65a8170192
- Display recipe's original S-expression when available (code is data) - Fall back to generating S-expression from plan for legacy JSON - Run service now prefers .sexp plan files over .json - Add get_run_plan_sexp() for direct S-expression access Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
705 lines
27 KiB
Python
705 lines
27 KiB
Python
"""
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Run Service - business logic for run management.
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Runs are content-addressed (run_id computed from inputs + recipe).
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Completed runs are stored in PostgreSQL, not Redis.
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In-progress runs are tracked via Celery task state.
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"""
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import hashlib
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import json
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, List, Dict, Any, Tuple
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def compute_run_id(input_hashes: list, recipe: str, recipe_hash: str = None) -> str:
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"""
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Compute a deterministic run_id from inputs and recipe.
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The run_id is a SHA3-256 hash of:
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- Sorted input content hashes
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- Recipe identifier (recipe_hash if provided, else "effect:{recipe}")
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This makes runs content-addressable: same inputs + recipe = same run_id.
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"""
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# Handle both list and dict inputs
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if isinstance(input_hashes, dict):
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sorted_inputs = sorted(input_hashes.values())
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else:
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sorted_inputs = sorted(input_hashes)
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data = {
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"inputs": sorted_inputs,
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"recipe": recipe_hash or f"effect:{recipe}",
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"version": "1",
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}
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json_str = json.dumps(data, sort_keys=True, separators=(",", ":"))
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return hashlib.sha3_256(json_str.encode()).hexdigest()
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def detect_media_type(cache_path: Path) -> str:
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"""Detect if file is image, video, or audio based on magic bytes."""
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try:
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with open(cache_path, "rb") as f:
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header = f.read(32)
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except Exception:
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return "unknown"
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# Video signatures
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if header[:4] == b'\x1a\x45\xdf\xa3': # WebM/MKV
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return "video"
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if len(header) > 8 and header[4:8] == b'ftyp': # MP4/MOV/M4A
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# Check for audio-only M4A
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if len(header) > 11 and header[8:12] in (b'M4A ', b'm4a '):
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return "audio"
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return "video"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'AVI ': # AVI
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return "video"
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# Image signatures
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if header[:8] == b'\x89PNG\r\n\x1a\n': # PNG
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return "image"
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if header[:2] == b'\xff\xd8': # JPEG
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return "image"
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if header[:6] in (b'GIF87a', b'GIF89a'): # GIF
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return "image"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'WEBP': # WebP
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return "image"
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# Audio signatures
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if header[:3] == b'ID3' or header[:2] == b'\xff\xfb': # MP3
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return "audio"
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if header[:4] == b'fLaC': # FLAC
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return "audio"
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if header[:4] == b'OggS': # Ogg (could be audio or video, assume audio)
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return "audio"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'WAVE': # WAV
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return "audio"
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return "unknown"
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class RunService:
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"""
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Service for managing recipe runs.
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Uses PostgreSQL for completed runs, Celery for task state.
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Redis is only used for task_id mapping (ephemeral).
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"""
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def __init__(self, database, redis, cache):
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self.db = database
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self.redis = redis # Only for task_id mapping
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self.cache = cache
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self.task_key_prefix = "artdag:task:" # run_id -> task_id mapping only
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self.cache_dir = Path(os.environ.get("CACHE_DIR", "/tmp/artdag-cache"))
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def _ensure_inputs_list(self, inputs) -> list:
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"""Ensure inputs is a list, parsing JSON string if needed."""
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if inputs is None:
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return []
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if isinstance(inputs, list):
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return inputs
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if isinstance(inputs, str):
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try:
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parsed = json.loads(inputs)
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if isinstance(parsed, list):
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return parsed
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except json.JSONDecodeError:
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pass
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return []
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return []
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async def get_run(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get a run by ID. Checks database first, then Celery task state."""
