634 lines
22 KiB
Python
634 lines
22 KiB
Python
"""GeoCrop Worker - RQ task runner for inference jobs.
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STEP 9: Real end-to-end pipeline orchestration.
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This module wires together all the step modules:
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- contracts.py (validation, payload parsing)
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- storage.py (MinIO adapter)
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- stac_client.py (DEA STAC search)
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- feature_computation.py (51-feature extraction)
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- dw_baseline.py (windowed DW baseline)
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- inference.py (model loading + prediction)
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- postprocess.py (majority filter smoothing)
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- cog.py (COG export)
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"""
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from __future__ import annotations
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import json
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import os
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import sys
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import tempfile
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import traceback
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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# Redis/RQ for job queue
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from redis import Redis
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from rq import Queue
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# ==========================================
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# Redis Configuration
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# ==========================================
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def _get_redis_conn():
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"""Create Redis connection, handling both simple and URL formats."""
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redis_url = os.getenv("REDIS_URL")
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if redis_url:
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# Handle REDIS_URL format (e.g., redis://host:6379)
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# MUST NOT use decode_responses=True because RQ uses pickle (binary)
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return Redis.from_url(redis_url)
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# Handle separate REDIS_HOST and REDIS_PORT
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redis_host = os.getenv("REDIS_HOST", "redis.geocrop.svc.cluster.local")
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redis_port_str = os.getenv("REDIS_PORT", "6379")
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# Handle case where REDIS_PORT might be a full URL
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try:
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redis_port = int(redis_port_str)
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except ValueError:
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# If it's a URL, extract the port
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if "://" in redis_port_str:
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import urllib.parse
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parsed = urllib.parse.urlparse(redis_port_str)
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redis_port = parsed.port or 6379
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else:
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redis_port = 6379
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# MUST NOT use decode_responses=True because RQ uses pickle (binary)
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return Redis(host=redis_host, port=redis_port)
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redis_conn = _get_redis_conn()
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# ==========================================
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# Status Update Helpers
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# ==========================================
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def safe_now_iso() -> str:
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"""Get current UTC time as ISO string."""
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return datetime.now(timezone.utc).isoformat()
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def update_status(
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job_id: str,
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status: str,
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stage: str,
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progress: int,
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message: str,
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outputs: Optional[Dict] = None,
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error: Optional[Dict] = None,
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) -> None:
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"""Update job status in Redis.
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Args:
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job_id: Job identifier
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status: Overall status (queued, running, failed, done)
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stage: Current pipeline stage
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progress: Progress percentage (0-100)
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message: Human-readable message
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outputs: Output file URLs (when done)
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error: Error details (on failure)
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"""
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key = f"job:{job_id}:status"
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status_data = {
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"status": status,
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"stage": stage,
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"progress": progress,
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"message": message,
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"updated_at": safe_now_iso(),
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}
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if outputs:
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status_data["outputs"] = outputs
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if error:
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status_data["error"] = error
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try:
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redis_conn.set(key, json.dumps(status_data), ex=86400) # 24h expiry
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# Also update the job metadata in RQ if possible
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from rq import get_current_job
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job = get_current_job()
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if job:
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job.meta['progress'] = progress
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job.meta['stage'] = stage
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job.meta['status_message'] = message
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job.save_meta()
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except Exception as e:
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print(f"Warning: Failed to update Redis status: {e}")
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# ==========================================
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# Payload Validation
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# ==========================================
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def parse_and_validate_payload(payload: dict) -> tuple[dict, List[str]]:
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"""Parse and validate job payload.
