Add dataset bench and validation agents

.env.example

@@ -3,7 +3,7 @@ SEG_DATA_SERVER_ROOT=.
 SEG_BACKEND_DB=var/seg_data_server.sqlite3
 SEG_BACKEND_LOG_DIR=var/job_logs
 SEG_TASK_CONDA_ENV=seg_smp
-SEG_BACKEND_CONDA_ENV=seg_server
+SEG_BACKEND_CONDA_ENV=seg_smp
 SEG_WEIGHT_MODE=copy
 SEG_ENABLE_SHELL_TASKS=1
-VITE_API_BASE=http://localhost:8000
+VITE_API_BASE=http://localhost:8010

README.md

@@ -27,8 +27,9 @@ Seg_Data_Server_Net/
 cd Seg_Data_Server_Net
 cp .env.example .env
 
-# Backend. The existing machine already has a seg_server env with FastAPI.
+# Backend. The deployment env is seg_smp so the API and task wrappers share
-conda run -n seg_server uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8000
+# the same segmentation dependency stack.
+conda run -n seg_smp uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8010
 
 # Frontend.
 cd frontend
@@ -37,7 +38,13 @@ npm run dev -- --host 0.0.0.0
 ```
 
 Open the Vite URL shown in the terminal. The frontend expects the backend at
-`http://localhost:8000` by default.
+`http://localhost:8010` by default.
+
+The web UI includes a dataset bench for creating upload workspaces, uploading
+images/labels/masks, and jumping into the existing rename, PNG conversion,
+resize, pair-check, label rebuild, transparent overlay, stitch, and video-frame
+jobs. Segmentation previews, YOLO heatmaps, and loss/metric artifacts are
+grouped on the results dashboard.
 
 ## Weight Sync
 
@@ -77,3 +84,15 @@ The backend exposes all current Seg capabilities as job types. Examples:
 
 Use `GET /api/catalog` to inspect supported models, algorithms, datasets, and
 task types discovered from the existing `Seg/` workspace.
+
+## Agents
+
+Run the local evaluation and validation agents before publishing changes:
+
+```bash
+PYTHONPATH=backend conda run -n seg_smp python scripts/run_agents.py --build
+```
+
+The validation agent checks catalog coverage, the new `seg_smp` env, GPU
+visibility, no-weight Git safety, backend tests, frontend build, and live
+backend/frontend endpoints when the services are running.

backend/app/agents/__init__.py

@@ -0,0 +1,2 @@
+"""Local deterministic agents for app evaluation and validation."""
+

backend/app/agents/evaluation_agent.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from ..catalog import get_catalog
+from ..config import settings
+
+
+REQUIRED_TASKS = {
+    "dataset.upload": "covered_by_api",
+    "dataset.video_frames": "job",
+    "segmodel.train": "job",
+    "segmodel.predict": "job",
+    "yolo.heatmap": "job",
+    "mmseg.flops_fps": "job",
+    "analysis.all": "job",
+}
+
+
+def evaluate_project() -> dict:
+    """Return product/implementation suggestions for the current web platform."""
+    frontend = settings.project_root / "frontend" / "src" / "main.tsx"
+    backend = settings.project_root / "backend" / "app" / "main.py"
+    readme = settings.project_root / "README.md"
+    catalog = get_catalog()
+    checks = []
+    suggestions = []
+
+    frontend_text = frontend.read_text(encoding="utf-8") if frontend.exists() else ""
+    backend_text = backend.read_text(encoding="utf-8") if backend.exists() else ""
+    readme_text = readme.read_text(encoding="utf-8") if readme.exists() else ""
+
+    expectations = {
+        "left_nav_dataset": "数据集" in frontend_text and "#datasets" in frontend_text,
+        "upload_ui": "uploadDatasetFiles" in frontend_text and "labels" in frontend_text and "masks" in frontend_text,
+        "loss_result_ui": "loss" in frontend_text.lower() and "heatmap" in frontend_text.lower(),
+        "dataset_api": "/api/datasets" in backend_text and "api_upload_dataset_files" in backend_text,
+        "no_weight_to_gitea": "Do not push" in readme_text and "check_no_weight_git" in readme_text,
+        "all_core_tasks": all(task in catalog["task_types"] for task in REQUIRED_TASKS if REQUIRED_TASKS[task] == "job"),
+    }
+
+    for name, passed in expectations.items():
+        checks.append({"name": name, "passed": bool(passed)})
+        if not passed:
+            suggestions.append(f"Improve missing capability: {name}")
+
+    if len(catalog["mmseg_algorithms"]) < 31:
+        suggestions.append("MMSeg algorithm catalog should expose all 31 algorithm generators.")
