import gradio as gr
from transformers import pipeline

# Load your image classification model
classifier = pipeline("image-classification", model="ander-machine/autotrain-u2mob-eufcd")

def predict(image):
    results = classifier(image)

    # Convert results list into a dict {label: score}
    output = {r["label"]: float(r["score"]) for r in results}
    return output

# Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload an image"),
    outputs=gr.Label(num_top_classes=3, label="Prediction")  # change 3 to len(results) if you want all
)

if __name__ == "__main__":
    demo.launch()