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import gradio as gr
import spaces
from transformers import AutoModel, AutoTokenizer, AutoProcessor
from PIL import Image
import torch
# Load PaddleOCR-VL model
model_name = "PaddlePaddle/PaddleOCR-VL"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
if torch.cuda.is_available():
model = model.cuda()
@spaces.GPU
def ocr_inference(image):
"""
Perform OCR on the input image using PaddleOCR-VL
"""
if image is None:
return "Please upload an image."
try:
# Convert to PIL Image if needed
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
# Prepare inputs
prompt = "Extract all text from this image."
inputs = processor(images=image, text=prompt, return_tensors="pt")
if torch.cuda.is_available():
inputs = {k: v.cuda() for k, v in inputs.items()}
# Run OCR inference
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=512)
# Decode the output
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return result
except Exception as e:
return f"Error during OCR: {str(e)}"
# Create Gradio interface
demo = gr.Interface(
fn=ocr_inference,
inputs=gr.Image(type="pil", label="Upload Image for OCR"),
outputs=gr.Textbox(label="Extracted Text"),
title="PaddleOCR-VL OCR Demo",
description="Upload an image to extract text using PaddlePaddle/PaddleOCR-VL model"
)
demo.launch() |