Update app.py

app.py

@@ -1,57 +1,64 @@
-import pandas as pd
-import gradio as gr
-import joblib
-
-
-le=joblib.load('le_col.pkl')
-std=joblib.load('std_col.pkl')
-lg=joblib.load('lg.pkl')
-
-le_col=['gender','education','region','loyalty_status','purchase_frequency','product_category']
-std_col=['age','income','purchase_amount','promotion_usage','satisfaction_score']
-
-
-def Prediction_will_purchase_again_Model(a,g,i,e,r,l,p,pp,c,u,s):
-    try:
-        input_data=pd.DataFrame({
-            'age':[a],
-            'gender':[g],
-            'income':[i],
-            'education':[e],
-            'region':[r],
-            'loyalty_status':[l],
-            'purchase_frequency':[p],
-            'purchase_amount':[pp],
-            'product_category':[c],
-            'promotion_usage':[u],
-            'satisfaction_score':[s]
-    
-        })
-        for col in le_col:
-            input_data[col]=le[col].transform(input_data[col])
-        input_data[std_col]=std.transform(input_data[std_col])
-        prediction=lg.predict(input_data)
-        if prediction==1:
-            return 'Yes'
-        else:
-            return 'No'
-    except Exception as e:
-        return str(e)
-gr.Interface(
-    fn=Prediction_will_purchase_again_Model,
-    inputs=[
-        gr.Number(label='age'),
-        gr.Dropdown(['Male','Female'],label='gender'),
-        gr.Number(label='income'),
-        gr.Dropdown(['Bachelor', 'Masters', 'HighSchool', 'College'],label='education'),
-        gr.Dropdown(['East', 'West', 'South', 'North'],label='region'),
-        gr.Dropdown(['Gold', 'Regular', 'Silver'],label='loyalty_status'),
-        gr.Dropdown(['frequent', 'rare', 'occasional'],label='purchase_frequency'),
-        gr.Number(label='purchase_amount'),
-        gr.Dropdown(['Books', 'Clothing', 'Food', 'Electronics', 'Home', 'Beauty','Health'],label='product_category'),
-        gr.Number(label='promotion_usage'),
-        gr.Number(label='satisfaction_score')
-    ],
-    title='Prediction_will_purchase_again_Model',
-    outputs=gr.Textbox(label='Prediction')
-).launch()
+import pandas as pd
+import gradio as gr
+import joblib
+
+# تحميل الأكواد المحفوظة
+le = joblib.load('le_col.pkl')
+std = joblib.load('std_col.pkl')
+lg = joblib.load('lg.pkl')
+
+le_col = ['gender','education','region','loyalty_status','purchase_frequency','product_category']
+std_col = ['age','income','purchase_amount','promotion_usage','satisfaction_score']
+
+def Prediction_will_purchase_again_Model(a,g,i,e,r,l,p,pp,c,u,s):
+    try:
+        input_data = pd.DataFrame({
+            'age':[a],
+            'gender':[g],
+            'income':[i],
+            'education':[e],
+            'region':[r],
+            'loyalty_status':[l],
+            'purchase_frequency':[p],
+            'purchase_amount':[pp],
+            'product_category':[c],
+            'promotion_usage':[u],
+            'satisfaction_score':[s]
+        })
+
+        # Label Encoding
+        for col in le_col:
+            input_data[col] = le[col].transform(input_data[col])
+        
+        # Standardization
+        input_data[std_col] = std.transform(input_data[std_col])
+
+        # Predict using probability + threshold
+        prob = lg.predict_proba(input_data)[:,1][0]
+        threshold = 0.4  # غيره حسب النتيجة اللي عايزها
+        if prob >= threshold:
+            return f'Yes (Prob={prob:.2f})'
+        else:
+            return f'No (Prob={prob:.2f})'
+    except Exception as e:
+        return str(e)
+
+# Gradio Interface
+gr.Interface(
+    fn=Prediction_will_purchase_again_Model,
+    inputs=[
+        gr.Number(label='age'),
+        gr.Dropdown(['Male','Female'],label='gender'),
+        gr.Number(label='income'),
+        gr.Dropdown(['Bachelor', 'Masters', 'HighSchool', 'College'],label='education'),
+        gr.Dropdown(['East', 'West', 'South', 'North'],label='region'),
+        gr.Dropdown(['Gold', 'Regular', 'Silver'],label='loyalty_status'),
+        gr.Dropdown(['frequent', 'rare', 'occasional'],label='purchase_frequency'),
+        gr.Number(label='purchase_amount'),
+        gr.Dropdown(['Books', 'Clothing', 'Food', 'Electronics', 'Home', 'Beauty','Health'],label='product_category'),
+        gr.Number(label='promotion_usage'),
+        gr.Number(label='satisfaction_score')
+    ],
+    title='Prediction_will_purchase_again_Model',
+    outputs=gr.Textbox(label='Prediction')
+).launch()