Upload Lab07.py

Lab07.py

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+import joblib
+import pandas as pd
+import streamlit as st
+
+# โหลดโมเดลและข้อมูลที่จำเป็น
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
+
+def main():
+    st.title("Customer Segmentation Prediction")
+
+    # สร้างฟอร์มสำหรับป้อนข้อมูล
+    with st.form("questionnaire"):
+        Gender = st.selectbox("Gender", unique_values["Gender"])
+        Ever_Married = st.selectbox("Ever Married", unique_values["Ever_Married"])
+        Age = st.slider("Age", min_value=18, max_value=89)
+        Graduated = st.selectbox("Graduated", unique_values["Graduated"])
+        Profession = st.selectbox("Profession", unique_values["Profession"])
+        Work_Experience = st.slider("Work Experience", min_value=0, max_value=14)
+        Spending_Score = st.selectbox("Spending Score", unique_values["Spending_Score"])
+        Family_Size = st.slider("Family Size", min_value=1, max_value=9)
+        Var_1 = st.selectbox("Var_1", unique_values["Var_1"])
+        ID = st.slider("ID", min_value=458982, max_value=467974)
+
+        # สร้างปุ่มสำหรับการทำนาย
+        clicked = st.form_submit_button("Predict Segmentation")
+        if clicked:
+            # ใช้โมเดลทำนาย Segmentation จากข้อมูลที่ป้อน
+            result = model.predict(pd.DataFrame({"Gender": [Gender],
+                                                 "Ever_Married": [Ever_Married],
+                                                 "Age": [Age],
+                                                 "ID": [ID],
+                                                 "Graduated": [Graduated],
+                                                 "Profession": [Pros[Profession]],
+                                                 "Work_Experience": [Work_Experience],
+                                                 "Spending_Score": [Spending_Score],
+                                                 "Family_Size": [Family_Size],
+                                                 "Var_1": [Var_1]
+                                                 }))
+            # แปลงผลลัพธ์ให้เป็นข้อความ
+            if result[0] == 0:
+                result = "A"
+            elif result[0] == 1:
+                result = "B"
+            elif result[0] == 2:
+                result = "C"
+            else:
+                result = "D"
+            # แสดงผลลัพธ์
+            st.success('Predicted Segmentation: {}'.format(result))
+
+if __name__ == '__main__':
+    main()