Create app.py

app.py

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+import gradio as gr
+from transformers import ViTHybridImageProcessor, ViTHybridForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "google/vit-hybrid-base-bit-384"
+feature_extractor = ViTHybridImageProcessor.from_pretrained(model_name)
+model = ViTHybridForImageClassification.from_pretrained(model_name)
+
+# Function for prediction
+def classify_image(image):
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+# Gradio UI
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="ViT-Hybrid Image Classifier",
+    description="Upload an image to classify it using the ViT-Hybrid model.",
+)
+
+if __name__ == "__main__":
+    iface.launch()