Create app.py

app.py

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+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import requests
+import gradio as gr
+
+# Load the model and processor
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+def predict(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+def classify_image(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    label = predict(image)
+    return label
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(type="numpy", label="Upload an Image"),
+    outputs=gr.outputs.Textbox(label="Predicted Class"),
+    title="Image Classification with ViT",
+    description="Upload an image to classify it using the Vision Transformer (ViT) model."
+)
+
+if __name__ == "__main__":
+    iface.launch()