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import streamlit as st
from huggingface_hub import snapshot_download
from pdf2image import convert_from_bytes
from PIL import Image
import torch
import os
st.set_page_config(page_title="PDF Extract Kit QA", layout="centered")
@st.cache_resource
def load_model():
model_dir = snapshot_download(repo_id="opendatalab/pdf-extract-kit-1.0", local_dir="./pdf_model", max_workers=4)
# TODO: Load model from model_dir using correct logic, e.g.:
# model = torch.load(os.path.join(model_dir, "model.pt"))
# return model
return model_dir # TEMP placeholder
model_or_dir = load_model()
def extract_answer(image, question):
# TODO: Implement the actual inference using the model
# For now, we return a placeholder
return "Answering is not implemented yet. Replace this with model inference."
st.title("📄 PDF Extract Kit: 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..."):
answer = extract_answer(page_image, question)
st.success("Answer:")
st.write(answer)
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