import streamlit as st
import pandas as pd
import pickle

# Load the fitted model
model = pickle.load(open('model (10).pkl', 'rb'))

# Mapping antara kelas dan nama tipe almond
class_mapping = {
    0: 'Sanora',
    1: 'Mamra',
    2: 'Regular',
    # tambahkan kelas lainnya sesuai kebutuhan
}

st.title('Almond Classification')
st.write('This web app classifies almonds based on your input features.')

# Input untuk setiap fitur
length_major_axis = st.number_input('Length (major axis)', min_value=269.356903, max_value=279.879883)
width_minor_axis = st.number_input('Width (minor axis)', min_value=176.023636, max_value=227.940628)
thickness_depth = st.number_input('Thickness (depth)', min_value=107.253448, max_value=127.795132)
area = st.number_input('Area', min_value=18471.5, max_value=36683.0)
perimeter = st.number_input('Perimeter', min_value=551.688379, max_value=887.310743)
roundness = st.slider('Roundness', min_value=0.472718, max_value=0.643761, step=0.01)
solidity = st.slider('Solidity', min_value=0.931800, max_value=0.973384, step=0.01)
compactness = st.slider('Compactness', min_value=1.383965, max_value=1.764701, step=0.01)
aspect_ratio = st.slider('Aspect Ratio', min_value=1.530231, max_value=1.705716, step=0.01)
eccentricity = st.slider('Eccentricity', min_value=0.75693, max_value=0.81012, step=0.01)
extent = st.slider('Extent', min_value=0.656535, max_value=0.725739, step=0.01)
convex_area = st.number_input('Convex hull (convex area)', min_value=18068.0, max_value=36683.0, step=0.01)

# Tombol untuk memprediksi
if st.button('Predict'):
        # Muat scaler
    scaler = pickle.load(open('scaler.pkl', 'rb'))
    
    # Input dari pengguna
    input_features = [[length_major_axis, width_minor_axis, thickness_depth, area,
                       perimeter, roundness, solidity, compactness, aspect_ratio,
                       eccentricity, extent, convex_area]]
    
    # Lakukan scaling pada input
    input_features_scaled = scaler.transform(input_features)
    
    # Prediksi menggunakan model
    prediction = model.predict(input_features_scaled)



    # Menggunakan mapping untuk mendapatkan nama tipe almond
    predicted_class_name = class_mapping[prediction[0]]
    
    st.write(f'The predicted class is: {predicted_class_name}')