from transformers import pipeline
import gradio as gr

repo_id = "pamunarr/P7EjOpc1-MecAt"

classifier = pipeline('text-classification', model=repo_id)
labels = {
    "LABEL_0" : "World" , "LABEL_1" : "Nigeria" , "LABEL_2" : "Health" ,
    "LABEL_3" : "Africa" , "LABEL_4" : "Politics"
}

def predict(text):
    scores = classifier(text , top_k = 5)
    return {labels[dicc["label"]] : dicc["score"] for dicc in scores}

gr.Interface(fn=predict, inputs="text", outputs=gr.components.Label(num_top_classes=5)).launch(share=False)