app.py

import streamlit as st
import torch
from transformers import AutoProcessor, UdopForConditionalGeneration
# from datasets import load_dataset

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

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("Upload and Select a document (/an image) to test the model.")

# File selection
uploaded_files = st.file_uploader("Upload document(s) [/image(s)]:", type=["docx", "pdf", "pptx", "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 file
if selected_file is not None and selected_file != "None":
    file_extension = selected_file.name.split(".")[-1]
    if file_extension in ["jpg", "jpeg", "png"]:
        image = st.image(selected_file, caption="Selected Image")
    else:
        st.write("Selected file: ", selected_file.name)

# Model Testing 
## Test button
testButton = st.button("Test Model")

## Question (/Prompt)
question = "Question answering. How many unsafe practice of Lifting Operation?"

## Perform Model Testing when Image is uploaded and selected as well as Test button is pressed
if testButton and selected_file != "None":
    st.write("Testing the model with the selected image...")
    # encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
    model_encoding = processor(iamges=image, text=question, return_tensors="pt")
    model_output = model.generate(**model_encoding)
    output_text = processor.batch_decode(model_output, skip_special_tokens=True)[0]
    st.write(output_text)    
elif testButton and selected_file == "None":
    st.write("Please upload and select a document (/an image).")