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Vietnamese Woman Bra Size Classifier by n.t.phuc149
Version: v1.0

.streamlit/config.toml

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+[theme]

Models/21/ANNs_full.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2171b1124533345a97d16e6a4c7175d14f4a30c68e4f7d2d88e0ee7bebe99d1
+size 2151608

Models/21/scaler.pkl

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+oid sha256:1bba338e5a9e948bcb24eeae5564b38b3f357ddf003b319120b4d9e805049247
+size 1167

Models/6/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:112f1f7423b966cb0262c61e3cfe06bb587fbc8a4573f120e025137d2fa219cf
+size 842

Models/6/svc_fs_tune.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dbcd2543e3b001c0463ae9287023bd4ddfb7fe4309359aec1fa458040b2f32c8
+size 25810

Models/8/ANNs_8base.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec6d4f39c2b589d0d9b7d9f9a7c4d20f257d5fbb298d2257f720c63d073728d0
+size 2075968

Models/8/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72a59613ba32e5f133a958bd1bd30770b992ad45f9e48ebee0c37a90997fd2e7
+size 777

app.py

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+import pickle
+import numpy as np
+import streamlit as st
+from tensorflow.keras.models import load_model
+
+def load_essential_models(scaler_6_path, scaler_8_path, scaler_full_path, clf_6_path, clf_8_path, clf_full_path):
+    scaler_6 = pickle.load(open(file=scaler_6_path, mode='rb'))
+    scaler_8 = pickle.load(open(file=scaler_8_path, mode='rb'))
+    scaler_21 = pickle.load(open(file=scaler_full_path, mode='rb'))
+    clf_6 = pickle.load(open(file=clf_6_path, mode='rb'))
+    clf_8 = load_model(clf_8_path)
+    clf_21 = load_model(clf_full_path)
+    return scaler_6, scaler_8, scaler_21, clf_6, clf_8, clf_21
+
+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/ANNs_8base.h5', clf_full_path='./Models/21/ANNs_full.h5')
+
+st.title('Demo System for Vietnamese Woman Bra Size Classifier')
+
+header_col_1, header_col_2 = st.columns([3, 2])
+num_of_features = header_col_1.selectbox(label='Please select the number of measurements you have:', options=['6 measurements', '8 measurements', '21 measurements'])
+
+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)'])
+    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='Your vtn: ', value=82.00, min_value=0.00, step=0.01)
+        vn = container_col_2.number_input(label='Your vn:', value=82.40, min_value=0.00, step=0.01)
+        vcn = container_col_3.number_input(label='Your vcn:', value=73.10, min_value=0.00, step=0.01)
+        cl = container_col_1.number_input(label='Your cl:', value=9.30, min_value=0.00, step=0.01)
+        ttp = container_col_2.number_input(label='Your ttp:', value=325.6, min_value=0.00, step=0.01)
+        ttt = container_col_3.number_input(label='Your ttt:', value=325.6, min_value=0.00, step=0.01)
+    elif sample_options_6 == 'Sample 2 (Medium)':
+        vtn = container_col_1.number_input(label='Your vtn: ', value=77.20, min_value=0.00, step=0.01)
+        vn = container_col_2.number_input(label='Your vn:', value=78.60, min_value=0.00, step=0.01)
+        vcn = container_col_3.number_input(label='Your vcn:', value=66.50, min_value=0.00, step=0.01)
+        cl = container_col_1.number_input(label='Your cl:', value=12.10, min_value=0.00, step=0.01)
+        ttp = container_col_2.number_input(label='Your ttp:', value=388.80, min_value=0.00, step=0.01)
+        ttt = container_col_3.number_input(label='Your ttt:', value=324.60, min_value=0.00, step=0.01)
+    else:
+        vtn = container_col_1.number_input(label='Your vtn: ', value=86.50, min_value=0.00, step=0.01)
+        vn = container_col_2.number_input(label='Your vn:', value=88.00, min_value=0.00, step=0.01)
+        vcn = container_col_3.number_input(label='Your vcn:', value=74.00, min_value=0.00, step=0.01)
+        cl = container_col_1.number_input(label='Your cl:', value=14.00, min_value=0.00, step=0.01)
+        ttp = container_col_2.number_input(label='Your ttp:', value=451.30, min_value=0.00, step=0.01)
+        ttt = container_col_3.number_input(label='Your ttt:', 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)'])
+    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='Your ttp:', value=325.10, min_value=0.00, step=0.01)
+        cl = container_col_2.number_input(label='Your cl:', value=8.90, min_value=0.00, step=0.01)
+        cnnp = container_col_3.number_input(label='Your cnnp:', value=12.30, min_value=0.00, step=0.01)
+        vn = container_col_4.number_input(label='Your vn:', value=83.20, min_value=0.00, step=0.01)
+        vtn = container_col_1.number_input(label='Your vtn: ', value=80.10, min_value=0.00, step=0.01)
