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@@ -13,4 +13,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # - `-w 4`: Uses 4 worker processes for handling requests
 # - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
 # - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:revenue_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:backend_predictor_api"]

app.py

@@ -40,13 +40,13 @@ def predict_dijak_backend():
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([sample])
 
-    # Make a revenue prediction using the trained model
+    # Make a prediction using the trained model
     prediction = model.predict(input_data).tolist()[0]
 
     # Return the prediction as a JSON response
     return jsonify({'Prediction': prediction})
 
-# Define an endpoint to predict revenue for a batch of revenue
+# Define an endpoint to predict for a batch of input
 @backend_predictor_api.post('/v1/dijakbnbatch')
 def predict_dijak_backend_batch():
     # Get the uploaded CSV file from the request
@@ -56,18 +56,18 @@ def predict_dijak_backend_batch():
     input_data = pd.read_csv(file)
 
     # Drop Product_Id before prediction
-    features = input_data.drop("Product_Id", axis=1)
+    features = input_data.drop("Personal_Loan", axis=1)
 
     # Make predictions
     predictions = model.predict(features).tolist()
 
-    # Build structured output with Product_Id, Store_Id, and rounded revenue
+    # Build structured output with Product_Id, Store_Id, and rounded output
     output_list = []
     for i in range(len(predictions)):
         output_list.append({
-            "Product_Id": input_data.loc[i, "Product_Id"],
-            "Store_Id": input_data.loc[i, "Store_Id"],
-            "Revenue Prediction": round(predictions[i], 2)
+            #"Product_Id": input_data.loc[i, "Product_Id"],
+            #"Store_Id": input_data.loc[i, "Store_Id"],
+            "Prediction": round(predictions[i], 2)
         })
 
     return jsonify(output_list)