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| import os | |
| import spaces # Initialize ZeroGPU before importing torch or the model. | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from openhtr.inference import Recognizer | |
| # ZeroGPU exposes a CUDA device at startup to register the model for GPU workers. | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| if device == "cpu": | |
| torch.set_num_threads(min(2, os.cpu_count() or 1)) | |
| recognizer = Recognizer(device) | |
| def recognize(image: Image.Image | None) -> str: | |
| if image is None: | |
| raise gr.Error("Upload or paste a handwritten line image first.") | |
| if not isinstance(image, Image.Image): | |
| raise gr.Error("Upload an image in a supported format such as PNG, JPEG, or WebP.") | |
| text = recognizer.recognize(image) | |
| if not text: | |
| gr.Warning("No text was recognized. Try a clearer crop of a single line.") | |
| return text | |
| with gr.Blocks(title="OpenHTR Chinese Handwriting Recognition", analytics_enabled=False) as demo: | |
| gr.Markdown("# OpenHTR-Rec\nUpload or paste a cropped image of a single line of Chinese handwriting.") | |
| with gr.Row(): | |
| image = gr.Image( | |
| type="pil", image_mode=None, sources=["upload", "clipboard"], format="png", | |
| label="Handwritten Line Image", height=280, | |
| ) | |
| output = gr.Textbox(label="Transcription", interactive=False, lines=9, buttons=["copy"]) | |
| with gr.Row(): | |
| submit = gr.Button("Recognize", variant="primary") | |
| gr.ClearButton([image, output], value="Clear") | |
| submit.click(recognize, inputs=image, outputs=output, api_name="recognize") | |
| demo.queue(default_concurrency_limit=1, max_size=16) | |
| if __name__ == "__main__": | |
| demo.launch() | |