Create app.py

app.py

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+import gradio as gr
+import torch
+
+from timm import create_model
+from timm.data import resolve_data_config
+from timm.data.transforms_factory import create_transform
+
+IMAGENET_1K_URL = "https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt"
+LABELS = requests.get(IMAGENET_1K_URL).text.strip().split("\n")
+model = create_model('resnet50', pretrained=True)
+transform = create_transform(
+    **resolve_data_config({}, model=model)
+)  
+
+model.eval()
+
+def predict_fn(img)"
+  img = img.convert('RGB')
+  img = transform(img).unsqueeze(0)
+  with torch.no_grad():
+    out = model(img)
+    
+  probabilities = torch.nn.functional.softmax(out[0], dim=0)
+  values, indices = torch.topk(probabilities, k=3)
+  return {LABELS[i]: v.item() for i, v in zip(indices, values)}
+  
+gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label').launch()
+