Create app.py

app.py

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+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import torch
+import gradio as gr
+
+# Load the model and feature extractor
+model_name = 'google/vit-base-patch16-224'
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+model = ViTForImageClassification.from_pretrained(model_name)
+
+# Function to load and preprocess the image
+def preprocess_image(image):
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    return inputs['pixel_values']
+
+# Function to predict the class of the image
+def predict_image(image):
+    pixel_values = preprocess_image(image)
+    with torch.no_grad():
+        outputs = model(pixel_values)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+# Define the Gradio interface
+image_input = gr.inputs.Image(type="pil")
+label_output = gr.outputs.Label(num_top_classes=3)
+
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=image_input,
+    outputs=label_output,
+    title="Image Classification with ViT",
+    description="Upload an image and get the predicted label using Vision Transformer (ViT)."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()