+    if len(catalog["segmodel_architectures"]) < 12:
+        suggestions.append("SegModel catalog should expose all 12 supported architectures.")
+    if not suggestions:
+        suggestions.append("Current platform covers the requested control-plane features; next focus is real dataset/training acceptance tests.")
+
+    score = sum(1 for item in checks if item["passed"]) / max(len(checks), 1)
+    return {
+        "agent": "evaluation_suggestion_agent",
+        "score": round(score, 3),
+        "checks": checks,
+        "suggestions": suggestions,
+    }
+

backend/app/agents/validation_agent.py

@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+import json
+import os
+import subprocess
+import tempfile
+import urllib.error
+import urllib.request
+from pathlib import Path
+
+from ..catalog import get_catalog
+from ..config import settings
+from ..modules.system.service import get_conda_envs, get_gpus
+from ..modules.weights.service import load_manifest
+
+
+def _run(command: list[str], cwd: Path | None = None, timeout: int = 60) -> dict:
+    result = subprocess.run(command, cwd=cwd, capture_output=True, text=True, timeout=timeout)
+    return {
+        "command": command,
+        "returncode": result.returncode,
+        "stdout": result.stdout[-4000:],
+        "stderr": result.stderr[-4000:],
+        "passed": result.returncode == 0,
+    }
+
+
+def _fetch(url: str, timeout: int = 5) -> dict:
+    try:
+        with urllib.request.urlopen(url, timeout=timeout) as response:
+            body = response.read(20000).decode("utf-8", errors="replace")
+            return {"url": url, "status": response.status, "body": body, "passed": 200 <= response.status < 300}
+    except urllib.error.HTTPError as exc:
+        body = exc.read(20000).decode("utf-8", errors="replace")
+        return {"url": url, "status": exc.code, "body": body, "passed": False}
+    except Exception as exc:
+        return {"url": url, "error": str(exc), "passed": False}
+
+
+def validate_project(run_build: bool = False) -> dict:
+    """Validate current runtime readiness without launching heavy training."""
+    checks = []
+    catalog = get_catalog()
+    manifest = load_manifest()
+
+    checks.append({"name": "catalog_has_yolo_heatmap", "passed": "yolo.heatmap" in catalog["task_types"]})
+    checks.append({"name": "catalog_has_mmseg_31_algs", "passed": len(catalog["mmseg_algorithms"]) >= 31})
+    checks.append({"name": "weights_manifest_present", "passed": manifest.get("count", 0) >= 1})
+    checks.append({"name": "gpus_query", "passed": bool(get_gpus().get("available"))})
+    env_names = [item["name"] for item in get_conda_envs().get("envs", [])]
+    checks.append({"name": "seg_smp_env_exists", "passed": "seg_smp" in env_names})
+
+    smoke = _run(
+        [
+            "conda",
+            "run",
+            "-n",
+            "seg_smp",
+            "python",
+            "-c",
+            "import fastapi, uvicorn, torch, cv2, segmentation_models_pytorch, ultralytics, albumentations, mmengine, mmseg, mmcv; print(torch.__version__, torch.cuda.is_available())",
+        ],
+        cwd=settings.project_root,
+    )
+    checks.append({"name": "seg_smp_backend_smoke", "passed": smoke["passed"], "detail": smoke})
+
+    no_weight = _run(["bash", "scripts/check_no_weight_git.sh"], cwd=settings.project_root)
+    checks.append({"name": "no_weight_in_git", "passed": no_weight["passed"], "detail": no_weight})
+
+    if os.getenv("SEG_VALIDATE_LIVE", "1") == "1":
+        backend_url = os.getenv("SEG_VALIDATE_BACKEND_URL", "http://127.0.0.1:8010")
+        frontend_url = os.getenv("SEG_VALIDATE_FRONTEND_URL", "http://127.0.0.1:5173")
+        health = _fetch(f"{backend_url}/api/health")
