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()