from __future__ import annotations

from django.db import transaction

from .models import ProfileRevision, SearchProfile


@transaction.atomic
def save_profile_revision(profile: SearchProfile, *, reason: str) -> ProfileRevision:
    latest = profile.revisions.order_by("-version").first()
    next_version = (latest.version if latest else 0) + 1
    if profile.version != next_version:
        profile.version = next_version
        profile.save(update_fields=["version", "updated_at"])
    return ProfileRevision.objects.create(
        profile=profile,
        version=next_version,
        snapshot=profile.snapshot(),
        reason=reason,
    )


@transaction.atomic
def apply_feedback_delta(
    profile: SearchProfile,
    feature: str,
    delta: float,
    *,
    min_weight: float = 0.0,
    max_weight: float = 40.0,
) -> SearchProfile:
    if not profile.learning_enabled:
        return profile
    weights = dict(profile.weights)
    current = float(weights.get(feature, 0.0))
    weights[feature] = round(max(min_weight, min(max_weight, current + delta)), 2)
    profile.weights = weights
    profile.save(update_fields=["weights", "updated_at"])
    save_profile_revision(profile, reason=f"feedback_delta:{feature}:{delta:+.2f}")
    return profile