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app.py

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+import os 
+os.system("curl -L -o tensor.pt https://seyarabata.com/btfo_by_24mb_model")
+
+import torch
+from PIL import Image
+import gradio as gr
+from torchvision import transforms as T
+from typing import Tuple
+
+def get_transform(img_size: Tuple[int], augment: bool = False, rotation: int = 0):
+    transforms = []
+    if augment:
+        from .augment import rand_augment_transform
+        transforms.append(rand_augment_transform())
+    if rotation:
+        transforms.append(lambda img: img.rotate(rotation, expand=True))
+    transforms.extend([
+        T.Resize(img_size, T.InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(0.5, 0.5)
+    ])
+    return T.Compose(transforms)
+
+parseq = torch.load('tensor.pt', map_location=torch.device('cpu')).eval()
+img_transform = get_transform(parseq.hparams.img_size)
+
+def captcha_solver(img):
+  img = img.convert('RGB')
+  img = img_transform(img).unsqueeze(0)
+
+  logits = parseq(img)
+  logits.shape
+  
+  # # Greedy decoding
+  pred = logits.softmax(-1)
+  label, confidence = parseq.tokenizer.decode(pred)
+  return label[0]
+
+demo = gr.Interface(fn=captcha_solver, inputs=gr.inputs.Image(type="pil"), outputs=gr.outputs.Textbox())
+demo.launch()