app.py

from transformers import pipeline
import gradio as gr

def image_classifier(image):
    classifier = pipeline("image-classification", model="dupthotshering/bhutanese-textile-model")
    results = classifier(image)
    
    # Convert results into a dictionary with class names as keys
    formatted_results = {result['label']: result['score'] for result in results}
    return formatted_results

# Define Gradio interface
demo = gr.Interface(
    fn=image_classifier,
    inputs=gr.Image(type="pil"),  # Ensure input is an image
    outputs=gr.Label(num_top_classes=7),  # Display all class scores
    title="Bhutanese Textile Classifier",
    description="Upload an image to classify it into one of the Bhutanese textile categories."
)

demo.launch()