| from huggingface_hub import from_pretrained_fastai | |
| import gradio as gr | |
| repo_id = "pamunarr/P7EjOpc1-LSTM" | |
| learner = from_pretrained_fastai(repo_id) | |
| labels = ["World" , "Nigeria" , "Health" , "Africa" , "Politics"] | |
| def predict(text): | |
| _ , _ , probs = learner.predict(text) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| gr.Interface(fn=predict, inputs="text", outputs=gr.components.Label(num_top_classes=5)).launch(share=False) |