refactor storage minio for k8s

core/api/detect_sources.py

@@ -0,0 +1,259 @@
+"""
+Source browser for detection pipeline.
+
+Lists available media sources from blob storage (MinIO).
+All file-based sources go through MinIO — no host filesystem access.
+The pipeline downloads chunks to a temp path before processing.
+
+Source types (current and future):
+  - chunk_job: pre-chunked segments in MinIO (current)
+  - upload:    user-uploaded file, lands in MinIO via upload endpoint (future)
+  - device:    local camera/capture card via ffmpeg, no MinIO (future)
+  - stream:    RTMP/HLS URL via ffmpeg, no MinIO (future)
+
+GET  /detect/sources                    — list chunk jobs from blob store
+GET  /detect/sources/{job_id}/chunks    — list chunks for a specific job
+POST /detect/run                        — launch pipeline on selected source
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import threading
+import uuid
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/detect", tags=["detect"])
+
+# In-process pipeline tracking
+_running_jobs: dict[str, "threading.Thread"] = {}
+_cancelled_jobs: set[str] = set()
+
+
+class ChunkInfo(BaseModel):
+    filename: str
+    key: str
+    size_bytes: int
+
+
+class SourceInfo(BaseModel):
+    job_id: str
+    source_type: str = "chunk_job"
+    chunk_count: int
+    total_bytes: int = 0
+
+
+class RunRequest(BaseModel):
+    video_path: str           # storage key
+    profile_name: str = "soccer_broadcast"
+    source_asset_id: str = ""
+    checkpoint: bool = True
+    skip_vlm: bool = False
+    skip_cloud: bool = False
+    log_level: str = "INFO"  # INFO | DEBUG
+
+
+class RunResponse(BaseModel):
+    status: str
+    job_id: str
+    video_path: str
+
+
+# ---------------------------------------------------------------------------
+# Source listing
+# ---------------------------------------------------------------------------
+
+def _list_sources() -> list[SourceInfo]:
+    """List chunk jobs from blob storage."""
+    from core.storage.blob import get_store
+
+    store = get_store("out")
+    try:
+        objects = store.list(prefix="chunks/")
+    except Exception as e:
+        logger.warning("Failed to list blob sources: %s", e)
+        return []
+
+    jobs: dict[str, int] = {}
+    job_bytes: dict[str, int] = {}
+    for obj in objects:
+        # Keys include store prefix: out/chunks/{job_id}/file.mp4
+        # Strip prefix to get: chunks/{job_id}/file.mp4
+        rel_key = obj.key.removeprefix(store.prefix)
+        parts = rel_key.split("/")
+        if len(parts) >= 3 and parts[0] == "chunks":
+            job_id = parts[1]
+            jobs[job_id] = jobs.get(job_id, 0) + 1
+            job_bytes[job_id] = job_bytes.get(job_id, 0) + obj.size_bytes
+
+    sources = []
+    for job_id, count in sorted(jobs.items()):
+        source = SourceInfo(
+            job_id=job_id,
+            source_type="chunk_job",
+            chunk_count=count,
+            total_bytes=job_bytes.get(job_id, 0),
+        )
+        sources.append(source)
+    return sources
+
+
+@router.get("/sources", response_model=list[SourceInfo])
+def list_sources():
+    """List available chunk jobs from blob storage."""
+    return _list_sources()
+
+
+@router.get("/sources/{source_job_id}/chunks", response_model=list[ChunkInfo])
+def list_chunks(source_job_id: str):
+    """List chunks for a specific source job."""
+    from core.storage.blob import get_store
+
+    store = get_store("out")
+    try:
+        objects = store.list(prefix=f"chunks/{source_job_id}/", extensions={".mp4"})
+    except Exception as e:
+        logger.warning("Failed to list chunks for %s: %s", source_job_id, e)
+        raise HTTPException(status_code=503, detail=f"Blob storage unavailable: {e}")
+
+    if not objects:
+        raise HTTPException(status_code=404, detail=f"Source not found: {source_job_id}")
+
+    chunks = []
+    for obj in objects:
+        info = ChunkInfo(filename=obj.filename, key=obj.key, size_bytes=obj.size_bytes)
+        chunks.append(info)
+    return sorted(chunks, key=lambda c: c.filename)
+
+
+@router.get("/sources/{source_job_id}/chunks/{filename}/url")
+def get_chunk_url(source_job_id: str, filename: str):
+    """Return a presigned URL for previewing a chunk in the browser."""
+    from core.storage.blob import get_store
