app.py

from transformers import pipeline
import gradio as gr


MODEL_NAME = "imageclassif"
HF_USER = "universalml"


def prediction_function(input_file):
    repo_id = HF_USER + "/" + MODEL_NAME
    model = pipeline("image-classification", model=repo_id)

    try:
        result = model(input_file)
        predictions = {}
        labels = []
        for each_label in result:
            predictions[each_label["label"]] = each_label["score"]
            labels.append(each_label["label"])
        result = predictions
    except:
        result = "no data provided!!"

    return result


def create_interface():
    interface = gr.Interface(
        fn=prediction_function,
        inputs=gr.Image(type="pil"),
        outputs=gr.Label(num_top_classes=3),
        title=MODEL_NAME,
    )

    interface.launch(debug=True)


create_interface()