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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)
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="pil", label="captcha image")
text = gr.Textbox(label="recognized character sequence")
def recognize_captcha(input):
input = transform(input.convert('RGB')).unsqueeze(0)
model = torch.jit.load(model_path)
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 ",
examples = ['examples/251615.png','examples/e7dx2r.jpg','examples/980209.png','examples/2DMM4.png',
'examples/H1GQD.png','examples/Sv5Cwm.jpg','examples/Wwx7.png','examples/qw7vr.png',
'examples/mCCyy.jpg','examples/mCGe.jpg','examples/0a0eou.png','examples/9322.png',
'examples/g5cch.png','examples/0HHM.jpg','examples/qWzNeZ.png','examples/tU6.png','examples/000073.jpg']
)
iface.launch() |