"""Veridian.ai / LQIS — minimal inference example.

    pip install -r requirements.txt
    python inference.py

Loads the trained pipeline (weights in models/) and scores a sample negotiation,
printing the loss probability, the turn where the deal tipped, and coaching.
"""
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent))

from src.inference.live_scorer import LiveScorer  # noqa: E402


def main():
    scorer = LiveScorer.build()  # loads models/ (encoder + temporal + FiLM + calibration)
    dialogue = [
        {"speaker": "buyer", "text": "Is the charger still available?"},
        {"speaker": "seller", "text": "Yes, asking $10."},
        {"speaker": "buyer", "text": "That is way too expensive, $4 is my max."},
        {"speaker": "seller", "text": "I could maybe do $8."},
        {"speaker": "buyer", "text": "No. $4 or I am done, not paying more."},
    ]
    out = scorer.score(dialogue, fetch_external=False)
    print(f"trained model       : {not out['untrained']}")
    print(f"loss_probability    : {out['loss_probability']:.3f}")
    print(f"deal tipped at turn : {out['tipping_turn']}")
    print("coaching            :")
    for c in out["coaching"]:
        print(f"   - {c['message']}")


if __name__ == "__main__":
    main()