+        datasets = _fetch(f"{backend_url}/api/datasets")
+        frontend = _fetch(frontend_url)
+        checks.append({"name": "live_backend_health", "passed": health["passed"] and '"ok":true' in health.get("body", "").replace(" ", ""), "detail": health})
+        checks.append({"name": "live_dataset_api", "passed": datasets["passed"] and datasets.get("body", "").lstrip().startswith("["), "detail": datasets})
+        checks.append({"name": "live_frontend_index", "passed": frontend["passed"] and "Seg Data Server" in frontend.get("body", ""), "detail": frontend})
+
+    if run_build:
+        tests = _run(["conda", "run", "-n", settings.backend_conda_env, "python", "-m", "pytest", "-q"], cwd=settings.project_root, timeout=120)
+        checks.append({"name": "backend_tests", "passed": tests["passed"], "detail": tests})
+        build = _run(["npm", "run", "build"], cwd=settings.project_root / "frontend", timeout=120)
+        checks.append({"name": "frontend_build", "passed": build["passed"], "detail": build})
+
+    passed = all(item["passed"] for item in checks)
+    return {
+        "agent": "validation_agent",
+        "passed": passed,
+        "checks": checks,
+    }
+
+
+def write_validation_report(path: Path, run_build: bool = False) -> dict:
+    result = validate_project(run_build=run_build)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+    return result

backend/app/catalog.py

@@ -103,6 +103,11 @@ def discover_datasets() -> list[dict[str, Any]]:
         if item.name == "All_Data_Record.json" or not data:
             continue
         candidates.append({"name": item.stem, "path": rel(item, root), "source": "mmseg_parameter"})
+    uploaded_root = settings.project_root / "var" / "uploads" / "datasets"
+    if uploaded_root.exists():
+        for item in sorted(uploaded_root.iterdir()):
+            if item.is_dir():
+                candidates.append({"name": item.name, "path": rel(item, settings.project_root), "source": "uploaded"})
     return candidates
 
 
@@ -137,4 +142,3 @@ def get_catalog() -> dict[str, Any]:
         "datasets": discover_datasets(),
         "weights": discover_weights_summary(),
     }
-

backend/app/config.py

@@ -46,7 +46,7 @@ def get_settings() -> Settings:
         log_dir=log_dir,
         weights_root=weights_root,
         task_conda_env=os.getenv("SEG_TASK_CONDA_ENV", "seg_smp"),
-        backend_conda_env=os.getenv("SEG_BACKEND_CONDA_ENV", "seg_server"),
+        backend_conda_env=os.getenv("SEG_BACKEND_CONDA_ENV", "seg_smp"),
         weight_mode=os.getenv("SEG_WEIGHT_MODE", "copy"),
         enable_shell_tasks=os.getenv("SEG_ENABLE_SHELL_TASKS", "1") == "1",
     )

backend/app/main.py

@@ -4,7 +4,7 @@ import asyncio
 import json
 from pathlib import Path
 
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, File, HTTPException, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse, StreamingResponse
 
@@ -13,9 +13,12 @@ from .catalog import get_catalog
 from .config import settings
 from .jobs import cancel_job, create_job
 from .modules.system.service import disk_usage, get_conda_envs, get_gpus, scan_results
+from .modules.dataset.service import create_dataset, list_uploaded_datasets, save_upload
 from .modules.weights.service import load_manifest, sync_weights, verify_weights
+from .agents.evaluation_agent import evaluate_project
+from .agents.validation_agent import validate_project
 from .paths import ensure_inside
-from .schemas import JobCreate, ProfileCreate, WeightSyncRequest
+from .schemas import DatasetCreate, JobCreate, ProfileCreate, WeightSyncRequest
 
 app = FastAPI(title="Seg Data Server Net", version="0.1.0")
 
@@ -60,6 +63,24 @@ def api_catalog() -> dict:
     return get_catalog()
 
 
