fixing app.py

app.py

@@ -6,27 +6,27 @@ 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')
+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)
+    **resolve_data_config({}, model=model)
 )
 model.eval()
 
 def predict_fn(img):
     img = img.convert('RGB')
-    img = transform(img).unsqueze(0)
+    img = transform(img).unsqueeze(0)
 
-    with torch._nograd():
+    with torch.no_grad():
         out = model(img)
+    
+    probabilites = torch.nn.functional.softmax(out[0], dim=0)
 
-    probabilities = torch.nn.functional.softmax(out[0], dim=0)
+    values, indices = torch.topk(probabilites, k=5)
 
-    values, indices = torch.topk(probabilities,k=5)
+    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
 
-    return {LABELS[i]: v.item() for i,v in zip(indices,values)}
-
-gr.Interface(predict_fn,gr.inputs.Image(type='pil'), outputs='label').launch()
+gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label').launch()