Update app.py

app.py

@@ -3,73 +3,41 @@ import pandas as pd
 import pickle
 
 # Load the fitted model
-best_model = pickle.load(open('model (10).pkl', 'rb'))
+model = pickle.load(open('model (10).pkl', 'rb'))
+
+# Mapping antara kelas dan nama tipe almond
+class_mapping = {
+    0: 'Sweet Almond',
+    1: 'Bitter Almond',
+    2: 'California Almond',
+    # 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'):
-#     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)
+# 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'):
-    predictions = best_model.predict(input_features)
-    for i, prediction in enumerate(predictions):
-        st.write(f'The predicted class for sample {i + 1} is: {prediction}')
-
-
+    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)
+    
+    # Menggunakan mapping untuk mendapatkan nama tipe almond
+    predicted_class_name = class_mapping[prediction[0]]
+    
+    st.write(f'The predicted class is: {predicted_class_name}')