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)