Upload folder using huggingface_hub

app.py

@@ -28,11 +28,13 @@ Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type
 
 # 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,
@@ -41,9 +43,9 @@ input_data = pd.DataFrame([{
 
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict"):
-    response = requests.post("https://Anu159-md2fe.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    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()['Predicted Price (in dollars)']
+        prediction = response.json()['prediction']      # check what should be value here
         st.success(f"Predicted Product Revenue (in dollars): {prediction}")
     else:
         st.error("Error making prediction.")
@@ -57,11 +59,10 @@ uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["
 # Make batch prediction when the "Predict Batch" button is clicked
 if uploaded_file is not None:
     if st.button("Predict Batch"):
-        response = requests.post("https://Anu159-md2fe.hf.space/v1/salesbatch", files={"file": uploaded_file})  # Send file to Flask API
+        response = requests.post("https://Anu159-SuperKartSalesForecastPredictionBackend.hf.space/v1/salesbatch", files={"file": uploaded_file})  # Send file to Flask API
         if response.status_code == 200:
             predictions = response.json()
             st.success("Batch predictions completed!")
             st.write(predictions)  # Display the predictions
         else:
             st.error("Error making batch prediction.")
-