from django.conf import settings
from django.core.validators import MinValueValidator, MaxValueValidator
from django.db import models

from pgvector.django import HnswIndex, VectorField


class Skill(models.Model):
    DIFFICULTY_CHOICES = [
        ('BEGINNER', 'Beginner'),
        ('INTERMEDIATE', 'Intermediate'),
        ('ADVANCED', 'Advanced'),
    ]

    skill_name = models.CharField(max_length=255, unique=True)
    category = models.CharField(max_length=100)
    description = models.TextField(blank=True)
    difficulty_level = models.CharField(
        max_length=20,
        choices=DIFFICULTY_CHOICES,
        default='BEGINNER'
    )

    def __str__(self):
        return self.skill_name


class UserSkill(models.Model):
    DIFFICULTY_CHOICES = [
        ('BEGINNER', 'Beginner'),
        ('INTERMEDIATE', 'Intermediate'),
        ('ADVANCED', 'Advanced'),
    ]

    user = models.ForeignKey(
        settings.AUTH_USER_MODEL,
        on_delete=models.CASCADE,
        related_name='user_skills'
    )
    skill = models.ForeignKey(
        Skill,
        on_delete=models.CASCADE,
        related_name='user_skills'
    )
    proficiency = models.IntegerField(
        validators=[MinValueValidator(0), MaxValueValidator(100)]
    )
    user_level = models.CharField(max_length=20, choices=DIFFICULTY_CHOICES)
    updated_at = models.DateTimeField(auto_now=True)
    # Cursor used by the upgrade-suggestion flow. Advanced on BOTH dismiss AND
    # apply (see apps.progress.services). A suggestion is eligible when the
    # latest related checkpoint completion is newer than this timestamp.
    # Name is historical — kept to avoid a field-rename migration.
    dismissed_upgrade_at = models.DateTimeField(
        null=True,
        blank=True,
        help_text=(
            "Last-action cursor for the upgrade-suggestion flow. Set on both "
            "dismiss and apply (see apps.progress.services.apply_upgrade and "
            "dismiss_upgrade). A new upgrade is eligible when any related "
            "resource completion is newer than this timestamp."
        ),
    )

    class Meta:
        unique_together = [['user', 'skill']]

    def __str__(self):
        return f'{self.user.email} - {self.skill.skill_name}'


class SkillEmbedding(models.Model):
    """SBERT embedding of a Skill, queried by Module 8 Layer 4 (SBERT canonical
    mapping) when higher-layer NER models fail to resolve a candidate span.

    384 dims matches all-MiniLM-L6-v2; swapping models means re-running
    scripts/build_skill_embeddings.py against a fresh catalog.
    """
    skill = models.OneToOneField(
        Skill,
        on_delete=models.CASCADE,
        related_name='embedding',
    )
    embedding = VectorField(dimensions=384)
    source_text = models.TextField()
    model_name = models.CharField(max_length=100, default='all-MiniLM-L6-v2')
    updated_at = models.DateTimeField(auto_now=True)

    class Meta:
        indexes = [
            HnswIndex(
                name='skillemb_hnsw_idx',
                fields=['embedding'],
                m=16,
                ef_construction=64,
                opclasses=['vector_cosine_ops'],
            ),
        ]

    def __str__(self):
        return f'Embedding({self.skill.skill_name})'