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"""
Video generation service for managing video creation jobs and operations.
This service handles the business logic for video generation requests,
job queue management, progress tracking, and integration with the
multi-agent video generation pipeline.
"""
import logging
import uuid
from datetime import datetime, timedelta
from typing import Optional, Dict, Any, List
from redis.asyncio import Redis
from ..models.job import (
Job, JobCreateRequest, JobStatus, JobType, JobProgress,
JobConfiguration, JobError, JobMetrics, BatchJobCreateRequest
)
from ..models.video import VideoMetadata, VideoStatus
from ..core.redis import RedisKeyManager, redis_json_get, redis_json_set
logger = logging.getLogger(__name__)
class VideoService:
"""
Service class for video generation operations.
Handles job creation, status management, progress tracking,
and integration with the video generation pipeline.
"""
def __init__(self, redis_client: Redis):
self.redis_client = redis_client
async def create_video_job(
self,
request: JobCreateRequest,
user_id: str
) -> Job:
"""
Create a new video generation job.
Args:
request: Job creation request with configuration
user_id: ID of the user creating the job
Returns:
Created Job instance
Raises:
Exception: If job creation fails
"""
try:
# Generate unique job ID
job_id = str(uuid.uuid4())
# Calculate estimated completion time based on quality and complexity
estimated_completion = self._calculate_estimated_completion(
request.configuration
)
# Create job progress with initial values
progress = JobProgress(
percentage=0.0,
current_stage="queued",
estimated_completion=estimated_completion
)
# Create job instance
job = Job(
id=job_id,
user_id=user_id,
job_type=JobType.VIDEO_GENERATION,
priority=request.priority,
configuration=request.configuration,
status=JobStatus.QUEUED,
progress=progress,
created_at=datetime.utcnow(),
updated_at=datetime.utcnow()
)
# Store job in Redis
await self._store_job_in_redis(job)
# Add job to processing queue
await self._enqueue_job_for_processing(job_id, request.priority.value)
logger.info(
"Created video generation job",
job_id=job_id,
user_id=user_id,
topic=request.configuration.topic,
quality=request.configuration.quality,
estimated_completion=estimated_completion
)
return job
except Exception as e:
logger.error(
"Failed to create video generation job",
user_id=user_id,
error=str(e),
exc_info=True
)
raise
async def create_batch_jobs(
self,
request: BatchJobCreateRequest,
user_id: str
) -> List[Job]:
"""
Create multiple video generation jobs as a batch.
Args:
request: Batch job creation request
user_id: ID of the user creating the jobs
Returns:
List of created Job instances
Raises:
Exception: If batch job creation fails
"""
try:
batch_id = str(uuid.uuid4())
jobs = []
for job_config in request.jobs:
# Create individual job request
job_request = JobCreateRequest(
configuration=job_config,
priority=request.batch_priority
)
# Create job
job = await self.create_video_job(job_request, user_id)
# Set batch information
job.batch_id = batch_id
job.job_type = JobType.BATCH_VIDEO_GENERATION
jobs.append(job)
logger.info(
"Created batch video generation jobs",
batch_id=batch_id,
job_count=len(jobs),
user_id=user_id
)
return jobs
except Exception as e:
logger.error(
"Failed to create batch video generation jobs",
user_id=user_id,
error=str(e),
exc_info=True
)
raise
async def get_job_status(self, job_id: str) -> Optional[Job]:
"""
Get current job status and information.
Args:
job_id: Unique job identifier
Returns:
Job instance or None if not found
"""
try:
job_key = RedisKeyManager.job_key(job_id)
job_data = await redis_json_get(self.redis_client, job_key)
if not job_data:
return None
return Job(**job_data)
except Exception as e:
logger.error(
"Failed to get job status",
job_id=job_id,
error=str(e),
exc_info=True
)
return None
async def update_job_status(
self,
job_id: str,
status: JobStatus,
progress_percentage: Optional[float] = None,
current_stage: Optional[str] = None,
error_info: Optional[Dict[str, Any]] = None,
metrics: Optional[Dict[str, Any]] = None
) -> bool:
"""
Update job status and progress information.