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Args:
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payload: Raw job payload dict
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Returns:
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Tuple of (validated_payload, list_of_errors)
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"""
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errors = []
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# Required fields
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required = ["job_id", "lat", "lon", "radius_m", "year"]
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for field in required:
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if field not in payload:
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errors.append(f"Missing required field: {field}")
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# Validate AOI
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if "lat" in payload and "lon" in payload:
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lat = float(payload["lat"])
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lon = float(payload["lon"])
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# Zimbabwe bounds check
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if not (-22.5 <= lat <= -15.6):
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errors.append(f"Latitude {lat} outside Zimbabwe bounds")
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if not (25.2 <= lon <= 33.1):
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errors.append(f"Longitude {lon} outside Zimbabwe bounds")
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# Validate radius
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if "radius_m" in payload:
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radius = int(payload["radius_m"])
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if radius > 5000:
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errors.append(f"Radius {radius}m exceeds max 5000m")
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if radius < 100:
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errors.append(f"Radius {radius}m below min 100m")
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# Validate year
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if "year" in payload:
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year = int(payload["year"])
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current_year = datetime.now().year
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if year < 2015 or year > current_year:
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errors.append(f"Year {year} outside valid range (2015-{current_year})")
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# Validate model
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if "model" in payload:
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valid_models = ["Ensemble", "RandomForest", "XGBoost", "LightGBM", "CatBoost"]
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if payload["model"] not in valid_models:
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errors.append(f"Invalid model: {payload['model']}. Must be one of {valid_models}")
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# Validate kernel
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if "smoothing_kernel" in payload:
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kernel = int(payload["smoothing_kernel"])
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if kernel not in [3, 5, 7]:
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errors.append(f"Invalid smoothing_kernel: {kernel}. Must be 3, 5, or 7")
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# Set defaults
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validated = {
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"job_id": payload.get("job_id", "unknown"),
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"lat": float(payload.get("lat", 0)),
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"lon": float(payload.get("lon", 0)),
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"radius_m": int(payload.get("radius_m", 2000)),
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"year": int(payload.get("year", 2022)),
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"season": payload.get("season", "summer"),
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"model": payload.get("model", "Ensemble"),
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"smoothing_kernel": int(payload.get("smoothing_kernel", 5)),
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"outputs": {
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"refined": payload.get("outputs", {}).get("refined", True),
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"dw_baseline": payload.get("outputs", {}).get("dw_baseline", False),
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"true_color": payload.get("outputs", {}).get("true_color", False),
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"indices": payload.get("outputs", {}).get("indices", []),
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},
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}
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return validated, errors
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# ==========================================
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# Main Job Runner
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# ==========================================
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def run_job(payload_dict: dict) -> dict:
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"""Main job runner function.
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This is the RQ task function that orchestrates the full pipeline.
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"""
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from rq import get_current_job
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current_job = get_current_job()
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# Extract job_id from payload or RQ
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job_id = payload_dict.get("job_id")
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if not job_id and current_job:
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job_id = current_job.id
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if not job_id:
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job_id = "unknown"
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# Ensure job_id is in payload for validation
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payload_dict["job_id"] = job_id
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# Standardize payload from API format to worker format
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# API sends: radius_km, model_name
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# Worker expects: radius_m, model
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if "radius_km" in payload_dict and "radius_m" not in payload_dict:
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payload_dict["radius_m"] = int(float(payload_dict["radius_km"]) * 1000)
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if "model_name" in payload_dict and "model" not in payload_dict:
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payload_dict["model"] = payload_dict["model_name"]
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# Initialize storage
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try:
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from storage import MinIOStorage
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storage = MinIOStorage()
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except Exception as e:
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update_status(
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job_id, "failed", "init", 0,
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f"Failed to initialize storage: {e}",
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error={"type": "StorageError", "message": str(e)}
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)
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return {"status": "failed", "error": str(e)}
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# Parse and validate payload
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payload, errors = parse_and_validate_payload(payload_dict)
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if errors:
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update_status(
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job_id, "failed", "validation", 0,
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f"Validation failed: {errors}",
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error={"type": "ValidationError", "message": "; ".join(errors)}
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)
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return {"status": "failed", "errors": errors}
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# Update initial status
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update_status(job_id, "running", "fetch_stac", 5, "Fetching STAC items...")
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try:
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# ==========================================
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# Stage 1: Fetch STAC
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# ==========================================
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print(f"[{job_id}] Fetching STAC items for {payload['year']} {payload['season']}...")
