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app.py

@@ -25,7 +25,7 @@ Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Reg
 Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 2", "Tier 1", "Tier 3"])
 Store_Size = st.selectbox("Store Size", ["Medium", "High", "Small"])
 Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=50.0, step=1.0)
-Product_id = st.text_input("Product ID (Unique Code)", "FD6114")
+Product_Id = st.text_input("Product ID (Unique Code)", "FD6114")
 Store_Type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
 
 # Convert user input into DataFrame
@@ -39,7 +39,7 @@ input_data = pd.DataFrame([{
     'Store_Location_City_Type': Store_Location_City_Type,
     'Store_Size': Store_Size,
     'Product_Allocated_Area': Product_Allocated_Area,
-    'Product_id': Product_id,
+    'Product_Id': Product_Id,
     'Store_Type': Store_Type
 }])
 
@@ -69,8 +69,8 @@ if uploaded_file is not None:
             files={"file": uploaded_file}
         )
         if response.status_code == 200:
-            predictions = response.json()
-            st.success("Batch predictions completed!")
-            st.write(predictions)
+          prediction = response.json()['Predicted_Sales']
+          st.success(f"Predicted Sales: {prediction}")
         else:
-            st.error("Error making batch prediction.")
+          st.error(f"Error making prediction: {response.text}")
+