app_feedback_callbacks.py

from __future__ import annotations

from app_state import app_state


def train_cue_model_from_feedback():
    """Train lightweight cue and transition-candidate scorers from feedback."""
    from cue_learning import (
        load_training_examples, examples_from_rating_rows, fit_logistic_model, save_model,
        examples_from_candidate_rating_rows, fit_candidate_ranker, save_candidate_model,
    )
    from listening_benchmarks import load_ratings
    manual = load_training_examples("data/manual-cue-edits.jsonl")
    rows = load_ratings()
    rating_examples = examples_from_rating_rows(rows)
    candidate_examples = examples_from_candidate_rating_rows(rows)
    examples = manual + rating_examples
    if not examples and not candidate_examples:
        return "⚠️ No manual cue edits or decisive listening ratings available for training yet."
    path = None
    candidate_path = None
    if examples:
        model = fit_logistic_model(examples)
        path = save_model(model)
    if candidate_examples:
        candidate_model = fit_candidate_ranker(candidate_examples)
        candidate_path = save_candidate_model(candidate_model)
    return (
        "✅ Feedback models trained\n\n"
        f"- Cue examples: {len(examples)}\n"
        f"- Manual cue edits: {len(manual)}\n"
        f"- Rating-derived examples: {len(rating_examples)}\n"
        f"- Candidate examples: {len(candidate_examples)}\n"
        f"- Cue model output: `{path or 'not enough examples'}`\n"
        f"- Candidate ranker output: `{candidate_path or 'not enough examples'}`\n\n"
        "New analyses blend cue probability into cue confidence and candidate acceptance probability into transition ranking."
    )


def save_transition_rating(transition_idx, candidate_rank, rating, accepted, notes):
    """Persist a human listening rating for a transition candidate."""
    if not app_state.transitions:
        return "⚠️ Generate a set plan first"
    idx = int(transition_idx) - 1
    if idx < 0 or idx >= len(app_state.transitions):
        return f"⚠️ Invalid transition index. Choose 1-{len(app_state.transitions)}"
    trans = app_state.transitions[idx]
    track_a = app_state.analyses[trans.track_a_idx]
    track_b = app_state.analyses[trans.track_b_idx]
    from listening_benchmarks import record_transition_rating, summarize_ratings, format_rating_summary
    record_transition_rating(
        transition=trans,
        track_a=track_a,
        track_b=track_b,
        rating=float(rating),
        notes=str(notes or ""),
        candidate_rank=int(candidate_rank or 0),
        accepted=bool(accepted),
    )
    return "✅ Rating saved.\n\n" + format_rating_summary(summarize_ratings())


def show_listening_benchmarks():
    from listening_benchmarks import summarize_ratings, format_rating_summary
    return format_rating_summary(summarize_ratings())