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app.py

@@ -16,35 +16,34 @@ st.write(
 st.subheader("Online Prediction (Single Product-Store Entry)")
 
 # Collect user input for product & store features
-product_weight = st.number_input("Product Weight (grams)", min_value=0.0, value=250.0, step=50.0)
-product_sugar_content = st.selectbox("Sugar Content", ["low sugar", "regular", "no sugar"])
-product_allocated_area = st.number_input("Allocated Display Area (ratio)", min_value=0.0, max_value=1.0, value=0.15)
-product_type = st.selectbox(
-    "Product Type",
-    ["meat", "snack foods", "hard drinks", "dairy", "canned", "soft drinks", 
-     "health and hygiene", "baking goods", "bread", "breakfast", "frozen foods", 
-     "fruits and vegetables", "household", "seafood", "starchy foods", "others"]
-)
-product_mrp = st.number_input("Maximum Retail Price (MRP)", min_value=1.0, value=50.0)
+product_weight = st.number_input("Product Weight (kg)", min_value=4.0, max_value=22.0, value=12.65, step=0.1)
+product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "Non-edible"])
+product_allocated_area = st.number_input("Allocated Area (ratio)", min_value=0.004, max_value=0.298, value=0.068, step=0.001)
+product_type = st.selectbox("Product Type", [
+    "Fruits and Vegetables", "Snack Foods", "Dairy", "Canned", "Soft Drinks",
+    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
+    "Household", "Seafood", "Starchy Foods", "Meat", "Hard Drinks", "Others"
+])
+product_mrp = st.number_input("Product MRP", min_value=31.0, max_value=266.0, value=147.0, step=1.0)
 
-store_id = st.text_input("Store ID (e.g., STR001)", value="STR001")
-store_est_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2010, step=1)
-store_size = st.selectbox("Store Size", ["high", "medium", "low"])
-store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
-store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
+store_id = st.selectbox("Store ID", ["OUT001","OUT002","OUT003","OUT004"])
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1987, max_value=2025, value=2002, step=1)
+store_size = st.selectbox("Store Size", ["High", "Medium", "Low"])
+store_location_city_type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Food Mart"])
 
-# Convert user input into a DataFrame (single row)
+# Input DataFrame with EXACT column names as training
 input_data = pd.DataFrame([{
-    'Product_Weight': product_weight,
-    'Product_Sugar_Content': product_sugar_content,
-    'Product_Allocated_Area': product_allocated_area,
-    'Product_Type': product_type,
-    'Product_MRP': product_mrp,
-    'Store_Id': store_id,
-    'Store_Establishment_Year': store_est_year,
-    'Store_Size': store_size,
-    'Store_Location_City_Type': store_location_city_type,
-    'Store_Type': store_type
+    "Product_Weight": product_weight,
+    "Product_Sugar_Content": product_sugar_content,
+    "Product_Allocated_Area": product_allocated_area,
+    "Product_Type": product_type,
+    "Product_MRP": product_mrp,
+    "Store_Id": "store_id",
+    "Store_Establishment_Year": store_establishment_year,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": store_location_city_type,
+    "Store_Type": store_type
 }])
 
 # Make prediction when "Forecast Sales" is clicked