import uuid
from django.db import models
from django.conf import settings


class SplatJob(models.Model):
    class Status(models.TextChoices):
        QUEUED = "queued", "Queued"
        RUNNING = "running", "Running"
        SUCCEEDED = "succeeded", "Succeeded"
        FAILED = "failed", "Failed"

    class Step(models.TextChoices):
        EXTRACTING_FRAMES = "extracting_frames", "Extracting frames"
        RUNNING_COLMAP = "running_colmap", "Running COLMAP"
        TRAINING_GSPLAT = "training_gsplat", "Training gsplat"
        EXPORTING = "exporting", "Exporting .ksplat"
        QUALITY_CHECK = "quality_check", "Quality check"

    id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
    splat = models.OneToOneField("splats.Splat", on_delete=models.CASCADE, related_name="job")
    submitted_by = models.ForeignKey(
        settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True, related_name="splat_jobs"
    )

    status = models.CharField(max_length=20, choices=Status.choices, default=Status.QUEUED, db_index=True)
    current_step = models.CharField(max_length=30, choices=Step.choices, blank=True, default="")

    # 0–100 overall progress, updated by the RunPod webhook on each step transition
    progress = models.PositiveSmallIntegerField(default=0)

    # RunPod serverless job ID — used to poll status and match incoming webhooks
    runpod_job_id = models.CharField(max_length=255, blank=True, default="", db_index=True)
    celery_task_id = models.CharField(max_length=255, blank=True, default="")

    # Number of times this job has been requeued after a transient failure
    retry_count = models.PositiveSmallIntegerField(default=0)

    error_message = models.TextField(blank=True, default="")

    # Structured log output from each pipeline step, keyed by Step value.
    # Populated by the RunPod webhook on each step completion.
    # Example: {"extracting_frames": {"frames": 1350, "duration_s": 8.2}, ...}
    pipeline_logs = models.JSONField(default=dict, blank=True)

    # COLMAP sparse reconstruction quality signal.
    # Low point count (< ~500) usually means the splat will be poor quality.
    colmap_points = models.PositiveIntegerField(null=True, blank=True)

    queued_at = models.DateTimeField(auto_now_add=True)
    started_at = models.DateTimeField(null=True, blank=True)
    finished_at = models.DateTimeField(null=True, blank=True)

    def __str__(self):
        return f"Job {self.id} for splat {self.splat_id} [{self.status}]"

    class Meta:
        db_table = "splat_jobs"
        indexes = [
            models.Index(fields=["status", "queued_at"]),
        ]