app.py

import os
import wandb
import streamlit as st
from transformers import LayoutLMv3Processor, LayoutLMv3ForSequenceClassification
from pdf2image import convert_from_bytes
from PIL import Image

wandb_api_key = os.getnev("WANDB_API_KEY")
if not wandb_api_key:
    st.error(
        "Couldn't find WanDB API key. Please set it up as an environemnt variable",
        icon="🚨",
    )
else:
    wandb.login(key=wandb_api_key)

processor = LayoutLMv3Processor.from_pretrained("model/layoutlmv3/")
model = LayoutLMv3ForSequenceClassification.from_pretrained("model/layoutlmv3/")
id2label = model.config.id2label
label2id = model.config.label2id

st.title("Document Classification with LayoutLMv3")

uploaded_file = st.file_uploader(
    "Upload Document", type=["pdf", "jpg", "png"], accept_multiple_files=False
)

if uploaded_file:
    run = wandb.init(project='hydra-classifier', name='feedback-loop')

    if uploaded_file.type == "application/pdf":
        images = convert_from_bytes(uploaded_file.getvalue())
    else:
        images = [Image.open(uploaded_file)]

    for i, image in enumerate(images):
        st.image(image, caption=f'Uploaded Image {i}', use_container_width=True)

        print(f'Encoding image with index {i}')
        encoding = processor(
            image,
            return_tensors="pt",
            truncation=True,
            max_length=512,
        )
        print(f'Predicting image with index {i}')
        outputs = model(**encoding)
        prediction = outputs.logits.argmax(-1)[0].item()

        st.write(f"Prediction: {id2label[prediction]}")

        feedback = st.radio(
            "Is the classification correct?", ("Yes", "No"),
            key=f'prediction-{i}'
        )

        if feedback == "No":
            correct_label = st.selectbox(
                "Please select the correct label:", labels,
                key=f'selectbox-{i}'
            )
            print(f'Correct label for image {i}: {correct_label}')

            run.log({
                'filepath': uploaded_file,
                'filetype': uploaded_file.type,
                'predicted_label': id2label[prediction],
                'predicted_label_id': prediction,
                'correct_label': correct_label,
                'correct_label_id': label2id[correct_label]
            })

    run.finish()