debugged app.py

app.py

@@ -8,7 +8,7 @@ model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
 transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
 
 def predict(im):
-    labels = {0:"0-2", 1: "3-9" , 2: "10-19", 3: "20-29", 4: "30-39", 5: "40-49", 6: "50-59"} 
+    labels = {0:"0-2", 1: "3-9" , 2: "10-19", 3: "20-29", 4: "30-39", 5: "40-49", 6: "50-59", 7:"60-69",8:"more than 70"} 
 # Transform our image and pass it through the model
     inputs = transforms(im, return_tensors='pt')
     output = model(**inputs)
@@ -17,27 +17,23 @@ def predict(im):
     proba = output.logits.softmax(1)
 
 # Predicted Classes
-    preds = proba.argmax(1)
+    #preds = proba.argmax(1)
     values, indices = torch.topk(proba, k=5)
-    result = zip(indices, values)
+    
     
 
     return {labels[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
 
-
-print(predict(Image.open("baby.jpg")))
-
-
 inputs = [
-    gr.inputs.Image(type="pil", label="Original Image")
+    gr.inputs.Image(type="pil", label="Input Image")
 ]
 
 
 
-title = "Vit-Age-Classification"
+title = "ViT-Age-Classification"
 description = "ViT-Age-Classification is used to categorize an individual's age using images"
 article = " <a href='https://huggingface.co/nateraw/vit-age-classifier'>Model Repo on Hugging Face Model Hub</a>"
-examples = ["baby.jpg"]
+examples = ["stock_baby.webp","stock_teen.webp","stock_guy.jpg","stock_old_woman.jpg"]
 
 gr.Interface(
     predict,