only accept one file upload at a time

app.py

@@ -10,34 +10,34 @@ model = LayoutLMv3ForTokenClassification.from_pretrained("microsoft/layoutlmv3-b
 st.title("Document Classification with LayoutLMv3")
 
 # File uploader for PDFs, JPGs, and PNGs
-uploaded_files = st.file_uploader(
+uploaded_file = st.file_uploader(
     "Upload Document", type=["pdf", "jpg", "png"], accept_multiple_files=False
 )
 
-if uploaded_files:
-    for uploaded_file in uploaded_files:
-        if uploaded_file.type == "application/pdf":
-            images = convert_from_path(uploaded_file)
-        else:
-            images = [Image.open(uploaded_file)]
+if uploaded_file:
+    # for uploaded_file in uploaded_files:
+    if uploaded_file.type == "application/pdf":
+        images = convert_from_path(uploaded_file)
+    else:
+        images = [Image.open(uploaded_file)]
 
-        # Process each image for classification
-        for i, image in enumerate(images):
-            st.image(image, caption=f'Uploaded Image {i}', use_column_width=True)
-            # Prepare image for model input
-            encoding = processor(image, return_tensors="pt")
-            outputs = model(**encoding)
-            predictions = outputs.logits.argmax(-1)
+    # Process each image for classification
+    for i, image in enumerate(images):
+        st.image(image, caption=f'Uploaded Image {i}', use_column_width=True)
+        # Prepare image for model input
+        encoding = processor(image, return_tensors="pt")
+        outputs = model(**encoding)
+        predictions = outputs.logits.argmax(-1)
 
-            # Display predictions (you may want to map indices to labels)
-            st.write(f"Predictions: {predictions}")
+        # Display predictions (you may want to map indices to labels)
+        st.write(f"Predictions: {predictions}")
 
-            # User feedback section
-            feedback = st.radio(
-                "Is the classification correct?", ("Yes", "No")
+        # User feedback section
+        feedback = st.radio(
+            "Is the classification correct?", ("Yes", "No")
+        )
+        if feedback == "No":
+            correct_label = st.text_input(
+                "Please provide the correct label:"
             )
-            if feedback == "No":
-                correct_label = st.text_input(
-                    "Please provide the correct label:"
-                )
-                # Here you can implement logic to store or process feedback
+            # Here you can implement logic to store or process feedback