+        ccnt = container_col_2.number_input(label='Your ccnt:', value=11.10, min_value=0.00, step=0.01)
+        cntp = container_col_3.number_input(label='Your cntp:', value=8.70, min_value=0.00, step=0.01)
+        vcn = container_col_4.number_input(label='Your vcn:', value=74.30, min_value=0.00, step=0.01)
+    elif sample_options_8 == 'Sample 2 (Medium)':
+        ttp = container_col_1.number_input(label='Your ttp:', value=385.80, min_value=0.00, step=0.01)
+        cl = container_col_2.number_input(label='Your cl:', value=13.60, min_value=0.00, step=0.01)
+        cnnp = container_col_3.number_input(label='Your cnnp:', value=8.80, min_value=0.00, step=0.01)
+        vn = container_col_4.number_input(label='Your vn:', value=83.20, min_value=0.00, step=0.01)
+        vtn = container_col_1.number_input(label='Your vtn: ', value=82.50, min_value=0.00, step=0.01)
+        ccnt = container_col_2.number_input(label='Your ccnt:', value=9.30, min_value=0.00, step=0.01)
+        cntp = container_col_3.number_input(label='Your cntp:', value=9.10, min_value=0.00, step=0.01)
+        vcn = container_col_4.number_input(label='Your vcn:', value=69.60, min_value=0.00, step=0.01)
+    else:
+        ttp = container_col_1.number_input(label='Your ttp:', value=453.30, min_value=0.00, step=0.01)
+        cl = container_col_2.number_input(label='Your cl:', value=13.00, min_value=0.00, step=0.01)
+        cnnp = container_col_3.number_input(label='Your cnnp:', value=12.50, min_value=0.00, step=0.01)
+        vn = container_col_4.number_input(label='Your vn:', value=85.30, min_value=0.00, step=0.01)
+        vtn = container_col_1.number_input(label='Your vtn: ', value=78.50, min_value=0.00, step=0.01)
+        ccnt = container_col_2.number_input(label='Your ccnt:', value=12.40, min_value=0.00, step=0.01)
+        cntp = container_col_3.number_input(label='Your cntp:', value=10.00, min_value=0.00, step=0.01)
+        vcn = container_col_4.number_input(label='Your vcn:', value=72.30, 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)'])
+    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='Your h:', value=158.50, min_value=0.00, step=0.01)
+        w = container_col_2.number_input(label='Your w:', value=44.00, min_value=0.00, step=0.01)
+        bmi = container_col_3.number_input(label='Your bmi:', value=17.50, min_value=0.00, step=0.01)
+        vtn = container_col_4.number_input(label='Your vtn: ', value=75.40, min_value=0.00, step=0.01)
+        vn = container_col_5.number_input(label='Your vn:', value=81.10, min_value=0.00, step=0.01)
+        vcn = container_col_1.number_input(label='Your vcn:', value=74.80, min_value=0.00, step=0.01)
+        cn = container_col_2.number_input(label='Your cn:', value=14.50, min_value=0.00, step=0.01)
+        cnnp = container_col_3.number_input(label='Your cnnp:', value=13.80, min_value=0.00, step=0.01)
+        cnnt = container_col_4.number_input(label='Your cnnt:', value=14.40, min_value=0.00, step=0.01)
+        cntp = container_col_5.number_input(label='Your cntp:', value=8.60, min_value=0.00, step=0.01)
+        cntt = container_col_1.number_input(label='Your cntt:', value=8.40, min_value=0.00, step=0.01)
+        ccnp = container_col_2.number_input(label='Your ccnp:', value=21.50, min_value=0.00, step=0.01)
+        ccnt = container_col_3.number_input(label='Your ccnt:', value=21.20, min_value=0.00, step=0.01)
+        snt = container_col_4.number_input(label='Your snt:', value=8.90, min_value=0.00, step=0.01)
+        sndp = container_col_5.number_input(label='Your sndp:', value=6.50, min_value=0.00, step=0.01)
+        sndt = container_col_1.number_input(label='Your sndt:', value=6.50, min_value=0.00, step=0.01)
+        xup = container_col_2.number_input(label='Your xup:', value=21.80, min_value=0.00, step=0.01)
+        xut = container_col_3.number_input(label='Your xut:', value=21.10, min_value=0.00, step=0.01)
+        cl = container_col_4.number_input(label='Your cl:', value=6.30, min_value=0.00, step=0.01)
+        ttp = container_col_5.number_input(label='Your ttp:', value=325.10, min_value=0.00, step=0.01)
+        ttt = container_col_3.number_input(label='Your ttt:', value=335.70, min_value=0.00, step=0.01)
+    elif sample_options_21 == 'Sample 2 (Medium)':
+        h = container_col_1.number_input(label='Your h:', value=163.00, min_value=0.00, step=0.01)
+        w = container_col_2.number_input(label='Your w:', value=43.00, min_value=0.00, step=0.01)
+        bmi = container_col_3.number_input(label='Your bmi:', value=16.20, min_value=0.00, step=0.01)
+        vtn = container_col_4.number_input(label='Your vtn: ', value=76.00, min_value=0.00, step=0.01)
