Update app.py

app.py

@@ -30,14 +30,46 @@ convex_area = st.number_input('Convex hull (convex area)', min_value=18068.0, ma
 #     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'):
-    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}')
+    predictions = model.predict(input_features)
+    for i, prediction in enumerate(predictions):
+        st.write(f'The predicted class for sample {i + 1} is: {prediction}')
+