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app.py

@@ -23,11 +23,9 @@ def predict_product_sales_price():
     sample = {
         'Product_Weight': product_data['Product_Weight'],
         'Product_Sugar_Content': product_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
         'Product_Type': product_data['Product_Type'],
         'Product_MRP': product_data['Product_MRP'],
         'Store_Id': product_data['Store_Id'],
-        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
         'Store_Size': product_data['Store_Size'],
         'Store_Location_City_Type': product_data['Store_Location_City_Type'],
         'Store_Type': product_data['Store_Type']
@@ -45,11 +43,12 @@ def predict_product_sales_price():
 # Define an endpoint to predict product sales price for a batch of product
 @super_kart_api.post('/v1/productbatch')
 def predict_product_batch():
-    # Get the uploaded CSV file from the request
+   # Get the uploaded CSV file from the request
     file = request.files['file']
 
     # Read the file into a DataFrame
     input_data = pd.read_csv(file)
+    input_data = input_data.drop(['Product_Id','Store_Establishment_Year','Product_Allocated_Area'],axis=1)
 
     # Make predictions for the batch data
     predictions = model.predict(input_data).tolist()

super_kart_model_v1_0.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5e1101d4e720ab19a7af36a63f3ce911a05a3fc56da59fe34ee106156ab5dd1c
-size 653576
+oid sha256:928b73c125a9a0a11d46cceaee99bbd78754562764ca064764ae121f019276cf
+size 619822