| 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) |