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import streamlit as st
from transformers import AutoModel, AutoTokenizer
from PIL import Image
import tempfile
import os
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
model = model.eval().cuda()
# Streamlit App Title
st.title("OCR with GOT-OCR2")
# File uploader for image input
image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if image_file is not None:
# Display the uploaded image
image = Image.open(image_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Save the uploaded file to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
temp_file.write(image_file.getvalue())
temp_file_path = temp_file.name
# Button to run OCR
if st.button("Run OCR"):
# Perform plain text OCR
res_plain = model.chat(tokenizer, temp_file_path, ocr_type='ocr')
# Perform formatted text OCR
res_format = model.chat(tokenizer, temp_file_path, ocr_type='format')
# Display the results
st.subheader("Plain Text OCR Results:")
st.write(res_plain)
st.subheader("Formatted Text OCR Results:")
st.write(res_format)
# You can add more OCR types as needed
# e.g., fine-grained OCR
res_fine_grained = model.chat(tokenizer, temp_file_path, ocr_type='ocr', ocr_box='')
st.subheader("Fine-Grained OCR Results:")
st.write(res_fine_grained)
# Render formatted OCR to HTML
res_render = model.chat(tokenizer, temp_file_path, ocr_type='format', render=True, save_render_file='./demo.html')
st.subheader("Rendered OCR Results (HTML):")
st.write(res_render)
# Clean up the temporary file after use
os.remove(temp_file_path)
# Note: No need for if __name__ == "__main__": st.run() |