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# Check database for completed run
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cached = await self.db.get_run_cache(run_id)
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if cached:
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": cached.get("recipe"),
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"inputs": self._ensure_inputs_list(cached.get("inputs")),
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"output_hash": cached.get("output_hash"),
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"ipfs_cid": cached.get("ipfs_cid"),
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"provenance_cid": cached.get("provenance_cid"),
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"actor_id": cached.get("actor_id"),
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"created_at": cached.get("created_at"),
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"completed_at": cached.get("created_at"),
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}
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# Check database for pending run
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pending = await self.db.get_pending_run(run_id)
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if pending:
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task_id = pending.get("celery_task_id")
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if task_id:
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# Check actual Celery task state
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from celery.result import AsyncResult
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from celery_app import app as celery_app
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result = AsyncResult(task_id, app=celery_app)
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status = result.status.lower()
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# Normalize status
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status_map = {
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"pending": "pending",
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"started": "running",
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"success": "completed",
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"failure": "failed",
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"retry": "running",
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"revoked": "failed",
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}
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normalized_status = status_map.get(status, status)
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run_data = {
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"run_id": run_id,
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"status": normalized_status,
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"celery_task_id": task_id,
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"actor_id": pending.get("actor_id"),
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"recipe": pending.get("recipe"),
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"inputs": self._ensure_inputs_list(pending.get("inputs")),
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"output_name": pending.get("output_name"),
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"created_at": pending.get("created_at"),
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"error": pending.get("error"),
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}
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# If task completed, get result
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if result.ready():
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if result.successful():
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run_data["status"] = "completed"
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task_result = result.result
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if isinstance(task_result, dict):
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run_data["output_hash"] = task_result.get("output_hash")
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else:
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run_data["status"] = "failed"
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run_data["error"] = str(result.result)
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return run_data
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# No task_id but have pending record - return from DB
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return {
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"run_id": run_id,
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"status": pending.get("status", "pending"),
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"recipe": pending.get("recipe"),
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"inputs": self._ensure_inputs_list(pending.get("inputs")),
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"output_name": pending.get("output_name"),
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"actor_id": pending.get("actor_id"),
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"created_at": pending.get("created_at"),
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"error": pending.get("error"),
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}
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# Fallback: Check Redis for backwards compatibility
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task_data = self.redis.get(f"{self.task_key_prefix}{run_id}")
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if task_data:
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if isinstance(task_data, bytes):
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task_data = task_data.decode()
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# Parse task data (supports both old format string and new JSON format)
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try:
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parsed = json.loads(task_data)
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task_id = parsed.get("task_id")
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task_actor_id = parsed.get("actor_id")
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task_recipe = parsed.get("recipe")
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task_recipe_name = parsed.get("recipe_name")
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task_inputs = parsed.get("inputs")
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# Ensure inputs is a list (might be JSON string)
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if isinstance(task_inputs, str):
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try:
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task_inputs = json.loads(task_inputs)
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except json.JSONDecodeError:
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task_inputs = None
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task_output_name = parsed.get("output_name")
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task_created_at = parsed.get("created_at")
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except json.JSONDecodeError:
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# Old format: just the task_id string
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task_id = task_data
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task_actor_id = None
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task_recipe = None
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task_recipe_name = None
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task_inputs = None
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task_output_name = None
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task_created_at = None
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# Get task state from Celery
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from celery.result import AsyncResult
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from celery_app import app as celery_app
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result = AsyncResult(task_id, app=celery_app)
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status = result.status.lower()
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# Normalize Celery status names
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status_map = {
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"pending": "pending",
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"started": "running",
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"success": "completed",
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"failure": "failed",
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"retry": "running",
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"revoked": "failed",
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}
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normalized_status = status_map.get(status, status)
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run_data = {
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"run_id": run_id,
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"status": normalized_status,
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"celery_task_id": task_id,
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"actor_id": task_actor_id,
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"recipe": task_recipe,
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"recipe_name": task_recipe_name,
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"inputs": self._ensure_inputs_list(task_inputs),
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"output_name": task_output_name,
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"created_at": task_created_at,
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}
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# If task completed, get result
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if result.ready():
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if result.successful():
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run_data["status"] = "completed"
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task_result = result.result
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if isinstance(task_result, dict):
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run_data["output_hash"] = task_result.get("output_hash")
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else:
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run_data["status"] = "failed"
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run_data["error"] = str(result.result)
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return run_data
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return None
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async def list_runs(self, actor_id: str, offset: int = 0, limit: int = 20) -> list:
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"""List runs for a user. Returns completed and pending runs from database."""
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# Get completed runs from database
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completed_runs = await self.db.list_runs_by_actor(actor_id, offset=0, limit=limit + 50)
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# Get pending runs from database
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pending_db = await self.db.list_pending_runs(actor_id=actor_id)
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# Convert pending runs to run format with live status check
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pending = []
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for pr in pending_db:
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run_id = pr.get("run_id")
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# Skip if already in completed
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if any(r.get("run_id") == run_id for r in completed_runs):
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continue
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# Get live status
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run = await self.get_run(run_id)
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if run and run.get("status") in ("pending", "running"):
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pending.append(run)
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# Combine and sort
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all_runs = pending + completed_runs
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all_runs.sort(key=lambda r: r.get("created_at", ""), reverse=True)
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return all_runs[offset:offset + limit]
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async def create_run(
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self,
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recipe: str,
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inputs: list,
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output_name: str = None,
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use_dag: bool = True,
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dag_json: str = None,
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actor_id: str = None,
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l2_server: str = None,
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recipe_name: str = None,
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) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
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"""
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Create a new rendering run. Checks cache before executing.
