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app.py

@@ -20,42 +20,19 @@ Store_Type = st.selectbox("Store Type", ["Supermarket Type2","Departmental Store
 Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0)
 Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar","Regular","No Sugar"])
 
-# Start with numerical features dictionary
-features_dict = {
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
     'Product_Weight': Product_Weight,
     'Product_Allocated_Area': Product_Allocated_Area,
     'Product_MRP': Product_MRP,
-    'Store_Age': Store_Age
-}
-
-# Manual one-hot encoding for categorical features as per after-encoding columns order
-# Product_Sugar_Content
-for cat in ["Low Sugar", "No Sugar", "Regular"]:
-    features_dict[f'Product_Sugar_Content_{cat}'] = int(Product_Sugar_Content == cat)
-
-# Store_Id
-for cat in ["OUT001","OUT002","OUT003","OUT004"]:
-    features_dict[f'Store_Id_{cat}'] = int(Store_Id == cat)
-
-# Store_Size
-for cat in ["High", "Medium", "Small"]:
-    features_dict[f'Store_Size_{cat}'] = int(Store_Size == cat)
-
-# Store_Location_City_Type
-for cat in ["Tier 1", "Tier 2", "Tier 3"]:
-    features_dict[f'Store_Location_City_Type_{cat}'] = int(Store_Location_City_Type == cat)
-
-# Store_Type
-for cat in ["Departmental Store","Food Mart","Supermarket Type1","Supermarket Type2"]:
-    features_dict[f'Store_Type_{cat}'] = int(Store_Type == cat)
-
-# Product_Group
-for cat in ["Non-Food/Household","Packaged/Processed Foods","Perishable Foods"]:
-    features_dict[f'Product_Group_{cat}'] = int(Product_Group == cat)
-
-# Create DataFrame with matching columns
-input_data = pd.DataFrame([features_dict])
-
+    'Store_Age': Store_Age,
+    'Product_Group': Product_Group,
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Store_Id': Store_Id,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
+}])
 
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict"):