app.py

import gradio as gr
from transformers import pipeline

model = "VerifiedPrompts/CNTXT-Filter-Prompt-Opt"
classifier = pipeline("text-classification", model=model, return_all_scores=True)

def classify(prompt):
    results = classifier(prompt)[0]

    # Map model labels to readable names
    label_map = {
        "LABEL_0": "Intent is unclear, Please input more context",
        "LABEL_1": "has context",
        "LABEL_2": "missing platform, audience, budget, goal"
    }

    # Convert list of label-score dicts into a usable dict
    score_dict = {r["label"]: r["score"] for r in results}

    # 🔒 Priority logic: if label_0 score > 0, block it
    if score_dict["LABEL_0"] > 0:
        return "Intent is unclear"

    # Otherwise, return the top label
    top_label = max(results, key=lambda r: r["score"])["label"]
    return label_map[top_label]

demo = gr.Interface(
    fn=classify,
    inputs="textbox",
    outputs="text",
    title="Prompt Context Classifier",
    description="Classifies prompts as clear, vague, or missing structural context."
)

demo.launch()