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import streamlit as st
# import torch
# from transformers import AutoProcessor, UdopForConditionalGeneration
# from datasets import load_dataset
# processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=True)
# model = UdopForConditionalGeneration.from_pretrained("microsoft/udop-large")
st.title("CIC Demo (by ITT)")
st.write("Select or upload a document (/an image) to test the model.")
# Image selection
uploaded_files = st.file_uploader("Upload document(s) [/image(s)]:", type=["docx", "pdf", "jpg", "jpeg", "png"], accept_multiple_files=True)
selected_file = st.selectbox("Select a document (/an image):", uploaded_files, format_func=lambda file: file.name if file else "None")
# Display selected image
if selected_file is not None and selected_file != "None":
st.image(selected_file, caption="Selected Image")
# Model testing button
testButton = st.button("Test Model")
if testButton and selected_file != "None":
st.write("Testing the model with the selected image...")
elif testButton and selected_file == "None":
st.write("Please upload and select an image.")
# encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
# predicted_ids = model.generate(**encoding)
# print(processor.batch_decode(predicted_ids, skip_special_tokens=True)[0])
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