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app.py

@@ -22,19 +22,17 @@ Product_Type = st.selectbox(
 )
 Product_MRP = st.number_input("Product MRP (Maximum Retail Price)", min_value=0.0, step=0.5)
 Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2000)
-Store_Size = st.selectbox("Store Size", ["High", "Medium", "Low"])
+Store_Size = st.selectbox("Store Size", ["High", "Medium", "Small"])
 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"])
 
 # Convert user input into a DataFrame
 input_data = pd.DataFrame([{
-    'Product_Id': Product_Id,
     '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,
@@ -45,7 +43,7 @@ input_data = pd.DataFrame([{
 if st.button("Predict"):
     response = requests.post("https://Anu159-SuperKartSalesForecastPredictionBackend.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()['prediction']      # check what should be value here
+        prediction = response.json()['Predicted Price (in dollars)']
         st.success(f"Predicted Product Revenue (in dollars): {prediction}")
     else:
         st.error("Error making prediction.")
@@ -66,3 +64,4 @@ if uploaded_file is not None:
             st.write(predictions)  # Display the predictions
         else:
             st.error("Error making batch prediction.")
+