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app.py

@@ -43,16 +43,16 @@ def predict_sales():
     # You MUST use the *trained* encoder to transform the new data
     # encoded_new_data = encoder.transform(input_data[['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type']])
 
-    encoded_new_data = pd.get_dummies(
-      input_data,
-      columns=['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type'],
-      drop_first=True,
-    );
-    print("The data entered are below")
-    print(encoded_new_data)
+    #encoded_new_data = pd.get_dummies(
+    #  input_data,
+    #  columns=['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type'],
+    #  drop_first=True,
+    #);
+    #print("The data entered are below")
+    #print(encoded_new_data)
 
     # Make a Sales prediction using the trained model
-    prediction = model.predict(encoded_new_data).tolist()[0]
+    prediction = model.predict(input_data).tolist()[0]
 
     #Calculate the actual price
     predicted_sales = np.exp(prediction)