+@app.get("/api/datasets")
+def api_datasets() -> list[dict]:
+    return list_uploaded_datasets()
+
+
+@app.post("/api/datasets")
+def api_create_dataset(dataset: DatasetCreate) -> dict:
+    return create_dataset(dataset.name, dataset.description)
+
+
+@app.post("/api/datasets/{dataset_name}/upload/{kind}")
+async def api_upload_dataset_files(dataset_name: str, kind: str, files: list[UploadFile] = File(...)) -> dict:
+    try:
+        return await save_upload(dataset_name, kind, files)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
 @app.get("/api/profiles")
 def api_profiles(kind: str | None = None) -> list[dict]:
     return db.list_profiles(kind)
@@ -170,3 +191,12 @@ def api_weight_sync(request: WeightSyncRequest) -> dict:
 def api_weight_verify() -> dict:
     return verify_weights()
 
+
+@app.get("/api/agents/evaluate")
+def api_agent_evaluate() -> dict:
+    return evaluate_project()
+
+
+@app.get("/api/agents/validate")
+def api_agent_validate(run_build: bool = False) -> dict:
+    return validate_project(run_build=run_build)

backend/app/modules/dataset/service.py

@@ -0,0 +1,132 @@
+from __future__ import annotations
+
+import json
+import re
+import shutil
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Iterable
+
+from fastapi import UploadFile
+
+from ...config import settings
+
+DATASET_KINDS = ("images", "labels", "masks")
+IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff"}
+
+
+def uploads_root() -> Path:
+    root = settings.project_root / "var" / "uploads" / "datasets"
+    root.mkdir(parents=True, exist_ok=True)
+    return root
+
+
+def slugify(value: str) -> str:
+    text = re.sub(r"[^A-Za-z0-9_.\-\u4e00-\u9fff]+", "_", value.strip())
+    return text.strip("._") or "dataset"
+
+
+def safe_filename(value: str | None) -> str:
+    original = Path(value or "upload.bin").name
+    suffix = Path(original).suffix.lower()
+    stem = slugify(Path(original).stem or "upload")
+    if suffix and re.fullmatch(r"\.[A-Za-z0-9]{1,12}", suffix):
+        return f"{stem}{suffix}"
+    return stem
+
+
+def dataset_dir(name: str) -> Path:
+    return uploads_root() / slugify(name)
+
+
+def metadata_path(name: str) -> Path:
+    return dataset_dir(name) / "dataset.json"
+
+
+def create_dataset(name: str, description: str = "") -> dict:
+    safe_name = slugify(name)
+    root = dataset_dir(safe_name)
+    for kind in DATASET_KINDS:
+        (root / kind).mkdir(parents=True, exist_ok=True)
+    meta = {
+        "name": safe_name,
+        "description": description,
+        "created_at": datetime.now(timezone.utc).isoformat(),
+        "root": str(root.relative_to(settings.project_root)),
+        "layout": {
+            "images": str((root / "images").relative_to(settings.project_root)),
+            "labels": str((root / "labels").relative_to(settings.project_root)),
+            "masks": str((root / "masks").relative_to(settings.project_root)),
+        },
+    }
+    metadata_path(safe_name).write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")
+    return describe_dataset(safe_name)
+
+
+def _load_meta(name: str) -> dict:
+    path = metadata_path(name)
+    if path.exists():
+        return json.loads(path.read_text(encoding="utf-8"))
+    return {"name": slugify(name), "description": "", "root": str(dataset_dir(name).relative_to(settings.project_root))}
+
+
+def _iter_files(root: Path) -> Iterable[Path]:
+    if not root.exists():
+        return []
+    return sorted(path for path in root.rglob("*") if path.is_file())
+
+
+def describe_dataset(name: str) -> dict:
+    safe_name = slugify(name)
+    root = dataset_dir(safe_name)
+    meta = _load_meta(safe_name)
+    counts = {}
+    samples = {}
+    for kind in sorted(DATASET_KINDS):
