File size: 1,411 Bytes
b42b1aa 8434495 f2460f7 c920662 d67cd1e 9184993 76581dc 8434495 a8781ff 8434495 dba283c a8781ff 3534c83 76581dc d67cd1e 9fca578 dba283c d67cd1e 76581dc d67cd1e 76581dc dba283c a8781ff 8434495 dba283c 2b3d2ae dba283c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import torch
from transformers import AutoModel, AutoTokenizer
from PIL import Image
import gradio as gr
import os
# Load the OCR 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,
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 file
def perform_ocr(image_file_path):
# Open the image using PIL
image = Image.open(image_file_path)
# Save the image temporarily
temp_image_path = "temp_image.png"
image.save(temp_image_path)
# Use torch.no_grad() to avoid unnecessary memory usage
with torch.no_grad():
# Perform OCR using the model (pass the file path of the saved image)
result = model.chat(tokenizer, temp_image_path, ocr_type='ocr')
# Clean up the temporary image file
os.remove(temp_image_path)
# 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()
|