+
+    store = get_store("out")
+    key = f"chunks/{source_job_id}/{filename}"
+    try:
+        url = store.get_url(key, expires=3600)
+    except Exception as e:
+        raise HTTPException(status_code=503, detail=f"Could not generate URL: {e}")
+    return {"url": url}
+
+
+# ---------------------------------------------------------------------------
+# Run pipeline
+# ---------------------------------------------------------------------------
+
+def _resolve_video_path(video_path: str) -> str:
+    """Download a chunk from blob storage to a temp file."""
+    from core.storage.blob import get_store
+
+    store = get_store("out")
+    try:
+        return store.download_to_temp(video_path)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Failed to download chunk: {e}")
+
+
+@router.post("/run", response_model=RunResponse)
+def run_pipeline(req: RunRequest):
+    """Launch a detection pipeline run on a source chunk."""
+    from detect import emit
+    from detect.graph import get_pipeline
+    from detect.state import DetectState
+
+    local_path = _resolve_video_path(req.video_path)
+    job_id = str(uuid.uuid4())[:8]
+
+    if req.skip_vlm:
+        os.environ["SKIP_VLM"] = "1"
+    elif "SKIP_VLM" in os.environ:
+        del os.environ["SKIP_VLM"]
+
+    if req.skip_cloud:
+        os.environ["SKIP_CLOUD"] = "1"
+    elif "SKIP_CLOUD" in os.environ:
+        del os.environ["SKIP_CLOUD"]
+
+    # Clear any stale events from a previous run with same job_id
+    from core.events import _get_redis
+    from detect.events import DETECT_EVENTS_PREFIX
+    r = _get_redis()
+    r.delete(f"{DETECT_EVENTS_PREFIX}:{job_id}")
+
+    emit.set_run_context(
+        run_id=job_id, parent_job_id=job_id, run_type="initial",
+        log_level=req.log_level,
+    )
+
+    pipeline = get_pipeline(checkpoint=req.checkpoint)
+
+    initial_state = DetectState(
+        video_path=local_path,
+        job_id=job_id,
+        profile_name=req.profile_name,
+        source_asset_id=req.source_asset_id,
+    )
+
+    import traceback
+
+    from detect.graph import PipelineCancelled, set_cancel_check, clear_cancel_check
+
+    set_cancel_check(job_id, lambda: job_id in _cancelled_jobs)
+
+    def _run():
+        try:
+            emit.log(job_id, "Pipeline", "INFO",
+                     f"Starting pipeline: {req.video_path} (profile={req.profile_name})")
+            pipeline.invoke(initial_state)
+            emit.log(job_id, "Pipeline", "INFO", "Pipeline completed successfully")
+            emit.job_complete(job_id, {"status": "completed"})
+        except PipelineCancelled:
+            emit.log(job_id, "Pipeline", "INFO", "Pipeline cancelled")
+            emit.job_complete(job_id, {"status": "cancelled"})
+        except Exception as e:
+            logger.exception("Pipeline run %s failed: %s", job_id, e)
+            tb = traceback.format_exc()
+            emit.log(job_id, "Pipeline", "ERROR", str(e))
+            emit.log(job_id, "Pipeline", "DEBUG", tb)
+            emit.job_complete(job_id, {"status": "failed", "error": str(e)})
+        finally:
+            _running_jobs.pop(job_id, None)
+            _cancelled_jobs.discard(job_id)
+            clear_cancel_check(job_id)
+            emit.clear_run_context()
+
+    thread = threading.Thread(target=_run, daemon=True, name=f"pipeline-{job_id}")
+    _running_jobs[job_id] = thread
+    thread.start()
+
+    return RunResponse(status="started", job_id=job_id, video_path=req.video_path)
+
+
+@router.post("/stop/{job_id}")
+def stop_pipeline(job_id: str):
+    """Stop a running pipeline. Signals cancellation; the thread checks on next stage."""
+    from detect import emit
+
+    if job_id not in _running_jobs:
+        raise HTTPException(status_code=404, detail=f"No running pipeline: {job_id}")
+
+    _cancelled_jobs.add(job_id)
+    emit.log(job_id, "Pipeline", "INFO", "Stop requested — cancelling after current stage")
+    return {"status": "stopping", "job_id": job_id}
+
+
+@router.post("/clear/{job_id}")
+def clear_pipeline(job_id: str):
+    """Clear events for a job from Redis."""
+    from core.events import _get_redis
+    from detect.events import DETECT_EVENTS_PREFIX
+
+    r = _get_redis()
+    r.delete(f"{DETECT_EVENTS_PREFIX}:{job_id}")
+    return {"status": "cleared", "job_id": job_id}

