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import streamlit as st
from transformers import LayoutLMv3Processor, LayoutLMv3ForSequenceClassification
from pdf2image import convert_from_bytes
from PIL import Image
labels = [
'budget',
'email',
'form',
'handwritten',
'invoice',
'language',
'letter',
'memo',
'news article',
'questionnaire',
'resume',
'scientific publication',
'specification',
]
id2label = {i: label for i, label in enumerate(labels)}
label2id = {v: k for k, v in id2label.items()}
processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
model = LayoutLMv3ForSequenceClassification.from_pretrained(
"microsoft/layoutlmv3-base",
num_labels=len(labels),
id2label=id2label,
label2id=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:
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)
encoding = processor(
image,
return_tensors="pt",
truncation=True,
max_length=512,
)
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
)
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