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app.py

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+
+import pandas as pd
+import gradio as gr
+import joblib as jp
+
+
+
+
+model=jp.load(r'C:\Users\ahmed\Desktop\Data Analysis Project Saudia Arabia\1th Project1\model.pkl')
+std = jp.load(r"C:\Users\ahmed\Desktop\Data Analysis Project Saudia Arabia\1th Project1\std_col.pkl")
+le = jp.load(r"C:\Users\ahmed\Desktop\Data Analysis Project Saudia Arabia\1th Project1\le_col.pkl")
+
+le_col=['gender','education','region','purchase_frequency','product_category']
+std_col=['age','income','purchase_amount']
+
+def Customer_Predction_Model(ag,gender,income,edu,reg,loy,pr,pu,prod,prom,sts):
+    try:
+        input_data=pd.DataFrame({   
+        'age':[ag],
+        'gender':[gender],
+        'income':[income],
+        'education':[edu],
+        'region':[reg],
+        'loyalty_status':[loy],
+        'purchase_frequency':[pr],
+        'purchase_amount':[pu],
+        'product_category':[prod],
+        'promotion_usage':[prom],
+        'satisfaction_score':[sts]
+        })
+        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=model.predict(input_data)
+        if prediction[0]==0:
+            return 'No'
+        else:
+            return 'Yes'
+            
+    except Exception as e:
+        return str(e)
+gr.Interface(
+    fn=Customer_Predction_Model,
+    inputs=[
+    gr.Number(label='age'),
+    gr.Dropdown(['Male','Female'],label='gender'),
+    gr.Number(label='income'),
+    gr.Dropdown(['College','Bachelor','HighSchool','Masters'],label='education'),
+    gr.Dropdown(['East','West','South','North'],label='region'),
+    gr.Number(label='loyalty_status'),
+    gr.Dropdown(['rare','occasional','frequent'],label='purchase_frequency'),
+    gr.Number(label='purchase_amount'),
+    gr.Dropdown(['Electronics','Clothing','Books','Food','Health','Home','Beauty'],label='product_category'),
+    gr.Number(label='promotion_usage'),
+    gr.Number(label='satisfaction_score')
+    
+],
+    outputs=gr.Textbox(label='Prediction'),
+    title='Customer Purchase Anticipation Prediction Model'
+
+).launch()

le_col.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:269984723101905983d864c4488497b844208316dd7e62680bb275a94e757af1
+size 1874

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:197258fe7506245944b9f20c04accb33dc6d55d01fe8d0f3bd71945927847378
+size 23745

requirements.txt

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+gradio
+pandas
+joblib
+scikit-learn

std_col.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de9c21ea3f4bcf71652cb38958321e388525fcc4534e08b43fa3e12259eb3509
+size 1007