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from stac_client import DEASTACClient
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from config import InferenceConfig
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cfg = InferenceConfig()
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# Get season dates
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start_date, end_date = cfg.season_dates(payload['year'], payload['season'])
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# Calculate AOI bbox
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lat, lon, radius = payload['lat'], payload['lon'], payload['radius_m']
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# Rough bbox from radius (in degrees)
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radius_deg = radius / 111000 # ~111km per degree
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bbox = [
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lon - radius_deg, # min_lon
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lat - radius_deg, # min_lat
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lon + radius_deg, # max_lon
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lat + radius_deg, # max_lat
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]
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# Search STAC
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stac_client = DEASTACClient()
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try:
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items = stac_client.search_items(
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bbox=bbox,
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start_date=start_date,
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end_date=end_date,
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)
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print(f"[{job_id}] Found {len(items)} STAC items")
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except Exception as e:
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print(f"[{job_id}] STAC search failed: {e}")
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# Continue but note that features may be limited
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update_status(job_id, "running", "build_features", 20, "Building feature cube...")
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# ==========================================
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# Stage 2: Build Feature Cube
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# ==========================================
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print(f"[{job_id}] Building feature cube...")
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from feature_computation import FEATURE_ORDER_V1
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feature_order = FEATURE_ORDER_V1
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expected_features = len(feature_order) # Should be 51
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print(f"[{job_id}] Expected {expected_features} features (FEATURE_ORDER_V1)")
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# Check if we have an existing feature builder in features.py
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feature_cube = None
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use_synthetic = False
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try:
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from features import build_feature_stack_from_dea
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print(f"[{job_id}] Trying build_feature_stack_from_dea for feature extraction...")
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# Try to call it - this requires stackstac and DEA STAC access
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try:
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feature_cube = build_feature_stack_from_dea(
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items=items,
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bbox=bbox,
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start_date=start_date,
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end_date=end_date,
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)
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print(f"[{job_id}] Feature cube built successfully: {feature_cube.shape if feature_cube is not None else 'None'}")
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except Exception as e:
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print(f"[{job_id}] Feature stack building failed: {e}")
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print(f"[{job_id}] Falling back to synthetic features for testing")
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use_synthetic = True
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except ImportError as e:
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print(f"[{job_id}] Feature builder not available: {e}")
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print(f"[{job_id}] Using synthetic features for testing")
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use_synthetic = True
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# Generate synthetic features for testing when real data isn't available
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if feature_cube is None:
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print(f"[{job_id}] Generating synthetic features for pipeline test...")
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# Determine raster dimensions from DW baseline if loaded
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if 'dw_arr' in dir() and dw_arr is not None:
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H, W = dw_arr.shape
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else:
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# Default size for testing
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H, W = 100, 100
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# Generate synthetic features: shape (H, W, 51)
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import numpy as np
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# Use year as seed for reproducible but varied features
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np.random.seed(payload['year'] + int(payload.get('lon', 0) * 100) + int(payload.get('lat', 0) * 100))
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# Generate realistic-looking features (normalized values)
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feature_cube = np.random.rand(H, W, expected_features).astype(np.float32)
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# Add some structure - make center pixels different from edges
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y, x = np.ogrid[:H, :W]
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center_y, center_x = H // 2, W // 2
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dist = np.sqrt((y - center_y)**2 + (x - center_x)**2)
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max_dist = np.sqrt(center_y**2 + center_x**2)
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# Add a gradient based on distance from center (simulating field pattern)
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for i in range(min(10, expected_features)):
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feature_cube[:, :, i] = (1 - dist / max_dist) * 0.5 + feature_cube[:, :, i] * 0.5
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print(f"[{job_id}] Synthetic feature cube shape: {feature_cube.shape}")
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# ==========================================
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# Stage 3: Load DW Baseline
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# ==========================================
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update_status(job_id, "running", "load_dw", 40, "Loading DW baseline...")
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print(f"[{job_id}] Loading DW baseline for {payload['year']}...")
