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app.py

@@ -8,21 +8,6 @@ st.title("SuperKart Sales Prediction")
 # Section for online prediction
 st.subheader("Sales Prediction")
 
-#Product_Weight = st.number_input("Product Weight", min_value=0.00, value=16.54)
-#Product_Sugar_Content =  st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"], value="")
-#Product_Allocated_Area = st.number_input("Product Allocated area", value=0.144)
-#Product_Type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods',
-#'Health and Hygiene', 'Snack Foods', 'Meat', 'Household',
-#'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks',
-#'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
-#Product_MRP = st.number_input("Product MRP", 171.43)
-#Store_Id =  st.selectbox("Select Store", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
-#Store_Establishment_Year = st.number_input("Store Establishment year", 1999)
-#Store_Size = st.selectbox("Select Store Size", ['Medium', 'High', 'Small'])
-#Store_Location_City_Type = st.selectbox("Select Store Location", ['Tier 2', 'Tier 1', 'Tier 3'])
-#Store_Type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1',
-#       'Food Mart'])
-
 Product_Weight = st.number_input("Product Weight", min_value=0.01, value=16.54)
 Product_Sugar_Content =  st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"], index=0)
 Product_Allocated_Area = st.number_input("Product Allocated area", value=0.144)
@@ -42,23 +27,23 @@ Store_Type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Stor
 
 # Convert user input into a DataFrame
 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_Establishment_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 the "Predict" button is clicked
 if st.button("Predict"):
     response = requests.post("https://codingbuddy-superkartbackendapi.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
     if response.status_code == 200:
-        prediction = response.json()['Predicted Price (in dollars)']
-        st.success(f"Predicted Rental Price (in dollars): {prediction}")
+        prediction = response.json()['Predicted_Sale']
+        st.success(f"Predicted Sales: {prediction}")
     else:
         st.error("Error making prediction.")