core/api/main.py

@@ -27,6 +27,7 @@ from core.api.chunker_sse import router as chunker_router
 from core.api.detect_sse import router as detect_router
 from core.api.detect_replay import router as detect_replay_router
 from core.api.detect_config import router as detect_config_router
+from core.api.detect_sources import router as detect_sources_router
 from core.api.graphql import schema as graphql_schema
 
 CALLBACK_API_KEY = os.environ.get("CALLBACK_API_KEY", "")
@@ -64,6 +65,9 @@ app.include_router(detect_replay_router)
 # Detection config
 app.include_router(detect_config_router)
 
+# Detection sources + run launcher
+app.include_router(detect_sources_router)
+
 
 @app.get("/health")
 def health():

core/jobs/lambda_handler.py

@@ -20,8 +20,8 @@ logger = logging.getLogger()
 logger.setLevel(logging.INFO)
 
 # S3 config
-S3_BUCKET_IN = os.environ.get("S3_BUCKET_IN", "mpr-media-in")
-S3_BUCKET_OUT = os.environ.get("S3_BUCKET_OUT", "mpr-media-out")
+S3_BUCKET_IN = os.environ.get("S3_BUCKET_IN", "in")
+S3_BUCKET_OUT = os.environ.get("S3_BUCKET_OUT", "out")
 AWS_REGION = os.environ.get("AWS_REGION", "us-east-1")
 
 s3 = boto3.client("s3", region_name=AWS_REGION)

core/schema/models/__init__.py

@@ -35,10 +35,12 @@ from .presets import BUILTIN_PRESETS, TranscodePreset
 from .detect import DETECT_VIEWS  # noqa: F401 — discovered by modelgen generic loader
 from .ui_state import UI_STATE_VIEWS  # noqa: F401 — UI store state types
 from .views import ChunkEvent, ChunkOutputFile, PipelineStats, WorkerEvent
+from .sources import ChunkInfo, SourceJob, SourceType
 
 # Core domain models - generates Django, Pydantic, TypeScript
 DATACLASSES = [MediaAsset, TranscodePreset, TranscodeJob, ChunkJob,
-               DetectJob, StageCheckpoint, KnownBrand, SourceBrandSighting]
+               DetectJob, StageCheckpoint, KnownBrand, SourceBrandSighting,
+               SourceJob, ChunkInfo]
 
 # API request/response models - generates TypeScript only (no Django)
 # WorkerStatus from grpc.py is reused here
@@ -52,7 +54,7 @@ API_MODELS = [
 ]
 
 # Status enums - included in generated code
-ENUMS = [AssetStatus, JobStatus, ChunkJobStatus, DetectJobStatus, RunType, BrandSource]
+ENUMS = [AssetStatus, JobStatus, ChunkJobStatus, DetectJobStatus, RunType, BrandSource, SourceType]
 
 # View/event models - generates TypeScript for UI consumption
 VIEWS = [ChunkEvent, WorkerEvent, PipelineStats, ChunkOutputFile]
@@ -105,6 +107,10 @@ __all__ = [
     "WorkerEvent",
     "PipelineStats",
     "ChunkOutputFile",
+    # Sources
+    "SourceType",
+    "SourceJob",
+    "ChunkInfo",
     # For generator
     "DATACLASSES",
     "API_MODELS",

core/schema/models/sources.py

@@ -0,0 +1,39 @@
+"""
+Media source models.
+
+Describes what types of sources the detection pipeline can process.
+Only chunk_job (blobs in MinIO) is implemented now — the rest are
+extension points with defined shapes.
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+
+
+class SourceType(str, Enum):
+    CHUNK_JOB = "chunk_job"   # pre-chunked video segments in blob storage
+    UPLOAD    = "upload"      # future: user-uploaded file → MinIO → pipeline
+    DEVICE    = "device"      # future: local camera/capture card via ffmpeg (no MinIO)
+    STREAM    = "stream"      # future: RTMP/HLS URL via ffmpeg (no MinIO)
+
+
+@dataclass
+class ChunkInfo:
+    """A single chunk (video segment) stored in blob storage."""
+    filename: str
+    key: str          # storage key (MinIO object key)
+    size_bytes: int
+
+
+@dataclass
+class SourceJob:
+    """
+    A group of chunks that belong together (same source video/session).
+
+    Listed by the source selector so the user can pick a job,
+    then drill into its chunks.
+    """
+    job_id: str
+    source_type: str          # SourceType value
+    chunk_count: int
+    total_bytes: int = 0

