app.py

# app.py
import json
import gradio as gr
from transformers import pipeline

# 1) Load base labels from JSON
with open("labels.json", "r") as f:
    base_labels = json.load(f)

# 2) Prepare default textbox value
default_label_str = ", ".join(base_labels)

# 3) Initialize zero-shot classifier
classifier = pipeline(
    task="zero-shot-classification",
    model="facebook/bart-large-mnli"
)

# 4) Interface function that merges runtime labels
def tag_question(question: str, labels_str: str):
    # Split & clean the user-supplied string
    labels = [lbl.strip() for lbl in labels_str.split(",") if lbl.strip()]
    # Zero-shot classify
    out = classifier(question, candidate_labels=labels)
    # Return top-3 labels with scores
    return {lbl: round(score,3) for lbl, score in zip(out["labels"], out["scores"])}

# 5) Build the Gradio UI
iface = gr.Interface(
    fn=tag_question,
    inputs=[
        gr.Textbox(lines=3, label="Question"),
        gr.Textbox(lines=2, label="Candidate Labels (comma-separated)",
                   value=default_label_str)
    ],
    outputs=gr.Label(num_top_classes=3),
    title="Hybrid Zero-Shot Question Tagger",
    description="Loaded labels from `labels.json`, editable at runtime."
)

if __name__ == "__main__":
    iface.launch()