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import gradio as gr
import numpy as np
import joblib
import folium
from folium import Map, Marker
from io import BytesIO
# 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'
}
}
# 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',
'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'
}
}
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':
latitude, longitude = prediction[0][0], prediction[0][1]
return f"Predicted Path after {time_interval}: Latitude: {latitude}, Longitude: {longitude}", display_map(latitude, longitude)
except Exception as e:
return str(e)
def display_map(latitude, longitude):
# Create a map centered around the predicted coordinates
m = folium.Map(location=[latitude, longitude], zoom_start=6)
folium.Marker([latitude, longitude], tooltip="Predicted Location").add_to(m)
# Save map as HTML and load in Gradio
map_data = BytesIO()
m.save(map_data, close_file=False)
return map_data
# Gradio interface components
with gr.Blocks() as cyclone_predictor:
gr.Markdown("# Cyclone Path Prediction App")
prediction_type = gr.Dropdown(choices=['Path'], value='Path', label="Select Prediction Type")
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'],
label="Select Time Interval"
)
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)")
prediction_output = gr.Textbox(label="Prediction Output")
map_output = gr.HTML(label="Predicted Location Map")
def get_input_data(previous_lat_lon, previous_speed, previous_timestamp, present_lat_lon, present_speed, present_timestamp):
try:
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")
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, map_output]
)
cyclone_predictor.launch()
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