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795edde | 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 44 45 46 47 48 49 50 | import gradio as gr
import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import time
MODEL = "microsoft/trocr-small-printed"
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load once at startup
processor = TrOCRProcessor.from_pretrained(MODEL)
model = VisionEncoderDecoderModel.from_pretrained(MODEL).to(device)
def extract_text(image):
if image is None:
return "⚠️ Please upload an image."
start_time = time.time()
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)
generated_ids = model.generate(pixel_values)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
runtime = round(time.time() - start_time, 2)
return f"""📝 Extracted Text:
{text}
⏱ Processed in {runtime} seconds
"""
demo = gr.Interface(
fn=extract_text,
inputs=gr.Image(type="pil", label="Upload Image"),
outputs=gr.Textbox(label="OCR Result"),
title="🖼 Image → Text Demo",
description="Upload an image with printed text. Powered by Microsoft TrOCR running locally on Hugging Face Spaces.",
examples=[
["https://huggingface.co/datasets/nielsr/image_dummy/raw/main/receipt.png"]
]
)
if __name__ == "__main__":
demo.launch() |