+        files = list(_iter_files(root / kind))
+        counts[kind] = len(files)
+        samples[kind] = [
+            {
+                "name": path.name,
+                "path": str(path.resolve()),
+                "relative_path": str(path.resolve().relative_to(settings.project_root)),
+                "size": path.stat().st_size,
+                "previewable": path.suffix.lower() in IMAGE_EXTS,
+            }
+            for path in files[:80]
+        ]
+    return {**meta, "counts": counts, "samples": samples}
+
+
+def list_uploaded_datasets() -> list[dict]:
+    root = uploads_root()
+    datasets = []
+    for item in sorted(root.iterdir()):
+        if item.is_dir():
+            datasets.append(describe_dataset(item.name))
+    return datasets
+
+
+async def save_upload(dataset: str, kind: str, files: list[UploadFile]) -> dict:
+    if kind not in DATASET_KINDS:
+        raise ValueError(f"unsupported dataset file kind: {kind}")
+    safe_name = slugify(dataset)
+    if not metadata_path(safe_name).exists():
+        create_dataset(safe_name)
+    target = dataset_dir(safe_name) / kind
+    target.mkdir(parents=True, exist_ok=True)
+    saved = []
+    for upload in files:
+        filename = safe_filename(upload.filename)
+        dst = target / filename
+        if dst.exists():
+            stem = dst.stem
+            suffix = dst.suffix
+            counter = 1
+            while dst.exists():
+                dst = target / f"{stem}_{counter}{suffix}"
+                counter += 1
+        with dst.open("wb") as handle:
+            shutil.copyfileobj(upload.file, handle)
+        saved.append({"name": dst.name, "relative_path": str(dst.relative_to(settings.project_root)), "size": dst.stat().st_size})
+    return {"dataset": describe_dataset(safe_name), "saved": saved}

backend/app/schemas.py

@@ -43,3 +43,7 @@ class WeightSyncRequest(BaseModel):
     hash_files: bool = True
     skip_existing: bool = True
 
+
+class DatasetCreate(BaseModel):
+    name: str
+    description: str = ""

backend/tests/test_agents.py

@@ -0,0 +1,14 @@
+from app.agents.evaluation_agent import evaluate_project
+from app.agents.validation_agent import validate_project
+
+
+def test_evaluation_agent_returns_checks():
+    result = evaluate_project()
+    assert result["agent"] == "evaluation_suggestion_agent"
+    assert result["checks"]
+
+
+def test_validation_agent_lightweight():
+    result = validate_project(run_build=False)
+    assert result["agent"] == "validation_agent"
+    assert any(item["name"] == "catalog_has_yolo_heatmap" for item in result["checks"])

backend/tests/test_dataset_service.py

@@ -0,0 +1,13 @@
+from app.modules.dataset.service import create_dataset, describe_dataset
+
+
+def test_create_dataset_layout(tmp_path, monkeypatch):
+    from types import SimpleNamespace
+    from app.modules.dataset import service
+
+    monkeypatch.setattr(service, "settings", SimpleNamespace(project_root=tmp_path))
+    created = create_dataset("case 01", "demo")
+    assert created["name"] == "case_01"
+    assert created["counts"] == {"images": 0, "labels": 0, "masks": 0}
+    described = describe_dataset("case_01")
+    assert described["layout"]["images"].endswith("images")

frontend/src/main.tsx

@@ -3,8 +3,10 @@ import { createRoot } from "react-dom/client";
 import {
   Activity,
   BarChart3,
+  Boxes,
   Cpu,
   Database,
+  FileImage,
   FileSearch,
   Gauge,
   HardDrive,
@@ -15,11 +17,12 @@ import {
   Square,
   Terminal,
   UploadCloud,
+  Wand2,
   Zap
 } from "lucide-react";
 import "./styles.css";
 
-const API_BASE = import.meta.env.VITE_API_BASE ?? "http://localhost:8000";
+const API_BASE = import.meta.env.VITE_API_BASE ?? "http://localhost:8010";
 