+        vn = container_col_5.number_input(label='Your vn:', value=79.00, min_value=0.00, step=0.01)
+        vcn = container_col_1.number_input(label='Your vcn:', value=64.00, min_value=0.00, step=0.01)
+        cn = container_col_2.number_input(label='Your cn:', value=16.50, min_value=0.00, step=0.01)
+        cnnp = container_col_3.number_input(label='Your cnnp:', value=13.10, min_value=0.00, step=0.01)
+        cnnt = container_col_4.number_input(label='Your cnnt:', value=12.60, min_value=0.00, step=0.01)
+        cntp = container_col_5.number_input(label='Your cntp:', value=9.20, min_value=0.00, step=0.01)
+        cntt = container_col_1.number_input(label='Your cntt:', value=9.10, min_value=0.00, step=0.01)
+        ccnp = container_col_2.number_input(label='Your ccnp:', value=19.80, min_value=0.00, step=0.01)
+        ccnt = container_col_3.number_input(label='Your ccnt:', value=18.20, min_value=0.00, step=0.01)
+        snt = container_col_4.number_input(label='Your snt:', value=8.40, min_value=0.00, step=0.01)
+        sndp = container_col_5.number_input(label='Your sndp:', value=3.50, min_value=0.00, step=0.01)
+        sndt = container_col_1.number_input(label='Your sndt:', value=3.70, min_value=0.00, step=0.01)
+        xup = container_col_2.number_input(label='Your xup:', value=21.00, min_value=0.00, step=0.01)
+        xut = container_col_3.number_input(label='Your xut:', value=20.50, min_value=0.00, step=0.01)
+        cl = container_col_4.number_input(label='Your cl:', value=15.00, min_value=0.00, step=0.01)
+        ttp = container_col_5.number_input(label='Your ttp:', value=521.60, min_value=0.00, step=0.01)
+        ttt = container_col_3.number_input(label='Your ttt:', value=513.50, min_value=0.00, step=0.01)
+    else:
+        h = container_col_1.number_input(label='Your h:', value=152.00, min_value=0.00, step=0.01)
+        w = container_col_2.number_input(label='Your w:', value=46.00, min_value=0.00, step=0.01)
+        bmi = container_col_3.number_input(label='Your bmi:', value=19.90, min_value=0.00, step=0.01)
+        vtn = container_col_4.number_input(label='Your vtn: ', value=77.50, min_value=0.00, step=0.01)
+        vn = container_col_5.number_input(label='Your vn:', value=85.50, min_value=0.00, step=0.01)
+        vcn = container_col_1.number_input(label='Your vcn:', value=70.40, min_value=0.00, step=0.01)
+        cn = container_col_2.number_input(label='Your cn:', value=18.90, min_value=0.00, step=0.01)
+        cnnp = container_col_3.number_input(label='Your cnnp:', value=13.50, min_value=0.00, step=0.01)
+        cnnt = container_col_4.number_input(label='Your cnnt:', value=12.50, min_value=0.00, step=0.01)
+        cntp = container_col_5.number_input(label='Your cntp:', value=10.30, min_value=0.00, step=0.01)
+        cntt = container_col_1.number_input(label='Your cntt:', value=10.50, min_value=0.00, step=0.01)
+        ccnp = container_col_2.number_input(label='Your ccnp:', value=20.40, min_value=0.00, step=0.01)
+        ccnt = container_col_3.number_input(label='Your ccnt:', value=20.10, min_value=0.00, step=0.01)
+        snt = container_col_4.number_input(label='Your snt:', value=8.50, min_value=0.00, step=0.01)
+        sndp = container_col_5.number_input(label='Your sndp:', value=5.50, min_value=0.00, step=0.01)
+        sndt = container_col_1.number_input(label='Your sndt:', value=4.20, min_value=0.00, step=0.01)
+        xup = container_col_2.number_input(label='Your xup:', value=19.50, min_value=0.00, step=0.01)
+        xut = container_col_3.number_input(label='Your xut:', value=20.50, min_value=0.00, step=0.01)
+        cl = container_col_4.number_input(label='Your cl:', value=15.10, min_value=0.00, step=0.01)
+        ttp = container_col_5.number_input(label='Your ttp:', value=625.80, min_value=0.00, step=0.01)
+        ttt = container_col_3.number_input(label='Your ttt:', value=585.40, min_value=0.00, step=0.01)
+
+col_1, col_2, col_3, col_4, col_5 = st.columns([1, 1, 1, 1, 1])
+
+with col_3:
+    predict = st.button(label='Predict', use_container_width=True)
+
+if predict:
+    if num_of_features == '6 measurements':
+        X_6 = [[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 {y_6} size!')
+    elif num_of_features == '8 measurements':
+        X_8 = [[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 {np.argmax(y_8, axis=1)} size!')
+    else:
+        X_21 = [[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 {np.argmax(y_21, axis=1)} size!')

requirements.txt

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+numpy==1.21.6
+streamlit==1.20.0
+scikit-learn==1.0.2
+tensorflow-gpu==2.10.0
+protobuf==3.19.6