import gradio as gr
import numpy as np
import joblib

# Define model paths
model_paths = { 
    'Path': {
        '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'
    },
    'Speed': {
        '3 hours': 'lgbm_3H_speed.pkl',
        '15 hours': 'lgbm_15H_speed.pkl',
        '27 hours': 'lgbm_27H_speed.pkl'
    }
}

# Define scaler paths
scaler_paths = {
    'Path': {
        '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',
        '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_scale_3H.pkl',
        '15 hours': 'lgbm_speed_scale_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_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()