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Returns (run_dict, error_message).
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"""
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import httpx
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try:
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from legacy_tasks import render_effect, execute_dag, build_effect_dag
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except ImportError as e:
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return None, f"Celery tasks not available: {e}"
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# Handle both list and dict inputs
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if isinstance(inputs, dict):
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input_list = list(inputs.values())
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else:
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input_list = inputs
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# Compute content-addressable run_id
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run_id = compute_run_id(input_list, recipe)
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# Generate output name if not provided
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if not output_name:
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output_name = f"{recipe}-{run_id[:8]}"
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# Check database cache first (completed runs)
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cached_run = await self.db.get_run_cache(run_id)
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if cached_run:
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output_hash = cached_run.get("output_hash")
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if output_hash and self.cache.has_content(output_hash):
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": recipe,
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"inputs": input_list,
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"output_name": output_name,
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"output_hash": output_hash,
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"ipfs_cid": cached_run.get("ipfs_cid"),
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"provenance_cid": cached_run.get("provenance_cid"),
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"created_at": cached_run.get("created_at"),
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"completed_at": cached_run.get("created_at"),
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"actor_id": actor_id,
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}, None
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# Check L2 if not in local cache
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if l2_server:
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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l2_resp = await client.get(f"{l2_server}/assets/by-run-id/{run_id}")
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if l2_resp.status_code == 200:
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l2_data = l2_resp.json()
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output_hash = l2_data.get("output_hash")
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ipfs_cid = l2_data.get("ipfs_cid")
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if output_hash and ipfs_cid:
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# Pull from IPFS to local cache
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try:
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import ipfs_client
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legacy_dir = self.cache_dir / "legacy"
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legacy_dir.mkdir(parents=True, exist_ok=True)
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recovery_path = legacy_dir / output_hash
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if ipfs_client.get_file(ipfs_cid, str(recovery_path)):
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# Save to database cache
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await self.db.save_run_cache(
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run_id=run_id,
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output_hash=output_hash,
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recipe=recipe,
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inputs=input_list,
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ipfs_cid=ipfs_cid,
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provenance_cid=l2_data.get("provenance_cid"),
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actor_id=actor_id,
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)
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": recipe,
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"inputs": input_list,
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"output_hash": output_hash,
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"ipfs_cid": ipfs_cid,
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"provenance_cid": l2_data.get("provenance_cid"),
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"created_at": datetime.now(timezone.utc).isoformat(),
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"actor_id": actor_id,
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}, None
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except Exception:
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pass # IPFS recovery failed, continue to run
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except Exception:
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pass # L2 lookup failed, continue to run
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# Not cached - submit to Celery
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try:
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if use_dag or recipe == "dag":
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if dag_json:
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dag_data = dag_json
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else:
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dag = build_effect_dag(input_list, recipe)
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dag_data = dag.to_json()
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task = execute_dag.delay(dag_data, run_id)
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else:
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if len(input_list) != 1:
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return None, "Legacy mode only supports single-input recipes. Use use_dag=true for multi-input."
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task = render_effect.delay(input_list[0], recipe, output_name)
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# Store pending run in database for durability
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try:
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await self.db.create_pending_run(
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run_id=run_id,
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celery_task_id=task.id,
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recipe=recipe,
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inputs=input_list,
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actor_id=actor_id,
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dag_json=dag_json,
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output_name=output_name,
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)
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except Exception as e:
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import logging
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logging.getLogger(__name__).error(f"Failed to save pending run: {e}")
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# Also store in Redis for backwards compatibility (shorter TTL)
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task_data = json.dumps({
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"task_id": task.id,
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"actor_id": actor_id,
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"recipe": recipe,
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"recipe_name": recipe_name,
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"inputs": input_list,
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"output_name": output_name,
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"created_at": datetime.now(timezone.utc).isoformat(),
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})
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self.redis.setex(
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f"{self.task_key_prefix}{run_id}",
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3600 * 4, # 4 hour TTL (database is primary now)
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task_data
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)
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return {
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"run_id": run_id,
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"status": "running",
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"recipe": recipe,
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"recipe_name": recipe_name,
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"inputs": input_list,
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"output_name": output_name,
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"celery_task_id": task.id,
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"created_at": datetime.now(timezone.utc).isoformat(),
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"actor_id": actor_id,
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}, None
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except Exception as e:
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return None, f"Failed to submit task: {e}"
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async def discard_run(
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self,
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run_id: str,
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actor_id: str,
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username: str,
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) -> Tuple[bool, Optional[str]]:
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"""
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Discard (delete) a run record.