 type Job = {
   id: string;
@@ -42,6 +45,22 @@ type Catalog = {
   weights: { count: number; total_bytes: number; updated_at?: string };
 };
 
+type UploadedDataset = {
+  name: string;
+  description?: string;
+  counts: { images: number; labels: number; masks: number };
+  samples: Record<string, Array<{ name: string; relative_path: string; size: number; previewable: boolean }>>;
+};
+
+type ResultItem = {
+  name: string;
+  path: string;
+  relative_path: string;
+  size: number;
+  modified: number;
+  kind: string;
+};
+
 type GpuPayload = {
   available: boolean;
   gpus: Array<{
@@ -66,6 +85,13 @@ async function api<T>(path: string, init?: RequestInit): Promise<T> {
 
 const defaultParams: Record<string, Record<string, unknown>> = {
   "mock.echo": { message: "hello from Seg Data Server" },
+  "dataset.rename": { input_dir: "../DataSet_Own", prefix: "image" },
+  "dataset.to_png": { input_dir: "../DataSet_Own", output_dir: "../DataSet_Own_png" },
+  "dataset.resize": { input_dir: "../DataSet_Own", output_dir: "../DataSet_Own_resize", size: "512x512" },
+  "dataset.pair": { image_dir: "../DataSet_Own/images", label_dir: "../DataSet_Own/labels" },
+  "dataset.rebuild_labels": { label_dir: "../DataSet_Own/labels", output_dir: "../DataSet_Own/rebuilt_labels" },
+  "dataset.stack": { image_dir: "../DataSet_Own/images", mask_dir: "../DataSet_Own/masks", output_dir: "../DataSet_Own/stacked" },
+  "dataset.stitch": { input_dir: "../DataSet_Own/stacked", output_dir: "../DataSet_Own/stitch" },
   "dataset.video_frames": { video: "../Seg_Predict_Own_Video_V2/LC_Video_1.mp4", interval: 0.5, resize: "1920x1080" },
   "segmodel.train": { architecture: "Unet" },
   "segmodel.predict": { architecture: "Unet", run_choice: 1 },
@@ -79,6 +105,17 @@ const defaultParams: Record<string, Record<string, unknown>> = {
   "analysis.all": { input_dir: "../BestMode_Predict_Results_DataSet_Public", output_dir: "./", dataset_choice: 1 }
 };
 
+const taskLabels: Record<string, string> = {
+  "dataset.rename": "重命名",
+  "dataset.to_png": "转 PNG",
+  "dataset.resize": "Resize",
+  "dataset.pair": "图片/Label 配对",
+  "dataset.rebuild_labels": "重建 Label",
+  "dataset.stack": "透明叠加",
+  "dataset.stitch": "拼接检查",
+  "dataset.video_frames": "视频抽帧"
+};
+
 function formatBytes(value?: number) {
   if (!value) return "0 B";
   const units = ["B", "KB", "MB", "GB", "TB"];
@@ -95,21 +132,24 @@ function useData() {
   const [catalog, setCatalog] = useState<Catalog | null>(null);
   const [gpus, setGpus] = useState<GpuPayload | null>(null);
   const [jobs, setJobs] = useState<Job[]>([]);
-  const [results, setResults] = useState<Array<Record<string, unknown>>>([]);
+  const [results, setResults] = useState<ResultItem[]>([]);
+  const [datasets, setDatasets] = useState<UploadedDataset[]>([]);
   const [error, setError] = useState<string>("");
 
   async function refresh() {
     try {
-      const [catalogNext, gpusNext, jobsNext, resultsNext] = await Promise.all([
+      const [catalogNext, gpusNext, jobsNext, resultsNext, datasetsNext] = await Promise.all([
         api<Catalog>("/api/catalog"),
         api<GpuPayload>("/api/system/gpus"),
         api<Job[]>("/api/jobs"),
-        api<Array<Record<string, unknown>>>("/api/results")
+        api<ResultItem[]>("/api/results"),
+        api<UploadedDataset[]>("/api/datasets")
       ]);
       setCatalog(catalogNext);
       setGpus(gpusNext);
       setJobs(jobsNext);
       setResults(resultsNext.slice(0, 80));
+      setDatasets(datasetsNext);
       setError("");
     } catch (err) {
       setError(String(err));
@@ -122,7 +162,7 @@ function useData() {
     return () => window.clearInterval(timer);
   }, []);
 
-  return { catalog, gpus, jobs, results, error, refresh };
