Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+
+def test(image):
+    processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    probabilities_list = probabilities.tolist()[0]  
+
+    class_probabilities = {
+        model.config.id2label[class_idx]: probability
+        for class_idx, probability in enumerate(probabilities_list)
+    }
+    
+    top_4_probabilities = dict(sorted(class_probabilities.items(), key=lambda item: item[1], reverse=True)[:4])
+
+    return top_4_probabilities
+
+demo = gr.Interface(fn=test, inputs=gr.Image(type="pil"), outputs=gr.Label("Top 4 Scores and Classes"))
+demo.launch()