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app.py

@@ -37,14 +37,20 @@ def predict_churn():
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([requestData])
 
+    # create encoder with OneHotEncoder for encoding the selected values to match the training data
+    encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False) # Important for handling unseen categories
+
+    # You MUST use the *trained* encoder to transform the new data
+    encoded_new_data = encoder.transform(input_data[['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type']])
+
     # Make a churn prediction using the trained model
-    prediction = model.predict(input_data).tolist()[0]
+    prediction = model.predict(encoded_new_data).tolist()[0]
 
     #Calculate the actual price
     predicted_sales = np.exp(prediction)
 
     # Convert predicted_price to Python float
     predicted_sales = round(float(predicted_sales), 2)
-  
+
     # Return the prediction as a JSON response
     return jsonify({'Predicted_Sale': predicted_sales})