app.py

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()