Upload app.py

app.py

@@ -0,0 +1,71 @@
+import pandas as pd
+import gradio as gr
+import joblib
+
+le=joblib.load('le_col.pkl')
+min_max=joblib.load('MinMax.pkl')
+lr=joblib.load('model.pkl')
+
+
+le_col=['gender','Partner','Dependents','PhoneService','PaperlessBilling','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies','Contract','PaymentMethod']
+MinMax=['TotalCharges','MonthlyCharges','tenure']
+
+def prediction_churn_model(gr,sc,pr,dp,t,ps,mp,Is,os,ob,dd,ts,st,sm,cr,pb,pm,mc,tc):
+    try:
+        input_data=pd.DataFrame({
+            'gender':[gr],
+            'SeniorCitizen':[sc],
+            'Partner':[pr],
+            'Dependents':[dp],
+            'tenure':[t],
+            'PhoneService':[ps],
+            'MultipleLines':[mp],
+            'InternetService':[Is],
+            'OnlineSecurity':[os],
+            'OnlineBackup':[ob],
+            'DeviceProtection':[dd],
+            'TechSupport':[ts],
+            'StreamingTV':[st],
+            'StreamingMovies':[sm],
+            'Contract':[cr],
+            'PaperlessBilling':[pb],
+            'PaymentMethod':[pm],
+            'MonthlyCharges':[mc],
+            'TotalCharges':[tc]
+        })
+        for col in le_col:
+            input_data[col]=le[col].transform(input_data[col])
+        input_data[MinMax]=min_max.transform(input_data[MinMax])
+        prediction=lr.predict(input_data)
+        if prediction[0]==0:
+            return 'No'
+        else:
+            return 'Yes'
+    except Exception as e:
+        return str(e)
+gr.Interface(
+    inputs=[
+        gr.Dropdown(['Female', 'Male'],label='gender'),
+        gr.Number(label='SeniorCitizen'),
+        gr.Dropdown(['No','Yes'],label='Partner'),
+        gr.Dropdown(['No','Yes'],label='Dependents'),
+        gr.Number(label='tenure'),
+        gr.Dropdown(['Yes','No'],label='PhoneService'),
+        gr.Dropdown(['No','Yes'],label='MultipleLines'),
+        gr.Dropdown(['Fiber optic','DSL','No'],label='InternetService'),
+        gr.Dropdown(['No','Yes','No internet service'],label='OnlineSecurity'),
+        gr.Dropdown(['No','Yes','No internet service'],label='OnlineBackup'),
+        gr.Dropdown(['No','Yes','No internet service'],label='DeviceProtection'),
+        gr.Dropdown(['No','Yes','No internet service'],label='TechSupport'),
+        gr.Dropdown(['No','Yes','No internet service'],label='StreamingTV'),
+        gr.Dropdown(['No','Yes','No internet service'],label='StreamingMovies'),
+        gr.Dropdown(['Month-to-month','Two year','One year'],label='Contract'),
+        gr.Dropdown(['Yes','No'],label='PaperlessBilling'),
+        gr.Dropdown(['Electroniccheck', 'Mailedcheck', 'Banktransfer', 'Creditcard'],label='PaymentMethod'),
+        gr.Number(label='MonthlyCharges'),
+        gr.Number(label='TotalCharges')
+    ],
+    fn=prediction_churn_model,
+    outputs=gr.Textbox(label='Prediction'),
+    title='Prediction Telecoum Churn Model'
+).launch()