add tentative app.py

app.py

@@ -0,0 +1,43 @@
+import streamlit as st
+from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
+from pdf2image import convert_from_path
+from PIL import Image
+
+# Load model and processor
+processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
+model = LayoutLMv3ForTokenClassification.from_pretrained("microsoft/layoutlmv3-base")
+
+st.title("Document Classification with LayoutLMv3")
+
+# File uploader for PDFs, JPGs, and PNGs
+uploaded_files = 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)]
+
+        # 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}")
+
+            # 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:"
+                )
+                # Here you can implement logic to store or process feedback