Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import torch
+from pdf2image import convert_from_bytes
+from PIL import Image
+
+@st.cache_resource
+def load_model():
+    return pipeline(
+        task="document-question-answering",
+        model="naver-clova-ix/donut-base-finetuned-docvqa",
+        device=0 if torch.cuda.is_available() else -1,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    )
+
+qa_pipeline = load_model()
+
+st.set_page_config(page_title="Donut PDF QA", layout="centered")
+st.title("📄 Donut: PDF Question Answering")
+
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+question = st.text_input("Ask a question about the document")
+
+if uploaded_file and question:
+    st.write("Reading and converting PDF...")
+    images = convert_from_bytes(uploaded_file.read(), dpi=200)
+
+    page_number = st.number_input("Select page", min_value=1, max_value=len(images), value=1, step=1)
+    page_image = images[page_number - 1]
+    st.image(page_image, caption=f"Page {page_number}")
+
+    with st.spinner("Finding answer..."):
+        result = qa_pipeline(image=page_image, question=question)
+        st.success("Answer:")
+        st.write(result[0]['answer'])