app.py

import gradio as gr
from transformers import pipeline

# Load multi-class topic classification pipeline
topic_pipeline = pipeline(
    "text-classification",
    model="AfroLogicInsect/topic-model-analysis-model",
    tokenizer="AfroLogicInsect/topic-model-analysis-model",
    return_all_scores=True
)

def predict_topics(text):
    if not text.strip():
        return [["Please enter some text", 0.0]]

    results = topic_pipeline(text)
    sorted_results = sorted(results[0], key=lambda x: x['score'], reverse=True)[:5]

    # Format for Gradio output: list of [label, score]
    return [[res['label'], round(res['score'], 3)] for res in sorted_results]

iface = gr.Interface(
    fn=predict_topics,
    inputs=gr.Textbox(label="Enter text"),
    outputs=gr.Dataframe(
        headers=["Topic", "Confidence"],
        label="Top 5 Predicted Topics",
        type="array"
    )
)

iface.launch(share=True)