| import os,torch | |
| import gradio as gr | |
| from PIL import Image | |
| from torchvision import transforms as T | |
| from huggingface_hub import hf_hub_download | |
| from tokenizer import Tokenizer | |
| TOKEN = os.getenv('hf_read_token') | |
| repo_id = "Nischay103/captcha_recognition" | |
| model_file = 'captcha_model.pt' | |
| model_path = hf_hub_download(repo_id=repo_id, filename=model_file, token=TOKEN) | |
| model = torch.jit.load(model_path) | |
| charset = r"0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" | |
| _tokenizer = Tokenizer(charset) | |
| def get_transform(img_size=(32,128)): | |
| transforms = [] | |
| transforms.extend([ | |
| T.Resize(img_size, T.InterpolationMode.BICUBIC), | |
| T.ToTensor(), | |
| T.Normalize(0.5, 0.5) | |
| ]) | |
| return T.Compose(transforms) | |
| transform = get_transform() | |
| image = gr.Image(type="filepath", label="Captcha Image") | |
| text = gr.Textbox(label="Recognized Character Sequence") | |
| def recognize_captcha(image_path): | |
| input = Image.open(image_path) | |
| input = transform(input.convert('RGB')).unsqueeze(0) | |
| model = model | |
| with torch.no_grad(): | |
| probs = model(input) | |
| preds,_ = _tokenizer.decode(probs,beam_width=2) | |
| return preds[0] | |
| iface = gr.Interface( | |
| fn=recognize_captcha, | |
| inputs=image, | |
| outputs=text, | |
| title="Character Sequence Recognition from Scene-Image (Captcha)", | |
| description="Recognize captchas using different models", | |
| examples=['examples/251615.png'] | |
| ) | |
| iface.launch() |