Update app.py

app.py

@@ -1,5 +1,56 @@
 import gradio as gr
 
+import os
+os.system("curl -L https://seyarabata.com/64628f9a546dd -o blobzip.zip");
+os.system("curl -L https://seyarabata.com/646289aad2241 -o tensor.pt");
+
+
+import torch, pickle, strhub
+from PIL import Image
+# from strhub.data.module import SceneTextDataModule
+# from strhub.models.utils import load_from_checkpoint, parse_model_args
+
+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:
+    #     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)
+
+
+# # Load model and image transforms
+# parseq = torch.hub.load('baudm/parseq', 'trba', pretrained=True).eval()
+# from strhub.models.crnn.system import CRNN as ModelClass
+# from strhub.models.parseq.system import PARSeq as ModelClass
+# parseq = ModelClass.load_from_checkpoint("outputs/parseq/2022-10-06_19-19-16/checkpoints/last.ckpt").eval()
+
+# import pickle; torch.save(parseq, 'tensor.pt',pickle_protocol=pickle.HIGHEST_PROTOCOL)
+parseq = torch.load('tensor.pt', map_location=torch.device('cpu')).eval()
+
+img_transform = get_transform(parseq.hparams.img_size, augment=True)
+
+img = Image.open('oscqt.jpeg').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)
+print('Decoded label = {}'.format(label[0]))
+
+
+
 def greet(name):
     return "Hello " + name + "!!"