+  return { catalog, gpus, jobs, results, datasets, error, refresh };
 }
 
 function StatusPill({ status }: { status: string }) {
@@ -130,12 +170,16 @@ function StatusPill({ status }: { status: string }) {
 }
 
 function App() {
-  const { catalog, gpus, jobs, results, error, refresh } = useData();
+  const { catalog, gpus, jobs, results, datasets, error, refresh } = useData();
   const [taskType, setTaskType] = useState("mock.echo");
   const [params, setParams] = useState(JSON.stringify(defaultParams["mock.echo"], null, 2));
   const [selectedJob, setSelectedJob] = useState<Job | null>(null);
   const [log, setLog] = useState("");
   const [busy, setBusy] = useState(false);
+  const [datasetName, setDatasetName] = useState("demo_dataset");
+  const [datasetDescription, setDatasetDescription] = useState("");
+  const [uploadKind, setUploadKind] = useState<"images" | "labels" | "masks">("images");
+  const [uploadFiles, setUploadFiles] = useState<FileList | null>(null);
 
   const runningCount = jobs.filter((job) => job.status === "running").length;
   const successCount = jobs.filter((job) => job.status === "success").length;
@@ -152,11 +196,18 @@ function App() {
     };
   }, [catalog]);
 
+  const datasetOps = taskGroups.dataset.filter((task) => task in taskLabels);
+
   function pickTask(next: string) {
     setTaskType(next);
     setParams(JSON.stringify(defaultParams[next] ?? {}, null, 2));
   }
 
+  function pickDatasetTask(next: string) {
+    pickTask(next);
+    window.location.hash = "jobs";
+  }
+
   async function createJob() {
     setBusy(true);
     try {
@@ -183,6 +234,36 @@ function App() {
     }
   }
 
+  async function createDataset() {
+    setBusy(true);
+    try {
+      await api("/api/datasets", {
+        method: "POST",
+        body: JSON.stringify({ name: datasetName, description: datasetDescription })
+      });
+      await refresh();
+    } finally {
+      setBusy(false);
+    }
+  }
+
+  async function uploadDatasetFiles() {
+    if (!uploadFiles || uploadFiles.length === 0) return;
+    setBusy(true);
+    try {
+      const body = new FormData();
+      Array.from(uploadFiles).forEach((file) => body.append("files", file));
+      const res = await fetch(`${API_BASE}/api/datasets/${encodeURIComponent(datasetName)}/upload/${uploadKind}`, {
+        method: "POST",
+        body
+      });
+      if (!res.ok) throw new Error(await res.text());
+      await refresh();
+    } finally {
+      setBusy(false);
+    }
+  }
+
   async function inspectJob(job: Job) {
     const detail = await api<Job>(`/api/jobs/${job.id}`);
     setSelectedJob(detail);
@@ -214,6 +295,7 @@ function App() {
         </div>
         <nav>
           <a href="#jobs"><Terminal size={18} />任务</a>
+          <a href="#datasets"><Boxes size={18} />数据集</a>
           <a href="#gpu"><Cpu size={18} />GPU</a>
           <a href="#weights"><HardDrive size={18} />权重</a>
           <a href="#results"><BarChart3 size={18} />结果</a>
@@ -251,8 +333,8 @@ function App() {
           </div>
           <div className="metric">
             <Database size={20} />
-            <span>数据集</span>
+            <span>上传集</span>
-            <strong>{catalog?.datasets.length ?? 0}</strong>
+            <strong>{datasets.length}</strong>
           </div>
         </section>
 
@@ -305,6 +387,82 @@ function App() {
           </div>
         </section>
 
+        <section className="grid two" id="datasets">
+          <div className="panel">
+            <div className="panelHead">
+              <div>
+                <p className="eyebrow">Dataset Bench</p>
+                <h2>数据集、Label、Mask 上传</h2>
+              </div>
+              <Database size={22} />
+            </div>
