Remove dead code: execute_level, render_dog_from_cat, duplicate file_hash

- Remove execute_level() from tasks/execute.py (defined but never called)
- Remove render_dog_from_cat() from legacy_tasks.py (test convenience, never used)
- Remove duplicate file_hash() from legacy_tasks.py, import from cache_manager
- Remove unused hashlib import

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

legacy_tasks.py

@@ -5,7 +5,6 @@ Distributed rendering tasks for the Art DAG system.
 Supports both single-effect runs and multi-step DAG execution.
 """
 
-import hashlib
 import json
 import logging
 import os
@@ -17,6 +16,7 @@ from typing import Dict, List, Optional
 
 from celery import Task
 from celery_app import app
+from cache_manager import file_hash
 
 # Import artdag components
 from artdag import DAG, Node, NodeType
@@ -77,16 +77,6 @@ def _dog_effect(input_path: Path, output_path: Path, config: dict) -> Path:
     return dog_process([input_path], output_path, config, None)
 
 
-def file_hash(path: Path) -> str:
-    """Compute SHA3-256 hash of a file."""
-    hasher = hashlib.sha3_256()
-    actual_path = path.resolve() if path.is_symlink() else path
-    with open(actual_path, "rb") as f:
-        for chunk in iter(lambda: f.read(65536), b""):
-            hasher.update(chunk)
-    return hasher.hexdigest()
-
-
 # Cache directory (shared between server and worker)
 CACHE_DIR = Path(os.environ.get("CACHE_DIR", str(Path.home() / ".artdag" / "cache")))
 
@@ -281,13 +271,6 @@ def render_effect(self, input_hash: str, effect_name: str, output_name: str) ->
     return provenance
 
 
-@app.task
-def render_dog_from_cat() -> dict:
-    """Convenience task: render cat through dog effect."""
-    CAT_HASH = "33268b6e167deaf018cc538de12dbe562612b33e89a749391cef855b320a269b"
-    return render_effect.delay(CAT_HASH, "dog", "dog-from-cat-celery").get()
-
-
 @app.task(base=RenderTask, bind=True)
 def execute_dag(self, dag_json: str, run_id: str = None) -> dict:
     """

tasks/execute.py

@@ -379,51 +379,3 @@ def execute_step(
         }
 
 
-@app.task(bind=True, name='tasks.execute_level')
-def execute_level(
-    self,
-    steps_json: List[str],
-    plan_id: str,
-    cache_ids: Dict[str, str],
-) -> dict:
-    """
-    Execute all steps at a given dependency level.
-
-    Steps at the same level can run in parallel.
-
-    Args:
-        steps_json: List of JSON-serialized ExecutionSteps
-        plan_id: ID of the parent execution plan
-        cache_ids: Mapping from step_id to cache_id
-
-    Returns:
-        Dict with results for all steps
-    """
-    from celery import group
-
-    # Dispatch all steps in parallel
-    tasks = [
-        execute_step.s(step_json, plan_id, cache_ids)
-        for step_json in steps_json
-    ]
-
-    # Execute in parallel and collect results
-    job = group(tasks)
-    results = job.apply_async()
-
-    # Wait for completion
-    step_results = results.get(timeout=3600)  # 1 hour timeout
-
-    # Build cache_ids from results
-    new_cache_ids = dict(cache_ids)
-    for result in step_results:
-        step_id = result.get("step_id")
-        cache_id = result.get("cache_id")
-        if step_id and cache_id:
-            new_cache_ids[step_id] = cache_id
-
-    return {
-        "status": "completed",
-        "results": step_results,
-        "cache_ids": new_cache_ids,
-    }