Upload app.py

app.py

@@ -1,65 +1,53 @@
 import joblib
 import pandas as pd
-import streamlit as st 
-
-Pros = {'Engineer' : 1, 
-              'Healthcare' : 2, 
-              'Executive' : 3, 
-              'Doctor' : 4, 
-              'Artist' : 5,
-              'Lawyer' : 6, 
-              'Entertainment' : 7, 
-              'Homemaker' : 8, 
-              'Marketing' : 9
-              }
+import streamlit as st
 
+# โหลดโมเดลและข้อมูลที่จำเป็น
 model = joblib.load('model.joblib')
 unique_values = joblib.load('unique_values.joblib')
 
-unique_Gender =  unique_values["Gender"]
-unique_Ever_Married =  unique_values["Ever_Married"]
-unique_Graduated	 =  unique_values["Graduated"]
-unique_Spending_Score	 =  unique_values["Spending_Score"]
-unique_Var_1 = unique_values["Var_1"]
-unique_Profession = unique_values["profession"]
-
 def main():
-    st.title("Customer segmentation")
+    st.title("Customer Segmentation Prediction")
 
-    with st.form("questionaire"):
-        Gender = st.selectbox("Gender", unique_Gender)
-        Ever_Married = st.selectbox("Married", unique_Ever_Married)
+    # 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_Graduated)
-        Profession = st.selectbox("Profession", unique_Profession)
-        Work_Experience = st.slider("Work_Experience", min_value=0, max_value=14)
-        Spending_Score = st.selectbox("Spending_Score", unique_Spending_Score)
-        Family_Size = st.slider("Family_Size", min_value=1, max_value=9)
-        Var_1 = st.selectbox("Var_1", unique_Var_1)
+        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:
-            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],
-                                               }))
-            if result[0] == "A":
+            # 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] == "B":
+            elif result[0] == 1:
                 result = "B"
-            elif result[0] == "C":
+            elif result[0] == 2:
                 result = "C"
             else:
                 result = "D"
-            st.success('The predicted Segmentation is {}'.format(result))
+            # แสดงผลลัพธ์
+            st.success('Predicted Segmentation: {}'.format(result))
 
-if __name__=='__main__':
+if __name__ == '__main__':
     main()