Args:
job_id: Unique job identifier
status: New job status
progress_percentage: Progress percentage (0-100)
current_stage: Current processing stage
error_info: Error information if job failed
metrics: Performance metrics
Returns:
True if update successful, False otherwise
"""
try:
# Get current job
job = await self.get_job_status(job_id)
if not job:
logger.warning(f"Job {job_id} not found for status update")
return False
# Update job status
job.status = status
job.updated_at = datetime.utcnow()
# Update progress if provided
if progress_percentage is not None:
job.progress.percentage = progress_percentage
if current_stage:
job.progress.current_stage = current_stage
if current_stage not in job.progress.stages_completed:
job.progress.stages_completed.append(current_stage)
# Handle completion
if status in [JobStatus.COMPLETED, JobStatus.FAILED, JobStatus.CANCELLED]:
job.completed_at = datetime.utcnow()
if status == JobStatus.COMPLETED:
job.progress.percentage = 100.0
# Handle processing start
if status == JobStatus.PROCESSING and not job.started_at:
job.started_at = datetime.utcnow()
# Update error information
if error_info and status == JobStatus.FAILED:
job.error = JobError(
error_code=error_info.get("error_code", "UNKNOWN_ERROR"),
error_message=error_info.get("error_message", "Unknown error occurred"),
error_details=error_info.get("error_details"),
stack_trace=error_info.get("stack_trace")
)
# Update metrics
if metrics:
if not job.metrics:
job.metrics = JobMetrics()
for key, value in metrics.items():
if hasattr(job.metrics, key):
setattr(job.metrics, key, value)
# Save updated job to Redis
job_key = RedisKeyManager.job_key(job_id)
await redis_json_set(self.redis_client, job_key, job.dict())
# Update status cache
status_key = RedisKeyManager.job_status_key(job_id)
await self.redis_client.set(status_key, status.value, ex=300)
logger.info(
"Updated job status",
job_id=job_id,
status=status,
progress=job.progress.percentage,
stage=current_stage
)
return True
except Exception as e:
logger.error(
"Failed to update job status",
job_id=job_id,
status=status,
error=str(e),
exc_info=True
)
return False
async def cancel_job(self, job_id: str) -> bool:
"""
Cancel a video generation job.
Args:
job_id: Unique job identifier
Returns:
True if cancellation successful, False otherwise
"""
try:
job = await self.get_job_status(job_id)
if not job:
return False
# Check if job can be cancelled
if not job.can_be_cancelled:
logger.warning(
f"Job {job_id} cannot be cancelled. Current status: {job.status}"
)
return False
# Update job status to cancelled
success = await self.update_job_status(
job_id=job_id,
status=JobStatus.CANCELLED,
current_stage="cancelled"
)
if success:
# Remove from processing queue if still queued
await self.redis_client.lrem(RedisKeyManager.JOB_QUEUE, 0, job_id)
logger.info(f"Job {job_id} cancelled successfully")
return success
except Exception as e:
logger.error(
"Failed to cancel job",
job_id=job_id,
error=str(e),
exc_info=True
)
return False
async def create_video_metadata(
self,
job_id: str,
filename: str,
file_path: str,
file_size: int,
duration_seconds: Optional[float] = None,
width: Optional[int] = None,
height: Optional[int] = None,
format: str = "mp4"
) -> VideoMetadata:
"""
Create video metadata for a completed job.
Args:
job_id: Associated job ID
filename: Video filename
file_path: Path to video file
file_size: File size in bytes
duration_seconds: Video duration
width: Video width in pixels
height: Video height in pixels
format: Video format
Returns:
VideoMetadata instance
"""
try:
# Get job to extract user_id
job = await self.get_job_status(job_id)
if not job:
raise ValueError(f"Job {job_id} not found")
# Create video metadata
video_metadata = VideoMetadata(
id=str(uuid.uuid4()),
job_id=job_id,
user_id=job.user_id,
filename=filename,
file_path=file_path,
file_size=file_size,
duration_seconds=duration_seconds,
width=width,
height=height,
format=format,
status=VideoStatus.READY,
created_at=datetime.utcnow(),
processed_at=datetime.utcnow()
)
# Store video metadata in Redis
video_key = RedisKeyManager.video_key(f"job_{job_id}")
await redis_json_set(self.redis_client, video_key, video_metadata.dict())
logger.info(
"Created video metadata",
job_id=job_id,
video_id=video_metadata.id,
filename=filename,
file_size=file_size
)
return video_metadata
except Exception as e:
logger.error(
"Failed to create video metadata",
job_id=job_id,
filename=filename,
error=str(e),
exc_info=True
)
raise
def _calculate_estimated_completion(
self,
configuration: JobConfiguration
) -> datetime:
"""
Calculate estimated completion time based on job configuration.
Args:
configuration: Job configuration
Returns:
Estimated completion datetime
"""
# Base processing time in minutes
base_time = 5
# Adjust based on quality
quality_multipliers = {
"low": 0.5,
"medium": 1.0,
"high": 1.5,
"ultra": 2.0
}
quality_multiplier = quality_multipliers.get(
configuration.quality.value, 1.0
)
# Adjust based on content complexity (rough estimate)
content_length = len(configuration.topic) + len(configuration.context)
complexity_multiplier = 1.0 + (content_length / 1000) # +1 minute per 1000 chars
# Adjust for RAG usage
rag_multiplier = 1.3 if configuration.use_rag else 1.0
# Calculate total estimated time
estimated_minutes = base_time * quality_multiplier * complexity_multiplier * rag_multiplier
return datetime.utcnow() + timedelta(minutes=estimated_minutes)
async def get_queue_length(self) -> int:
"""
Get current job queue length.