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Note: This removes the run record but not the output content.
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"""
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run = await self.get_run(run_id)
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if not run:
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return False, f"Run {run_id} not found"
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# Check ownership
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run_owner = run.get("actor_id")
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if run_owner and run_owner not in (username, actor_id):
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return False, "Access denied"
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# Remove task_id mapping from Redis
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self.redis.delete(f"{self.task_key_prefix}{run_id}")
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# Note: We don't delete from run_cache as that's a permanent record
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# of completed work. The content itself remains in cache.
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return True, None
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async def get_run_plan(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get execution plan for a run."""
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# Prefer S-expression plan
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sexp_path = self.cache_dir / "plans" / f"{run_id}.sexp"
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if sexp_path.exists():
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with open(sexp_path) as f:
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return {"sexp": f.read(), "format": "sexp"}
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# Fall back to JSON for legacy plans
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json_path = self.cache_dir / "plans" / f"{run_id}.json"
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if json_path.exists():
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with open(json_path) as f:
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plan = json.load(f)
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plan["format"] = "json"
|
|
return plan
|
|
|
|
return None
|
|
|
|
async def get_run_plan_sexp(self, run_id: str) -> Optional[str]:
|
|
"""Get execution plan as S-expression string."""
|
|
sexp_path = self.cache_dir / "plans" / f"{run_id}.sexp"
|
|
if sexp_path.exists():
|
|
with open(sexp_path) as f:
|
|
return f.read()
|
|
return None
|
|
|
|
async def get_run_artifacts(self, run_id: str) -> List[Dict[str, Any]]:
|
|
"""Get all artifacts (inputs + outputs) for a run."""
|
|
run = await self.get_run(run_id)
|
|
if not run:
|
|
return []
|
|
|
|
artifacts = []
|
|
|
|
def get_artifact_info(content_hash: str, role: str, name: str) -> Optional[Dict]:
|
|
if self.cache.has_content(content_hash):
|
|
path = self.cache.get_by_content_hash(content_hash)
|
|
if path and path.exists():
|
|
return {
|
|
"hash": content_hash,
|
|
"size_bytes": path.stat().st_size,
|
|
"media_type": detect_media_type(path),
|
|
"role": role,
|
|
"step_name": name,
|
|
}
|
|
return None
|
|
|
|
# Add inputs
|
|
inputs = run.get("inputs", [])
|
|
if isinstance(inputs, dict):
|
|
inputs = list(inputs.values())
|
|
for i, h in enumerate(inputs):
|
|
info = get_artifact_info(h, "input", f"Input {i + 1}")
|
|
if info:
|
|
artifacts.append(info)
|
|
|
|
# Add output
|
|
if run.get("output_hash"):
|
|
info = get_artifact_info(run["output_hash"], "output", "Output")
|
|
if info:
|
|
artifacts.append(info)
|
|
|
|
return artifacts
|
|
|
|
async def get_run_analysis(self, run_id: str) -> List[Dict[str, Any]]:
|
|
"""Get analysis data for each input in a run."""
|
|
run = await self.get_run(run_id)
|
|
if not run:
|
|
return []
|
|
|
|
analysis_dir = self.cache_dir / "analysis"
|
|
results = []
|
|
|
|
inputs = run.get("inputs", [])
|
|
if isinstance(inputs, dict):
|
|
inputs = list(inputs.values())
|
|
|
|
for i, input_hash in enumerate(inputs):
|
|
analysis_path = analysis_dir / f"{input_hash}.json"
|
|
analysis_data = None
|
|
|
|
if analysis_path.exists():
|
|
try:
|
|
with open(analysis_path) as f:
|
|
analysis_data = json.load(f)
|
|
except (json.JSONDecodeError, IOError):
|
|
pass
|
|
|
|
results.append({
|
|
"input_hash": input_hash,
|
|
"input_name": f"Input {i + 1}",
|
|
"has_analysis": analysis_data is not None,
|
|
"tempo": analysis_data.get("tempo") if analysis_data else None,
|
|
"beat_times": analysis_data.get("beat_times", []) if analysis_data else [],
|
|
"raw": analysis_data,
|
|
})
|
|
|
|
return results
|
|
|
|
def detect_media_type(self, path: Path) -> str:
|
|
"""Detect media type for a file path."""
