Create app.py

app.py

@@ -0,0 +1,105 @@
+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'
+    }
+}
+
+# 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'
+    }
+}
+
+def process_input(input_data, scaler):
+    input_data = np.array(input_data).reshape(-1, 7)
+    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 = 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]}"
+    except Exception as e:
+        return str(e)
+
+# Gradio interface components
+with gr.Blocks() as cyclone_predictor:
+    gr.Markdown("# Cyclone Prediction App")
+    
+    # Dropdown for Prediction Type
+    prediction_type = gr.Dropdown(
+        choices=['Path', 'Speed', 'Pressure'],
+        label="Select Prediction Type"
+    )
+    
+    # Dropdown for Time Interval
+    time_interval = gr.Dropdown(
+        choices=['3 hours', '6 hours', '9 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)"
+    )
+    
+    # Output prediction
+    prediction_output = gr.Textbox(label="Prediction Output")
+    
+    # Predict button
+    predict_button = gr.Button("Predict")
+    
+    # Linking function to UI elements
+    predict_button.click(
+        load_model_and_predict,
+        inputs=[prediction_type, time_interval, input_data],
+        outputs=prediction_output
+    )
+
+cyclone_predictor.launch()