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from dw_baseline import load_dw_baseline_window
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try:
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dw_arr, dw_profile = load_dw_baseline_window(
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storage=storage,
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year=payload['year'],
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aoi_bbox_wgs84=bbox,
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season=payload['season'],
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)
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if dw_arr is None:
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raise FileNotFoundError(f"No DW baseline found for year {payload['year']}")
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print(f"[{job_id}] DW baseline shape: {dw_arr.shape}")
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except Exception as e:
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update_status(
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job_id, "failed", "load_dw", 45,
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f"Failed to load DW baseline: {e}",
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error={"type": "DWBASELINE_ERROR", "message": str(e)}
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)
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return {"status": "failed", "error": f"DW baseline error: {e}"}
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# ==========================================
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# Stage 4: Skip AI Inference, use DW as result
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# ==========================================
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update_status(job_id, "running", "infer", 60, "Using DW baseline as classification...")
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print(f"[{job_id}] Using DW baseline as result (Skipping AI models as requested)")
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# We use dw_arr as the classification result
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cls_raster = dw_arr.copy()
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# ==========================================
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# Stage 5: Apply Smoothing (Optional for DW)
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# ==========================================
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if payload.get('smoothing_kernel'):
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kernel = payload['smoothing_kernel']
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update_status(job_id, "running", "smooth", 75, f"Applying smoothing (k={kernel})...")
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from postprocess import majority_filter
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cls_raster = majority_filter(cls_raster, kernel=kernel, nodata=0)
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print(f"[{job_id}] Smoothing applied")
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# ==========================================
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# Stage 6: Export COGs
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# ==========================================
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update_status(job_id, "running", "export_cog", 80, "Exporting COGs...")
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from cog import write_cog
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output_dir = Path(tempfile.mkdtemp())
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output_urls = {}
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missing_outputs = []
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# Export refined raster
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if payload['outputs'].get('refined', True):
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try:
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refined_path = output_dir / "refined.tif"
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dtype = "uint8" if cls_raster.max() <= 255 else "uint16"
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write_cog(
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str(refined_path),
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cls_raster.astype(dtype),
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dw_profile,
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dtype=dtype,
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nodata=0,
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)
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# Upload
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result_key = f"results/{job_id}/refined.tif"
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storage.upload_result(refined_path, result_key)
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output_urls["refined_url"] = storage.presign_get("geocrop-results", result_key)
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print(f"[{job_id}] Exported refined.tif")
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except Exception as e:
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missing_outputs.append(f"refined: {e}")
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# Export DW baseline if requested
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if payload['outputs'].get('dw_baseline', False):
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try:
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dw_path = output_dir / "dw_baseline.tif"
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write_cog(
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str(dw_path),
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dw_arr.astype("uint8"),
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dw_profile,
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dtype="uint8",
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nodata=0,
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)
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result_key = f"results/{job_id}/dw_baseline.tif"
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storage.upload_result(dw_path, result_key)
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output_urls["dw_baseline_url"] = storage.presign_get("geocrop-results", result_key)
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print(f"[{job_id}] Exported dw_baseline.tif")
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except Exception as e:
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missing_outputs.append(f"dw_baseline: {e}")
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# Note: indices and true_color not yet implemented
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if payload['outputs'].get('indices'):
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missing_outputs.append("indices: not implemented")
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if payload['outputs'].get('true_color'):
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missing_outputs.append("true_color: not implemented")
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# ==========================================
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# Stage 7: Final Status
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# ==========================================
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final_status = "partial" if missing_outputs else "done"
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final_message = f"Inference complete"
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if missing_outputs:
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final_message += f" (partial: {', '.join(missing_outputs)})"
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update_status(
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job_id,
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final_status,
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"done",
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100,
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final_message,
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outputs=output_urls,
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)
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print(f"[{job_id}] Job complete: {final_status}")
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return {
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"status": final_status,
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"job_id": job_id,
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"outputs": output_urls,
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"missing": missing_outputs if missing_outputs else None,
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}
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except Exception as e:
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# Catch-all for any unexpected errors
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error_trace = traceback.format_exc()
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print(f"[{job_id}] Error: {e}")
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print(error_trace)
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update_status(
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job_id, "failed", "error", 0,
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f"Unexpected error: {e}",
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error={"type": type(e).__name__, "message": str(e), "trace": error_trace}
|
|
)
|
|
|
|
return {
|
|
"status": "failed",
|
|
"error": str(e),
|
|
"job_id": job_id,
|
|
}
|
|
|
|
# Alias for API
|
|
run_inference = run_job
|
|
|
|
# ==========================================
|
|
# RQ Worker Entry Point
|
|
# ==========================================
|
|
|
|
def start_rq_worker():
|
|
"""Start the RQ worker to listen for jobs on the geocrop_tasks queue."""
