import streamlit as st
import numpy as np
import tensorflow as tf
from joblib import load

scaler = load('scaler.joblib')
model = tf.keras.models.load_model("TCM.h5")

st.title("Cover Type Classification App")

with st.form(key='TC23'):
    
    Elevation = st.number_input('Elevation', min_value=0, max_value=10000, value=500)
    Aspect = st.number_input('Aspect', min_value=0, max_value=10000, value=500)
    Slope = st.number_input('Slope', min_value=0, max_value=10000, value=500)
    Horizontal_Distance_To_Hydrology = st.number_input('Horizontal Distance to Hydrology', min_value=0, max_value=10000, value=500)
    Vertical_Distance_To_Hydrology = st.number_input('Vertical Distance to Hydrology', min_value=0, max_value=10000, value=500)
    Horizontal_Distance_To_Roadways = st.number_input('Horizontal Distance to Roadways', min_value=0, max_value=10000, value=500)
    Hillshade_9am = st.number_input('Hillshade at 9am', min_value=0, max_value=10000, value=500)
    Hillshade_Noon = st.number_input('Hillshade at 12pm', min_value=0, max_value=10000, value=500)
    Hillshade_3pm = st.number_input('Hillshade at 3pm', min_value=0, max_value=10000, value=500)
    Horizontal_Distance_To_Fire_Points = st.number_input('Horizontal Distance to Fire Points', min_value=0, max_value=10000, value=500)
    Wilderness_Area = st.number_input('Wilderness Area', min_value=1, max_value=4, value=1)
    Soil_Type = st.number_input('Soil Type', min_value=1, max_value=40, value=1)

    submitted = st.form_submit_button('Predict')

input_data = {
    'Elevation': Elevation,
    'Aspect': Aspect,
    'Slope': Slope,
    'Horizontal_Distance_To_Hydrology': Horizontal_Distance_To_Hydrology,
    'Vertical_Distance_To_Hydrology': Vertical_Distance_To_Hydrology,
    'Horizontal_Distance_To_Roadways': Horizontal_Distance_To_Roadways,
    'Hillshade_9am': Hillshade_9am,
    'Hillshade_Noon': Hillshade_Noon,
    'Hillshade_3pm': Hillshade_3pm,
    'Horizontal_Distance_To_Fire_Points': Horizontal_Distance_To_Fire_Points,
    'Wilderness_Area': Wilderness_Area,  
    'Soil_Type': Soil_Type  
}

class_to_cover_type = {
    0: "Spruce/Fir",
    1: "Lodgepole Pine",
    2: "Ponderosa Pine",
    3: "Cottonwood/Willow",
    4: "Aspen",
    5: "Douglas-fir",
    6: "Krummholz",
}

new_test = np.array(list(input_data.values())).reshape(1, -1)
new_test = scaler.transform(new_test)

if submitted:
    prediction = model.predict(new_test)
    predicted_class = np.argmax(prediction)
    cover_type = class_to_cover_type.get(predicted_class, "Unknown")
    st.write(f"Predicted Cover Type: {cover_type}")