updated

app.py

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+from tensorflow.keras.models import load_model
+
+saved_model = load_model('churn_model2.h5')
+
+def churn_prediction(CreditScore,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Location):
+    Geography_France,Geography_Germany,Geography_Spain=0,0,0
+    df = pd.DataFrame.from_dict({
+        "Credit score":[CreditScore],
+        "Is female?":[1 if Gender=='Female' else 0],
+        "Age":[Age],
+        'Tenure':[Tenure],
+        'Balance':[Balance],
+        'Number of products':[NumOfProducts],
+        'Has credit card?': [1 if HasCrCard=='Yes' else 0],
+        'Is a active member?':[1 if IsActiveMember=='Yes' else 0],
+        'Estimated Salary': [EstimatedSalary],
+        'Geography_France': [1 if Location=='France' else 0],
+        'Geography_Germany':[1 if Location=='Germany' else 0],
+        'Geography_Spain':[1 if Location=='Spain' else 0]
+        
+    })
+    cols_to_scale = ["Credit score",'Age','Tenure','Balance','Number of products','Estimated Salary']
+    df[cols_to_scale] = scaler.transform(df[cols_to_scale])
+    pred=saved_model.predict(df)
+    pred = pred[0][0]
+    churn_prob=str(round(pred,2))
+    non_churn_prob = str(round(1-pred,2))
+    percent_likelihood = int(pred*100) 
+    return {"probability customer will exit (%)":churn_prob , "probability customer will stay (%)": non_churn_prob}
+
+
+import gradio as gr
+
+iface = gr.Interface(fn=churn_prediction,
+                    inputs=['number',gr.inputs.Radio(['Female','Male']),'number','number','number','number',gr.inputs.Radio(['Yes','No']),gr.inputs.Radio(['Yes','No']),'number',gr.inputs.Radio(['France','Germany','Spain'])],
+                    outputs=['label'])
+
+iface.launch()

churn_model2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c002ce5378d37e065dc66704b68b083aa314ad4ff32e8d824179d4e6ad34ded0
+size 34600

requirements.txt

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+tensorflow