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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+predict_product_price_v1_0.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,10 +1,11 @@
 import joblib
 import pandas as pd
+import xgboost as xgb
 from flask import Flask, request, jsonify
 # Initialize Flask app
 app = Flask("Supermarket Product Price Predictor")
 ###model = joblib.load("D:/Machine_Learning_Neural_Nets_Course/SuperKart/backend_files/predict_product_price_v1_0.joblib")
-model = joblib.load("predict_product_price_v1_0.joblib")
+loaded_model  = joblib.load("predict_product_price_v1_0.joblib")
 # Define a route for the home page
 # @sapp.get('/')
 # def home():
@@ -17,6 +18,6 @@ def predict_price():
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([input_json])
     # Make a churn prediction using the trained model
-    prediction = model.predict(input_data)
+    prediction = loaded_model.predict(input_data)
     # Return the prediction as a JSON response
     return jsonify({'Price': prediction})

predict_product_price_v1_0.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e81a78201a5b9d52595fd1013c306a33d76bce92c09f96c31512b03921880bb
+size 11194810