {
-        '3 hours': 'path_to_3H_model.pkl',
-        '6 hours': 'path_to_6H_model.pkl',
-        '9 hours': 'path_to_9H_model.pkl'
-    },
-    'Speed': {
-        '3 hours': 'path_to_3H_speed_model.pkl',
-        '6 hours': 'path_to_6H_speed_model.pkl',
-        '9 hours': 'path_to_9H_speed_model.pkl'
-    },
-    'Pressure': {
-        '3 hours': 'path_to_3H_pressure_model.pkl',
-        '6 hours': 'path_to_6H_pressure_model.pkl',
-        '9 hours': 'path_to_9H_pressure_model.pkl'
+        '3 hours': 'lr_3H_lat_lon.pkl',
+        '6 hours': 'lr_6H_lat_lon.pkl',
+        '9 hours': 'lr_9H_lat_lon.pkl',
+        '12 hours': 'lr_12H_lat_lon.pkl',
+        '15 hours': 'lr_15H_lat_lon.pkl',
+        '18 hours': 'lr_18H_lat_lon.pkl',
+        '21 hours': 'lr_21H_lat_lon.pkl',
+        '24 hours': 'lr_24H_lat_lon.pkl',
+        '27 hours': 'lr_27H_lat_lon.pkl',
+        '30 hours': 'lr_30H_lat_lon.pkl',
+        '33 hours': 'lr_33H_lat_lon.pkl',
+        '36 hours': 'lr_36H_lat_lon.pkl'
     }
 }
 
 # Define scaler paths
 scaler_paths = {
     'Path': {
-        '3 hours': 'path_to_3H_scaler.pkl',
-        '6 hours': 'path_to_6H_scaler.pkl',
-        '9 hours': 'path_to_9H_scaler.pkl'
-    },
-    'Speed': {
-        '3 hours': 'path_to_3H_speed_scaler.pkl',
-        '6 hours': 'path_to_6H_speed_scaler.pkl',
-        '9 hours': 'path_to_9H_speed_scaler.pkl'
-    },
-    'Pressure': {
-        '3 hours': 'path_to_3H_pressure_scaler.pkl',
-        '6 hours': 'path_to_6H_pressure_scaler.pkl',
-        '9 hours': 'path_to_9H_pressure_scaler.pkl'
+        '3 hours': 'lr_3H_lat_lon_scaler.pkl',
+        '6 hours': 'lr_6H_lat_lon_scaler.pkl',
+        '9 hours': 'lr_9H_lat_lon_scaler.pkl',
+        '12 hours': 'lr_12H_lat_lon_scaler.pkl',
+        '15 hours': 'lr_15H_lat_lon_scaler.pkl',
+        '18 hours': 'lr_18H_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'
     }
 }
 
@@ -59,46 +54,75 @@ def load_model_and_predict(prediction_type, time_interval, input_data):
         prediction = model.predict(processed_data)
         
         if prediction_type == 'Path':
-            return f"Predicted Latitude: {prediction[0][0]}, Predicted Longitude: {prediction[0][1]}"
-        elif prediction_type == 'Speed':
-            return f"Predicted Speed: {prediction[0]}"
-        elif prediction_type == 'Pressure':
-            return f"Predicted Pressure: {prediction[0]}"
+            return f"Predicted Path after {time_interval}: Latitude: {prediction[0][0]}, Longitude: {prediction[0][1]}"
     except Exception as e:
         return str(e)
 
 # Gradio interface components
 with gr.Blocks() as cyclone_predictor:
-    gr.Markdown("# Cyclone Prediction App")
+    gr.Markdown("# Cyclone Path Prediction App")
     
     # Dropdown for Prediction Type
     prediction_type = gr.Dropdown(
-        choices=['Path', 'Speed', 'Pressure'],
+        choices=['Path'],
+        value='Path',
         label="Select Prediction Type"
     )
     
     # Dropdown for Time Interval
     time_interval = gr.Dropdown(
-        choices=['3 hours', '6 hours', '9 hours'],
+        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'],
         label="Select Time Interval"
     )
     
     # Input fields for user data
-    input_data = gr.Textbox(
-        placeholder="Enter cyclone data as list of lists, e.g., [[15.54,90.64,31,2024,10,23,0], [15.71,90.29,32,2024,10,23,3]]",
-        label="Input Cyclone Data (2 rows required)"
+    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", placeholder="e.g., 31")
+    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", placeholder="e.g., 32")
+    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
-    predict_button = gr.Button("Predict")
+    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 Path")
     
     # Linking function to UI elements
     predict_button.click(
-        load_model_and_predict,
-        inputs=[prediction_type, time_interval, input_data],
+        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
     )