"""
Content-Freshness-Scorer Space. Self-contained: downloads repo for model files only.
"""
import traceback
from pathlib import Path

import gradio as gr

REPO_ID = "syeedalireza/content-freshness-scorer"
FRESHNESS_HALFLIFE_DAYS = 365.0

def run(content, days_old):
    if not (content or "").strip():
        return "Enter content text."
    try:
        from huggingface_hub import snapshot_download
        path = Path(snapshot_download(REPO_ID))
        model_dir = path / "models"
    except Exception:
        return f"Download error:\n{traceback.format_exc()}"
    if not (model_dir / "encoder.joblib").exists() or not (model_dir / "query_embeddings.joblib").exists():
        return "Run train.py first (builds query embeddings from target_queries.txt) and re-upload."
    try:
        import joblib
        import numpy as np
        encoder = joblib.load(model_dir / "encoder.joblib")
        query_emb = joblib.load(model_dir / "query_embeddings.joblib")
        content_emb = encoder.encode([content.strip()])
        sim = np.dot(content_emb, query_emb.T).max(axis=1)[0]
        relevance = float(sim)
        days = float(days_old) if days_old is not None else 0.0
        freshness = min(1.0, 0.5 ** (days / FRESHNESS_HALFLIFE_DAYS)) if days >= 0 else 1.0
        refresh_priority = 0.5 * relevance + 0.5 * (1 - freshness)
        return f"**Relevance (vs queries):** {relevance:.3f}\n**Freshness score:** {freshness:.3f}\n**Refresh priority:** {refresh_priority:.3f}"
    except Exception:
        return f"Error:\n{traceback.format_exc()}"

demo = gr.Interface(
    fn=run,
    inputs=[
        gr.Textbox(label="Content", placeholder="Paste page content...", lines=5),
        gr.Number(label="Days since last modified", value=0),
    ],
    outputs=gr.Textbox(label="Scores"),
    title="Content-Freshness-Scorer",
    description="Relevance and freshness. Model: syeedalireza/content-freshness-scorer",
)
if __name__ == "__main__":
    demo.launch()