|
|
return detect_media_type(path)
|
|
|
|
async def recover_pending_runs(self) -> Dict[str, int]:
|
|
"""
|
|
Recover pending runs after restart.
|
|
|
|
Checks all pending runs in the database and:
|
|
- Updates status for completed tasks
|
|
- Re-queues orphaned tasks that can be retried
|
|
- Marks as failed if unrecoverable
|
|
|
|
Returns counts of recovered, completed, failed runs.
|
|
"""
|
|
from celery.result import AsyncResult
|
|
from celery_app import app as celery_app
|
|
|
|
try:
|
|
from legacy_tasks import execute_dag
|
|
except ImportError:
|
|
return {"error": "Celery tasks not available"}
|
|
|
|
stats = {"recovered": 0, "completed": 0, "failed": 0, "still_running": 0}
|
|
|
|
# Get all pending/running runs from database
|
|
pending_runs = await self.db.list_pending_runs()
|
|
|
|
for run in pending_runs:
|
|
run_id = run.get("run_id")
|
|
task_id = run.get("celery_task_id")
|
|
status = run.get("status")
|
|
|
|
if not task_id:
|
|
# No task ID - try to re-queue if we have dag_json
|
|
dag_json = run.get("dag_json")
|
|
if dag_json:
|
|
try:
|
|
new_task = execute_dag.delay(dag_json, run_id)
|
|
await self.db.create_pending_run(
|
|
run_id=run_id,
|
|
celery_task_id=new_task.id,
|
|
recipe=run.get("recipe", "unknown"),
|
|
inputs=run.get("inputs", []),
|
|
actor_id=run.get("actor_id"),
|
|
dag_json=dag_json,
|
|
output_name=run.get("output_name"),
|
|
)
|
|
stats["recovered"] += 1
|
|
except Exception as e:
|
|
await self.db.update_pending_run_status(
|
|
run_id, "failed", f"Recovery failed: {e}"
|
|
)
|
|
stats["failed"] += 1
|
|
else:
|
|
await self.db.update_pending_run_status(
|
|
run_id, "failed", "No DAG data for recovery"
|
|
)
|
|
stats["failed"] += 1
|
|
continue
|
|
|
|
# Check Celery task state
|
|
result = AsyncResult(task_id, app=celery_app)
|
|
celery_status = result.status.lower()
|
|
|
|
if result.ready():
|
|
if result.successful():
|
|
# Task completed - move to run_cache
|
|
task_result = result.result
|
|
if isinstance(task_result, dict) and task_result.get("output_hash"):
|
|
await self.db.save_run_cache(
|
|
run_id=run_id,
|
|
output_hash=task_result["output_hash"],
|
|
recipe=run.get("recipe", "unknown"),
|
|
inputs=run.get("inputs", []),
|
|
ipfs_cid=task_result.get("ipfs_cid"),
|
|
provenance_cid=task_result.get("provenance_cid"),
|
|
actor_id=run.get("actor_id"),
|
|
)
|
|
await self.db.complete_pending_run(run_id)
|
|
stats["completed"] += 1
|
|
else:
|
|
await self.db.update_pending_run_status(
|
|
run_id, "failed", "Task completed but no output hash"
|
|
)
|
|
stats["failed"] += 1
|
|
else:
|
|
# Task failed
|
|
await self.db.update_pending_run_status(
|
|
run_id, "failed", str(result.result)
|
|
)
|
|
stats["failed"] += 1
|
|
elif celery_status in ("pending", "started", "retry"):
|
|
# Still running
|
|
stats["still_running"] += 1
|
|
else:
|
|
# Unknown state - try to re-queue if we have dag_json
|
|
dag_json = run.get("dag_json")
|
|
if dag_json:
|
|
try:
|
|
new_task = execute_dag.delay(dag_json, run_id)
|
|
await self.db.create_pending_run(
|
|
run_id=run_id,
|
|
celery_task_id=new_task.id,
|
|
recipe=run.get("recipe", "unknown"),
|
|
inputs=run.get("inputs", []),
|
|
actor_id=run.get("actor_id"),
|
|
dag_json=dag_json,
|
|
output_name=run.get("output_name"),
|
|
)
|
|
stats["recovered"] += 1
|
|
except Exception as e:
|
|
await self.db.update_pending_run_status(
|
|
run_id, "failed", f"Recovery failed: {e}"
|
|
)
|
|
stats["failed"] += 1
|
|
else:
|
|
await self.db.update_pending_run_status(
|
|
run_id, "failed", f"Task in unknown state: {celery_status}"
|
|
)
|
|
stats["failed"] += 1
|
|
|
|
return stats
|