|
|
from rq import Worker
|
|
import signal
|
|
|
|
# Ensure /app is in sys.path so we can import modules
|
|
if '/app' not in sys.path:
|
|
sys.path.insert(0, '/app')
|
|
|
|
queue_name = os.getenv("RQ_QUEUE_NAME", "geocrop_tasks")
|
|
|
|
print(f"=== GeoCrop RQ Worker Starting ===")
|
|
print(f"Listening on queue: {queue_name}")
|
|
print(f"Redis: {os.getenv('REDIS_HOST', 'redis.geocrop.svc.cluster.local')}:{os.getenv('REDIS_PORT', '6379')}")
|
|
print(f"Python path: {sys.path[:3]}")
|
|
|
|
# Handle graceful shutdown
|
|
def signal_handler(signum, frame):
|
|
print("\nReceived shutdown signal, exiting gracefully...")
|
|
sys.exit(0)
|
|
|
|
signal.signal(signal.SIGINT, signal_handler)
|
|
signal.signal(signal.SIGTERM, signal_handler)
|
|
|
|
try:
|
|
q = Queue(queue_name, connection=redis_conn)
|
|
w = Worker([q], connection=redis_conn)
|
|
w.work()
|
|
except KeyboardInterrupt:
|
|
print("\nWorker interrupted, shutting down...")
|
|
except Exception as e:
|
|
print(f"Worker error: {e}")
|
|
raise
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(description="GeoCrop Worker")
|
|
parser.add_argument("--test", action="store_true", help="Run syntax test only")
|
|
parser.add_argument("--worker", action="store_true", help="Start RQ worker")
|
|
args = parser.parse_args()
|
|
|
|
if args.test or not args.worker:
|
|
# Syntax-level self-test
|
|
print("=== GeoCrop Worker Syntax Test ===")
|
|
|
|
# Test imports
|
|
try:
|
|
from contracts import STAGES, VALID_MODELS
|
|
from storage import MinIOStorage
|
|
from feature_computation import FEATURE_ORDER_V1
|
|
print(f"✓ Imports OK")
|
|
print(f" STAGES: {STAGES}")
|
|
print(f" VALID_MODELS: {VALID_MODELS}")
|
|
print(f" FEATURE_ORDER length: {len(FEATURE_ORDER_V1)}")
|
|
except ImportError as e:
|
|
print(f"⚠ Some imports missing (expected outside container): {e}")
|
|
|
|
# Test payload parsing
|
|
print("\n--- Payload Parsing Test ---")
|
|
test_payload = {
|
|
"job_id": "test-123",
|
|
"lat": -17.8,
|
|
"lon": 31.0,
|
|
"radius_m": 2000,
|
|
"year": 2022,
|
|
"model": "Ensemble",
|
|
"smoothing_kernel": 5,
|
|
"outputs": {"refined": True, "dw_baseline": True},
|
|
}
|
|
|
|
validated, errors = parse_and_validate_payload(test_payload)
|
|
if errors:
|
|
print(f"✗ Validation errors: {errors}")
|
|
else:
|
|
print(f"✓ Payload validation passed")
|
|
print(f" job_id: {validated['job_id']}")
|
|
print(f" AOI: ({validated['lat']}, {validated['lon']}) radius={validated['radius_m']}m")
|
|
print(f" model: {validated['model']}")
|
|
print(f" kernel: {validated['smoothing_kernel']}")
|
|
|
|
# Show what would run
|
|
print("\n--- Pipeline Overview ---")
|
|
print("Pipeline stages:")
|
|
for i, stage in enumerate(STAGES):
|
|
print(f" {i+1}. {stage}")
|
|
|
|
print("\nNote: This is a syntax-level test.")
|
|
print("Full execution requires Redis, MinIO, and STAC access in the container.")
|
|
|
|
print("\n=== Worker Syntax Test Complete ===")
|
|
|
|
if args.worker:
|
|
start_rq_worker()
|