Returns:
Number of jobs in queue
"""
try:
return await self.redis_client.llen(RedisKeyManager.JOB_QUEUE)
except Exception as e:
logger.error(f"Failed to get queue length: {e}")
return 0
async def get_user_jobs(
self,
user_id: str,
limit: int = 10,
offset: int = 0
) -> List[Job]:
"""
Get jobs for a specific user.
Args:
user_id: User ID
limit: Maximum number of jobs to return
offset: Number of jobs to skip
Returns:
List of Job instances
"""
try:
# Get user's job IDs
user_jobs_key = RedisKeyManager.user_jobs_key(user_id)
job_ids = await self.redis_client.smembers(user_jobs_key)
if not job_ids:
return []
# Get job data for each ID
jobs = []
for job_id in list(job_ids)[offset:offset + limit]:
job = await self.get_job_status(job_id)
if job:
jobs.append(job)
# Sort by creation date (newest first)
jobs.sort(key=lambda x: x.created_at, reverse=True)
return jobs
except Exception as e:
logger.error(
"Failed to get user jobs",
user_id=user_id,
error=str(e),
exc_info=True
)
return []
async def _store_job_in_redis(self, job: Job) -> None:
"""
Store job data in Redis with proper indexing.
Args:
job: Job instance to store
"""
# Store job data
job_key = RedisKeyManager.job_key(job.id)
await redis_json_set(self.redis_client, job_key, job.dict())
# Add to user's job index
user_jobs_key = RedisKeyManager.user_jobs_key(job.user_id)
await self.redis_client.sadd(user_jobs_key, job.id)
# Set status cache
status_key = RedisKeyManager.job_status_key(job.id)
await self.redis_client.set(status_key, job.status.value, ex=300)
async def _enqueue_job_for_processing(self, job_id: str, priority: str) -> None:
"""
Add job to the processing queue based on priority.
Args:
job_id: Job ID to enqueue
priority: Job priority level
"""
if priority == "urgent":
# Add to front of queue for urgent jobs
await self.redis_client.lpush(RedisKeyManager.JOB_QUEUE, job_id)
elif priority == "high":
# Add near front for high priority jobs
queue_length = await self.redis_client.llen(RedisKeyManager.JOB_QUEUE)
insert_position = min(queue_length // 4, 10)
if insert_position == 0:
await self.redis_client.lpush(RedisKeyManager.JOB_QUEUE, job_id)
else:
await self.redis_client.rpush(RedisKeyManager.JOB_QUEUE, job_id)
else:
# Normal and low priority jobs go to the end
await self.redis_client.rpush(RedisKeyManager.JOB_QUEUE, job_id)
async def process_job_queue(self, max_concurrent_jobs: int = 5) -> None:
"""
Process jobs from the queue with concurrency control.
Args:
max_concurrent_jobs: Maximum number of concurrent jobs to process
"""
try:
# Check current processing jobs
processing_key = "queue:processing"
current_processing = await self.redis_client.scard(processing_key)
if current_processing >= max_concurrent_jobs:
logger.info(f"Max concurrent jobs ({max_concurrent_jobs}) reached")
return
# Get next job from queue
result = await self.redis_client.blpop(RedisKeyManager.JOB_QUEUE, timeout=1)
if result:
queue_name, job_id = result
# Mark job as processing
await self.redis_client.sadd(processing_key, job_id)
# Update job status
await self.update_job_status(
job_id=job_id,
status=JobStatus.PROCESSING,
current_stage="initializing"
)
# Trigger video generation pipeline
await self._trigger_video_generation_pipeline(job_id)
logger.info(f"Started processing job {job_id}")
except Exception as e:
logger.error(f"Failed to process job queue: {e}", exc_info=True)
async def _trigger_video_generation_pipeline(self, job_id: str) -> None:
"""
Trigger the multi-agent video generation pipeline for a job.
Args:
job_id: Job ID to process
"""
try:
# Get job details
job = await self.get_job_status(job_id)
if not job:
logger.error(f"Job {job_id} not found for pipeline trigger")
return
# Create pipeline message
pipeline_message = {
"job_id": job_id,
"user_id": job.user_id,
"configuration": job.configuration.dict(),
"priority": job.priority.value,
"created_at": job.created_at.isoformat()
}
# Send to pipeline queue (this would integrate with your existing pipeline)
pipeline_queue_key = "pipeline:video_generation"
await redis_json_set(
self.redis_client,
f"{pipeline_queue_key}:{job_id}",
pipeline_message,
ex=3600 # Expire after 1 hour if not processed
)
# Notify pipeline workers (using pub/sub)
await self.redis_client.publish("pipeline:new_job", job_id)
logger.info(f"Triggered video generation pipeline for job {job_id}")
except Exception as e:
logger.error(f"Failed to trigger pipeline for job {job_id}: {e}", exc_info=True)
# Mark job as failed
await self.update_job_status(
job_id=job_id,
status=JobStatus.FAILED,
error_info={
"error_code": "PIPELINE_TRIGGER_FAILED",
"error_message": f"Failed to trigger video generation pipeline: {str(e)}"
}
) |