core/storage/__init__.py

@@ -1,6 +1,5 @@
+from .blob import BUCKET, PREFIX_CHECKPOINTS, PREFIX_IN, PREFIX_OUT, BlobObject, BlobStore, get_store
 from .s3 import (
-    BUCKET_IN,
-    BUCKET_OUT,
     download_file,
     download_to_temp,
     get_presigned_url,
@@ -8,3 +7,8 @@ from .s3 import (
     list_objects,
     upload_file,
 )
+
+# Backward compat — old code uses BUCKET_IN / BUCKET_OUT as full bucket names.
+# Now they're one bucket; these exist so existing handlers don't break.
+BUCKET_IN = BUCKET
+BUCKET_OUT = BUCKET

core/storage/blob.py

@@ -0,0 +1,112 @@
+"""
+Cloud-agnostic blob storage interface.
+
+All file-based sources (chunks, uploads, checkpoints) go through MinIO.
+Local dev runs MinIO in docker-compose — same code path as production.
+Production changes S3_ENDPOINT_URL; nothing else changes.
+
+Single bucket, multiple prefixes:
+  in/              — source media
+  out/             — transcoded chunks
+  checkpoints/     — detection intermediate blobs (frames, crops)
+
+Each prefix is independently configurable via env vars so they can
+be split into separate buckets later if needed.
+
+Nothing outside core/storage/ should import boto3 directly.
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import Optional
+
+
+# Single bucket, prefix-based layout
+BUCKET = os.environ.get("S3_BUCKET", "mpr")
+PREFIX_IN = os.environ.get("S3_PREFIX_IN", "in/")
+PREFIX_OUT = os.environ.get("S3_PREFIX_OUT", "out/")
+PREFIX_CHECKPOINTS = os.environ.get("S3_PREFIX_CHECKPOINTS", "checkpoints/")
+
+
+@dataclass
+class BlobObject:
+    key: str
+    filename: str
+    size_bytes: int
+
+
+class BlobStore:
+    """
+    Thin wrapper over the S3-compatible storage backend (MinIO / AWS S3).
+
+    All configuration (endpoint URL, credentials, region) is read from
+    environment variables by the underlying s3 module.
+    """
+
+    def __init__(self, bucket: str, prefix: str = ""):
+        self.bucket = bucket
+        self.prefix = prefix
+
+    def _full_prefix(self, prefix: str) -> str:
+        """Combine store prefix with caller prefix."""
+        return self.prefix + prefix
+
+    def list(
+        self,
+        prefix: str = "",
+        extensions: Optional[set[str]] = None,
+    ) -> list[BlobObject]:
+        """List objects in the bucket, optionally filtered by extension."""
+        from core.storage.s3 import list_objects
+
+        full = self._full_prefix(prefix)
+        raw = list_objects(self.bucket, prefix=full, extensions=extensions)
+        objects = []
+        for obj in raw:
+            blob = BlobObject(
+                key=obj["key"],
+                filename=obj["filename"],
+                size_bytes=obj["size"],
+            )
+            objects.append(blob)
+        return objects
+
+    def download_to_temp(self, key: str) -> str:
+        """Download a blob to a temp file. Caller is responsible for cleanup."""
+        from core.storage.s3 import download_to_temp
+
+        return download_to_temp(self.bucket, key)
+
+    def upload(self, local_path: str, key: str) -> None:
+        """Upload a local file to the bucket."""
+        from core.storage.s3 import upload_file
+
+        upload_file(local_path, self.bucket, key)
+
+    def get_url(self, key: str, expires: int = 3600) -> str:
+        """Return a presigned URL for the given key."""
+        from core.storage.s3 import get_presigned_url
+
+        return get_presigned_url(self.bucket, key, expires=expires)
+
+
+def get_store(purpose: str = "out") -> BlobStore:
+    """
+    Return a BlobStore for the given purpose.
+
+    Purposes map to prefixes:
+      "in"          → source media     (S3_PREFIX_IN)
+      "out"         → transcoded output (S3_PREFIX_OUT)
+      "checkpoints" → detection blobs   (S3_PREFIX_CHECKPOINTS)
+
+    All share the same bucket (S3_BUCKET), each scoped to its prefix.
+    """
+    prefix_map = {
+        "in": PREFIX_IN,
+        "out": PREFIX_OUT,
+        "checkpoints": PREFIX_CHECKPOINTS,
+    }
+    prefix = prefix_map.get(purpose, "")
+    return BlobStore(BUCKET, prefix=prefix)

core/storage/s3.py

@@ -13,8 +13,8 @@ from typing import Optional
 import boto3
 from botocore.config import Config
 
-BUCKET_IN = os.environ.get("S3_BUCKET_IN", "mpr-media-in")
-BUCKET_OUT = os.environ.get("S3_BUCKET_OUT", "mpr-media-out")
+BUCKET_IN = os.environ.get("S3_BUCKET_IN", "in")
+BUCKET_OUT = os.environ.get("S3_BUCKET_OUT", "out")
 
 
 def get_s3_client():