Upload folder using huggingface_hub

app.py

@@ -112,62 +112,30 @@ st.subheader("Batch Prediction")
 
 st.write("Upload a CSV file with columns: Product_Weight, Product_Sugar_Content, Product_Allocated_Area, Product_Type, Product_MRP, Store_Establishment_Year, Store_Size, Store_Location_City_Type, Store_Type")
 
-try:
-  # File uploader
-  uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
-except Exception as e:
-  st.error("An unexpected error occurred during prediction.")
-  st.exception(e)
+# Allow users to upload a CSV file
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
 
 if uploaded_file is not None:
-    st.success("File uploaded successfully")
-    # Read CSV for display
-    try:
-        # Read the uploaded file into a pandas DataFrame
-        input_df = pd.read_csv(uploaded_file)
-        st.write("Uploaded Data Preview:")
-        st.dataframe(input_df.head())
+    if st.button("Predict Batch"):
+        try:
+            # Prepare file payload correctly
+            files = {"file": (uploaded_file.name, uploaded_file, "text/csv")}
 
-        # Prepare file for API
-        # Reset file pointer to start
-        uploaded_file.seek(0)
-        files = {"file": (uploaded_file.name, uploaded_file, "text/csv")}
+            # Send the POST request to the Flask API
+            response = requests.post(
+                "https://sahilsingla-SuperKartPredictionBackend.hf.space/v1/salesbatch",
+                files=files
+            )
 
-        # Send POST request to batch API
-        model_batch_url = "https://sahilsingla-SuperKartPredictionBackend.hf.space/v1/salesbatch"
-        try:
-            response = requests.post(model_batch_url, files=files)
-            response.raise_for_status()
-            result = response.json()
-            predictions = result.get("predictions", None)
-            
-            if predictions:
-                # Create DataFrame with predictions
-                output_df = input_df.copy()
-                output_df["Predicted_Sales"] = [float(p) for p in predictions]
-                
-                # Display predictions
-                st.write("Batch Prediction Results:")
-                st.dataframe(output_df)
-                
-                # Provide download button for results
-                csv_buffer = StringIO()
-                output_df.to_csv(csv_buffer, index=False)
-                st.download_button(
-                    label="Download Predictions",
-                    data=csv_buffer.getvalue(),
-                    file_name="batch_predictions.csv",
-                    mime="text/csv"
-                )
+            # Check response status
+            if response.status_code == 200:
+                predictions = response.json()
+                st.success("Batch predictions completed!")
+                st.write(predictions)
             else:
-                st.error("No predictions returned from API")
-        except requests.exceptions.RequestException as e:
-            st.error(f"Error contacting the batch API: {e}")
-        except ValueError:
-            st.error("Invalid response format from batch API")
-        except pd.errors.EmptyDataError:
-            st.error("Uploaded CSV is empty")
+                st.error(f"Error: {response.status_code}")
+                st.error(response.text)
+
         except Exception as e:
-            st.error(f"Error processing CSV: {e}")
-    except Exception as e:
-        st.error(f"Error reading CSV: {e}")
+            st.error("An unexpected error occurred during prediction.")
+            st.exception(e)