import streamlit as st
import pandas as pd
import pickle

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

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'):
#     input_features = [[length_major_axis, width_minor_axis, thickness_depth, area,
#                        perimeter, roundness, solidity, compactness, aspect_ratio,
#                        eccentricity, extent, convex_area]]
#     prediction = model.predict(input_features)
#     st.write(f'The predicted class is: {prediction[0]}')

# # Tombol untuk memprediksi
# if st.button('Predict'):
#     input_features = [[length_major_axis, width_minor_axis, thickness_depth, area,
#                        perimeter, roundness, solidity, compactness, aspect_ratio,
#                        eccentricity, extent, convex_area]]
#     prediction = model.predict(input_features)
#     prediction_proba = model.predict_proba(input_features)

#     st.write(f'The predicted class is: {prediction[0]}')
#     st.write(f'Prediction probabilities: {prediction_proba}')

# Input untuk beberapa fitur
num_samples = st.number_input('Number of samples', min_value=1, max_value=10, value=1)

input_features = []
for i in range(num_samples):
    st.write(f'Sample {i + 1}')
    features = []
    length_major_axis = st.number_input('Length (major axis)', min_value=269.356903, max_value=279.879883, key=f'length_{i}')
    width_minor_axis = st.number_input('Width (minor axis)', min_value=176.023636, max_value=227.940628, key=f'width_{i}')
    thickness_depth = st.number_input('Thickness (depth)', min_value=107.253448, max_value=127.795132, key=f'thickness_{i}')
    area = st.number_input('Area', min_value=18471.5, max_value=36683.0, key=f'area_{i}')
    perimeter = st.number_input('Perimeter', min_value=551.688379, max_value=887.310743, key=f'perimeter_{i}')
    roundness = st.slider('Roundness', min_value=0.472718, max_value=0.643761, step=0.01, key=f'roundness_{i}')
    solidity = st.slider('Solidity', min_value=0.931800, max_value=0.973384, step=0.01, key=f'solidity_{i}')
    compactness = st.slider('Compactness', min_value=1.383965, max_value=1.764701, step=0.01, key=f'compactness_{i}')
    aspect_ratio = st.slider('Aspect Ratio', min_value=1.530231, max_value=1.705716, step=0.01, key=f'aspect_ratio_{i}')
    eccentricity = st.slider('Eccentricity', min_value=0.75693, max_value=0.81012, step=0.01, key=f'eccentricity_{i}')
    extent = st.slider('Extent', min_value=0.656535, max_value=0.725739, step=0.01, key=f'extent_{i}')
    convex_area = st.number_input('Convex hull (convex area)', min_value=18068.0, max_value=36683.0, step=0.01, key=f'convex_area_{i}')

    features = [length_major_axis, width_minor_axis, thickness_depth, area,
                perimeter, roundness, solidity, compactness, aspect_ratio,
                eccentricity, extent, convex_area]
    input_features.append(features)

# Tombol untuk memprediksi
if st.button('Predict'):
    predictions = best_model.predict(input_features)
    for i, prediction in enumerate(predictions):
        st.write(f'The predicted class for sample {i + 1} is: {prediction}')