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app.py

@@ -4,6 +4,7 @@ import requests
 
 # Set the title of the Streamlit app
 st.title('SuperKart Product Store Sales Total Prediction')
+st.write("This tool predicts store sales based on their products details")
 
 # Section for online prediction
 st.subheader('Online Prediction')
@@ -45,7 +46,7 @@ input_data = pd.DataFrame([{
 if st.button('Predict'):
     # Send the input data to the backend API for prediction
     # Make sure the URL is correct for your Hugging Face Space backend
-    response = requests.post("https://rommat/store_sales_total_prediction_backend.hf.space/v1/productstoresalestotal", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
+    response = requests.post("https://rommat/storesalestotalpredictionbackend.hf.space/v1/productstoresalestotal", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
     if response.status_code == 200:
         prediction = response.json()['Predicted Product Store Sales Total (in dollars)']
         st.success(f'Predicted Product Store Sales Total Price (in dollars): {prediction}')