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app.py

@@ -41,23 +41,6 @@ def predict_sales():
     # Return the prediction as a JSON response
     return jsonify({'Prediction': prediction})
 
-# Define an endpoint to predict churn for a batch of customers
-@sales_predictor_api.post('/v1/productstorebatch')
-def predict_churn_batch():
-    # Get the uploaded CSV file from the request
-    file = request.files['file']
-
-    # Read the file into a DataFrame
-    input_data = pd.read_csv(file)
-
-    # Make predictions for the batch data and convert raw predictions into a readable format
-    predictions = [model.predict(input_data.drop(["Product_Id","Store_Id"],axis=1)).tolist()]
-
-    prod_id_list = input_data.Product_Id.values.tolist()
-    output_dict = dict(zip(prod_id_list, predictions))
-
-    return output_dict
-
 # Run the Flask app in debug mode
 app = Flask(__name__)
 if __name__ == '__main__':