Update Home.py

Home.py

@@ -25,7 +25,7 @@ def convert_prediction(prediction):
     else:
         return 'L'
 
-st.title('Demo System for Vietnamese Woman Bra Size Classifier')
+st.markdown("<h1 style='text-align: center;'>Demo System for Vietnamese Woman Bra Size Classifier</h1>", unsafe_allow_html=True)
 
 scaler_6, scaler_8, scaler_21, clf_6, clf_8, clf_21 = load_essential_models(scaler_6_path='./Models/6/scaler.pkl', scaler_8_path='./Models/8/scaler.pkl', scaler_full_path='./Models/21/scaler.pkl', clf_6_path='./Models/6/svc_fs_tune.pkl', clf_8_path='./Models/8/ANN_8.h5', clf_full_path='./Models/21/ANNs_full.h5')
 
@@ -35,7 +35,7 @@ num_of_features = header_col_1.selectbox(label='Please select the number of meas
 # h, w, bmi, vtn, vn, vcn, cn, cnnp, cnnt, cntp, cntt, ccnp, ccnt, snt, sndp, sndt, xup, xut, cl, ttp, ttt = [0.0]*21
 
 if num_of_features == '6 measurements':
-    sample_options_6 = header_col_2.selectbox(label='Sample options:', options=['Sample 1 (Small)', 'Sample 2 (Medium)', 'Sample 3 (Large)'])
+    sample_options_6 = header_col_2.selectbox(label='Measurement sample options:', options=['Sample 1 (Small)', 'Sample 2 (Medium)', 'Sample 3 (Large)'])
     container_col_1, container_col_2, container_col_3 = st.columns([1, 1, 1])
     if sample_options_6 == 'Sample 1 (Small)':
         vtn = container_col_1.number_input(label='Upper bust circumference (cm):', value=82.00, min_value=0.00, step=0.01)
@@ -60,7 +60,7 @@ if num_of_features == '6 measurements':
         ttt = container_col_3.number_input(label='Volume of left breast (cm3):', value=471.60, min_value=0.00, step=0.01)
 
 elif num_of_features == '8 measurements':
-    sample_options_8 = header_col_2.selectbox(label='Sample options:', options=['Sample 1 (Small)', 'Sample 2 (Medium)', 'Sample 3 (Large)'])
+    sample_options_8 = header_col_2.selectbox(label='Measurement sample options:', options=['Sample 1 (Small)', 'Sample 2 (Medium)', 'Sample 3 (Large)'])
     container_col_1, container_col_2, container_col_3, container_col_4 = st.columns([1, 1, 1, 1])
     if sample_options_8 == 'Sample 1 (Small)':
         ttp = container_col_1.number_input(label='Volume of right breast (cm3):', value=287.50, min_value=0.00, step=0.01)
@@ -90,7 +90,7 @@ elif num_of_features == '8 measurements':
         cntp = container_col_3.number_input(label='Inner right breast curve (cm):', value=10.7, min_value=0.00, step=0.01)
         vcn = container_col_4.number_input(label='Chest circumference (cm):', value=75.0, min_value=0.00, step=0.01)
 else:
-    sample_options_21 = header_col_2.selectbox(label='Sample options:', options=['Sample 1 (Small)', 'Sample 2 (Medium)', 'Sample 3 (Large)'])
+    sample_options_21 = header_col_2.selectbox(label='Measurement sample options:', options=['Sample 1 (Small)', 'Sample 2 (Medium)', 'Sample 3 (Large)'])
     container_col_1, container_col_2, container_col_3, container_col_4, container_col_5 = st.columns([1, 1, 1, 1, 1])
     if sample_options_21 == 'Sample 1 (Small)':
         h = container_col_1.number_input(label='Height (cm):', value=158.50, min_value=0.00, step=0.01)
@@ -169,14 +169,14 @@ if predict:
         X_6 = np.array([[vtn, vn, vcn, cl, ttp, ttt]])
         X_6 = scaler_6.transform(X_6)
         y_6 = clf_6.predict(X_6)
-        st.success(f'We recommend you choosing {convert_prediction(y_6)} size!')
+        st.success(f'We recommend you choosing **{convert_prediction(y_6)}** size!')
     elif num_of_features == '8 measurements':
         X_8 = np.array([[ttp, cl, cnnp, vn, vtn, cnnt, cntp, vcn]])
         X_8 = scaler_8.transform(X_8)
         y_8 = clf_8.predict(X_8)
-        st.success(f'We recommend you choosing {convert_prediction(np.argmax(y_8, axis=1))} size!')
+        st.success(f'We recommend you choosing **{convert_prediction(np.argmax(y_8, axis=1))}** size!')
     else:
         X_21 = np.array([[h, w, bmi, vtn, vn, vcn, cn, cnnp, cnnt, cntp, cntt, ccnp, ccnt, snt, sndp, sndt, xup, xut, cl, ttp, ttt]])
         X_21 = scaler_21.transform(X_21)
         y_21 = clf_21.predict(X_21)
-        st.success(f'We recommend you choosing {convert_prediction(np.argmax(y_21, axis=1))} size!')
+        st.success(f'We recommend you choosing **{convert_prediction(np.argmax(y_21, axis=1))}** size!')