Initial commit

apps.py

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+import requests
+
+import gradio as gr
+import torch
+from timm import create_model 
+from timm.data import resolve_data_config 
+from timm.data.transforms import create_transform 
+
+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) 
+        
+    probabilites = torch.nn.functional.softmax(out[0], dim=0) 
+
+    values, indices = torch.topk(probabilites, k=5) 
+    
+    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
+    
+gr.Interface(predict_fn, gr.inputs.Image(type='pil1'), outputs='label').launch() 
+                      

requirements.txt

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+timm
+gradio