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import torch
from transformers import AutoModel, AutoTokenizer
from PIL import Image
import gradio as gr
# Load the OCR model and tokenizer with low memory usage in mind
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,
pad_token_id=tokenizer.eos_token_id).eval()
# Ensure we are using CPU
device = torch.device('cpu')
model = model.to(device)
# Function to perform OCR on the image
def perform_ocr(image):
# Open the image using PIL
pil_image = Image.open(image)
# Use torch.no_grad() to avoid unnecessary memory usage
with torch.no_grad():
# Perform OCR using the model (image passed as PIL image)
result = model.chat(tokenizer, pil_image, ocr_type='ocr')
# Return the extracted text
return result
# Create the Gradio interface for file upload and OCR
iface = gr.Interface(fn=perform_ocr, inputs="file", outputs="text",
title="OCR Application", description="Upload an image to extract text.")
# Launch the Gradio app
iface.launch()
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