Create app.py

app.py

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+import joblib
+import numpy as np
+import pandas as pd
+import folium
+import streamlit as st
+from streamlit_folium import folium_static
+import warnings
+warnings.filterwarnings("ignore")
+
+# 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'
+    }
+}
+
+# Load model and scaler based on time interval
+def load_model(time_interval):
+    model = joblib.load(model_paths['Path'][time_interval])
+    scaler = joblib.load(scaler_paths['Path'][time_interval])
+    return model, scaler
+
+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 predict_path(time_interval, input_data):
+    model, scaler = load_model(time_interval)
+    processed_data = process_input(input_data, scaler)
+    prediction = model.predict(processed_data)
+    
+    # Create DataFrame for predictions
+    df_predictions = pd.DataFrame(prediction, columns=['LAT', 'LON'])
+    df_predictions['Time'] = [time_interval]
+    return df_predictions
+
+# Function to plot predictions on a folium map and return the HTML representation
+def plot_predictions_on_map(df_predictions):
+    latitudes = df_predictions['LAT'].tolist()
+    longitudes = df_predictions['LON'].tolist()
+
+    m = folium.Map(location=[latitudes[0], longitudes[0]], zoom_start=6)
+    locations = list(zip(latitudes, longitudes))
+
+    for lat, lon in locations:
+        folium.Marker([lat, lon]).add_to(m)
+
+    folium.PolyLine(locations, color='blue', weight=2.5, opacity=0.7).add_to(m)
+    return m
+
+# Streamlit App
+def main():
+    st.title("Cyclone Path Prediction")
+    st.write("Input current and previous cyclone data to predict the path and visualize it on a map.")
+
+    # User inputs
+    time_interval = st.selectbox("Select Prediction Time Interval", [
+        '3 hours', '6 hours', '9 hours', '12 hours', '15 hours', '18 hours', 
+        '21 hours', '24 hours', '27 hours', '30 hours', '33 hours', '36 hours'
+    ])
+
+    previous_lat = st.number_input("Previous Latitude", format="%f")
+    previous_lon = st.number_input("Previous Longitude", format="%f")
+    previous_speed = st.number_input("Previous Speed", format="%f")
+    previous_year = st.number_input("Previous Year", format="%d")
+    previous_month = st.number_input("Previous Month", format="%d")
+    previous_day = st.number_input("Previous Day", format="%d")
+    previous_hour = st.number_input("Previous Hour", format="%d")
+
+    present_lat = st.number_input("Present Latitude", format="%f")
+    present_lon = st.number_input("Present Longitude", format="%f")
+    present_speed = st.number_input("Present Speed", format="%f")
+    present_year = st.number_input("Present Year", format="%d")
+    present_month = st.number_input("Present Month", format="%d")
+    present_day = st.number_input("Present Day", format="%d")
+    present_hour = st.number_input("Present Hour", format="%d")
+
+    if st.button("Predict"):
+        # Process input into array format
+        previous_data = [previous_lat, previous_lon, previous_speed, previous_year, previous_month, previous_day, previous_hour]
+        present_data = [present_lat, present_lon, present_speed, present_year, present_month, present_day, present_hour]
+        input_data = [previous_data, present_data]
+        
+        # Predict path
+        df_predictions = predict_path(time_interval, input_data)
+
+        # Display predicted path DataFrame
+        st.write("Predicted Path DataFrame:")
+        st.write(df_predictions)
+
+        # Plot map with predictions
+        st.write("Cyclone Path Map:")
+        map_ = plot_predictions_on_map(df_predictions)
+        folium_static(map_)
+
+if __name__ == "__main__":
+    main()