celery/app/routers/api.py

"""
3-phase API routes for L1 server.

Provides the plan/execute/run-recipe endpoints for programmatic access.
"""

import hashlib
import json
import logging
import uuid
from datetime import datetime, timezone
from typing import Dict, List, Optional

import yaml
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel

from artdag_common.middleware.auth import UserContext
from ..dependencies import require_auth, get_redis_client, get_cache_manager

router = APIRouter()
logger = logging.getLogger(__name__)

# Redis key prefix
RUNS_KEY_PREFIX = "artdag:run:"


class PlanRequest(BaseModel):
    recipe_yaml: str
    input_hashes: Dict[str, str]
    features: List[str] = ["beats", "energy"]


class ExecutePlanRequest(BaseModel):
    plan_json: str
    run_id: Optional[str] = None


class RecipeRunRequest(BaseModel):
    recipe_yaml: str
    input_hashes: Dict[str, str]
    features: List[str] = ["beats", "energy"]


def compute_run_id(input_hashes: List[str], recipe: str, recipe_hash: str = None) -> str:
    """Compute deterministic run_id from inputs and recipe."""
    data = {
        "inputs": sorted(input_hashes),
        "recipe": recipe_hash or f"effect:{recipe}",
        "version": "1",
    }
    json_str = json.dumps(data, sort_keys=True, separators=(",", ":"))
    return hashlib.sha3_256(json_str.encode()).hexdigest()


@router.post("/plan")
async def generate_plan_endpoint(
    request: PlanRequest,
    ctx: UserContext = Depends(require_auth),
):
    """
    Generate an execution plan without executing it.

    Phase 1 (Analyze) + Phase 2 (Plan) of the 3-phase model.
    Returns the plan with cache status for each step.
    """
    from tasks.orchestrate import generate_plan

    try:
        task = generate_plan.delay(
            recipe_yaml=request.recipe_yaml,
            input_hashes=request.input_hashes,
            features=request.features,
        )

        # Wait for result (plan generation is usually fast)
        result = task.get(timeout=60)

        return {
            "status": result.get("status"),
            "recipe": result.get("recipe"),
            "plan_id": result.get("plan_id"),
            "total_steps": result.get("total_steps"),
            "cached_steps": result.get("cached_steps"),
            "pending_steps": result.get("pending_steps"),
            "steps": result.get("steps"),
        }
    except Exception as e:
        logger.error(f"Plan generation failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@router.post("/execute")
async def execute_plan_endpoint(
    request: ExecutePlanRequest,
    ctx: UserContext = Depends(require_auth),
):
    """
    Execute a pre-generated execution plan.

    Phase 3 (Execute) of the 3-phase model.
    Submits the plan to Celery for parallel execution.
    """
    from tasks.orchestrate import run_plan

    run_id = request.run_id or str(uuid.uuid4())

    try:
        task = run_plan.delay(
            plan_json=request.plan_json,
            run_id=run_id,
        )

        return {
            "status": "submitted",
            "run_id": run_id,
            "celery_task_id": task.id,
        }
    except Exception as e:
        logger.error(f"Plan execution failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@router.post("/run-recipe")
async def run_recipe_endpoint(
    request: RecipeRunRequest,
    ctx: UserContext = Depends(require_auth),
):
    """
    Run a complete recipe through all 3 phases.

    1. Analyze: Extract features from inputs
    2. Plan: Generate execution plan with cache IDs
    3. Execute: Run steps with parallel execution

    Returns immediately with run_id. Poll /api/run/{run_id} for status.
    """
    from tasks.orchestrate import run_recipe
    import database

    redis = get_redis_client()
    cache = get_cache_manager()

    # Parse recipe name
    try:
        recipe_data = yaml.safe_load(request.recipe_yaml)
        recipe_name = recipe_data.get("name", "unknown")
    except Exception:
        recipe_name = "unknown"

    # Compute deterministic run_id
    run_id = compute_run_id(
        list(request.input_hashes.values()),
        recipe_name,
        hashlib.sha3_256(request.recipe_yaml.encode()).hexdigest()
    )

    # Check if already completed
    cached = await database.get_run_cache(run_id)
    if cached:
        output_hash = cached.get("output_hash")
        if cache.has_content(output_hash):
            return {
                "status": "completed",
                "run_id": run_id,
                "output_hash": output_hash,
                "output_ipfs_cid": cache.get_ipfs_cid(output_hash),
                "cached": True,
            }

    # Submit to Celery
    try:
        task = run_recipe.delay(
            recipe_yaml=request.recipe_yaml,
            input_hashes=request.input_hashes,
            features=request.features,
            run_id=run_id,
        )

        # Store run status in Redis
        run_data = {
            "run_id": run_id,
            "status": "pending",
            "recipe": recipe_name,
            "inputs": list(request.input_hashes.values()),
            "celery_task_id": task.id,
            "created_at": datetime.now(timezone.utc).isoformat(),
            "username": ctx.actor_id,
        }
        redis.setex(
            f"{RUNS_KEY_PREFIX}{run_id}",
            86400,
            json.dumps(run_data)
        )

        return {
            "status": "submitted",
            "run_id": run_id,
            "celery_task_id": task.id,
            "recipe": recipe_name,
        }
    except Exception as e:
        logger.error(f"Recipe run failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@router.get("/run/{run_id}")
async def get_run_status(
    run_id: str,
    ctx: UserContext = Depends(require_auth),
):
    """Get status of a recipe execution run."""
    import database
    from celery.result import AsyncResult

    redis = get_redis_client()

    # Check Redis for run status
    run_data = redis.get(f"{RUNS_KEY_PREFIX}{run_id}")
    if run_data:
        data = json.loads(run_data)

        # If pending, check Celery task status
        if data.get("status") == "pending" and data.get("celery_task_id"):
            result = AsyncResult(data["celery_task_id"])

            if result.ready():
                if result.successful():
                    task_result = result.get()
                    data["status"] = task_result.get("status", "completed")
                    data["output_hash"] = task_result.get("output_cache_id")
                    data["output_ipfs_cid"] = task_result.get("output_ipfs_cid")
                    data["total_steps"] = task_result.get("total_steps")
                    data["cached"] = task_result.get("cached")
                    data["executed"] = task_result.get("executed")

                    # Update Redis
                    redis.setex(
                        f"{RUNS_KEY_PREFIX}{run_id}",
                        86400,
                        json.dumps(data)
                    )
                else:
                    data["status"] = "failed"
                    data["error"] = str(result.result)
            else:
                data["celery_status"] = result.status

        return data

    # Check database cache
    cached = await database.get_run_cache(run_id)
    if cached:
        return {
            "run_id": run_id,
            "status": "completed",
            "output_hash": cached.get("output_hash"),
            "cached": True,
        }

    raise HTTPException(status_code=404, detail="Run not found")