Upload folder using huggingface_hub

SuperKart_Sales_prediction_model_v1_0.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2532dd7116bac66182adbccd88dff8a6b1fa5d3ef3e080c3c06bdee0aedd7c17
-size 190216
+oid sha256:7dd5f2e0b529409e7f8893fcac69d00a5918e13c60210043b39644ef1a7482e8
+size 194909

app.py

@@ -20,6 +20,7 @@ def home():
     The function displays simple welcome message.
     """
 
+    # message added  as part of debugging process to check the function was getting invoked
     handler = logging.FileHandler('app.log')
     product_sales_predictor_api.logger.addHandler(handler)
     product_sales_predictor_api.logger.setLevel(logging.INFO)
@@ -36,6 +37,8 @@ def predict_Product_Sales():
     the predicted sales price as a JSON response.
     """
 
+    #All types of logging enabled when issue was faced in endpoint access.
+    #code retained as it is after debugging
     product_sales_predictor_api.logger.info('single prediction entered')
     
     print(">>> product endpoint invoked!", flush=True)
@@ -60,11 +63,6 @@ def predict_Product_Sales():
     # Make prediction (get log_price)
     predicted_sales = model.predict(input_data)[0]
 
-    # Convert predicted_price to Python float
-    # predicted_sales = round(float(predicted_sales), 2)
-    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
-    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
-
     # Return the actual Predicted sales price
     return jsonify({'Predicted Sales': predicted_sales})