added app.py and requirements.txt

app.py

@@ -0,0 +1,51 @@
+from PIL import Image
+import gradio as gr
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+import torch
+
+# Init model, transforms
+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"} 
+# Transform our image and pass it through the model
+    inputs = transforms(im, return_tensors='pt')
+    output = model(**inputs)
+
+# Predicted Class probabilities
+    proba = output.logits.softmax(1)
+
+# Predicted Classes
+    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")
+]
+
+
+
+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 = ["surprisa.png"]
+
+gr.Interface(
+    predict,
+    inputs,
+    outputs = 'label',
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    theme="huggingface",
+).launch(debug=True, enable_queue=True)

requirements.txt

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+transformers
+gradio
+requests
+Pillow
+torch