Create app.py

app.py

@@ -0,0 +1,39 @@
+import torch
+from PIL import Image
+# from strhub.data.module import SceneTextDataModule
+from torchvision import transforms as T
+import gradio as gr
+
+# Load model and image transforms
+parseq = torch.hub.load('baudm/parseq', 'parseq', pretrained=True).eval()
+# img_transform = SceneTextDataModule.get_transform(parseq.hparams.img_size)
+
+transform = T.Compose([
+            T.Resize(parseq.hparams.img_size, T.InterpolationMode.BICUBIC),
+            T.ToTensor(),
+            T.Normalize(0.5, 0.5)
+        ])
+
+
+def infer(inps):
+    
+    img = inps.convert('RGB')
+    # Preprocess. Model expects a batch of images with shape: (B, C, H, W)
+    img = transform(img).unsqueeze(0)
+
+    logits = parseq(img)
+    # logits.shape  # torch.Size([1, 26, 95]), 94 characters + [EOS] symbol
+
+    # Greedy decoding
+    pred = logits.softmax(-1)
+    label, confidence = parseq.tokenizer.decode(pred)
+    # print('Decoded label = {}'.format(label[0]))
+    return label[0]
+
+
+demo = gr.Interface(fn=infer, 
+             inputs=[gr.inputs.Image(type="pil")],
+             outputs=[gr.outputs.Textbox(label="Output Text")]
+             )
+             
+demo.launch(share=True)