app.py

from transformers import pipeline

# Define the pipeline for image classification
pipe = pipeline("image-classification", model="DGurgurov/clip-vit-base-patch32-oxford-pets")

# Define the predict function using the pipeline
def predict(image):
    # Perform inference using the pipeline
    results = pipe(image)
    return {f"Class {i}": result['label'] for i, result in enumerate(results)}

# Now you can use this predict function in your Gradio interface
import gradio as gr

# Define Gradio interface
image = gr.components.Image()
label = gr.components.Label(num_top_classes=5)

interface = gr.Interface(
    fn=predict,
    inputs=image,
    outputs=label,
    title="CLIP Model - Oxford Pets",
    description="Upload an image and get the top 5 class predictions."
)

# Launch the Gradio app
interface.launch(share=True)