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4f4843d c985c54 f115ea3 4f4843d 0dc7afd 4f4843d de1940d ce1ba8d 4f4843d 458e89e ce1ba8d de1940d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | 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}')
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