+            <div className="datasetForm">
+              <label className="field compact">
+                <span>数据集名称</span>
+                <input value={datasetName} onChange={(event) => setDatasetName(event.target.value)} />
+              </label>
+              <label className="field compact">
+                <span>说明</span>
+                <input value={datasetDescription} onChange={(event) => setDatasetDescription(event.target.value)} />
+              </label>
+              <div className="segmented">
+                {(["images", "labels", "masks"] as const).map((kind) => (
+                  <button key={kind} className={uploadKind === kind ? "active" : ""} onClick={() => setUploadKind(kind)}>
+                    {kind}
+                  </button>
+                ))}
+              </div>
+              <label className="drop">
+                <UploadCloud size={24} />
+                <span>{uploadFiles?.length ? `${uploadFiles.length} files selected` : "选择图片、label 或 mask 文件"}</span>
+                <input multiple type="file" accept="image/*,.txt,.json,.yaml,.yml" onChange={(event) => setUploadFiles(event.target.files)} />
+              </label>
+              <div className="buttonRow">
+                <button className="primary" disabled={busy} onClick={createDataset}><Boxes size={17} />创建</button>
+                <button className="primary secondary" disabled={busy || !uploadFiles?.length} onClick={uploadDatasetFiles}><UploadCloud size={17} />上传</button>
+              </div>
+              <div className="opGrid">
+                {datasetOps.map((task) => (
+                  <button key={task} type="button" onClick={() => pickDatasetTask(task)}>
+                    <Wand2 size={16} />
+                    <span>{taskLabels[task]}</span>
+                  </button>
+                ))}
+              </div>
+            </div>
+          </div>
+
+          <div className="panel">
+            <div className="panelHead">
+              <div>
+                <p className="eyebrow">Files</p>
+                <h2>数据集浏览</h2>
+              </div>
+              <FileImage size={22} />
+            </div>
+            <div className="datasetList">
+              {datasets.map((dataset) => (
+                <div className="datasetCard" key={dataset.name}>
+                  <div className="datasetCardHead">
+                    <strong>{dataset.name}</strong>
+                    <span>{dataset.counts.images} image · {dataset.counts.labels} label · {dataset.counts.masks} mask</span>
+                  </div>
+                  <div className="sampleStrip">
+                    {["images", "labels", "masks"].flatMap((kind) =>
+                      (dataset.samples[kind] ?? []).slice(0, 4).map((sample) => (
+                        <a key={`${kind}-${sample.relative_path}`} href={`${API_BASE}/api/artifacts/${sample.relative_path}`} target="_blank" rel="noreferrer">
+                          <span>{kind}</span>
+                          <small>{sample.name}</small>
+                        </a>
+                      ))
+                    )}
+                  </div>
+                </div>
+              ))}
+            </div>
+          </div>
+        </section>
+
         <section className="grid three">
           <div className="panel" id="gpu">
             <div className="panelHead">
@@ -356,6 +514,46 @@ function App() {
           </div>
         </section>
 
+        <section className="grid three">
+          <div className="panel insight">
+            <div className="panelHead">
+              <div>
+                <p className="eyebrow">Segmentation</p>
+                <h2>分割结果</h2>
+              </div>
+              <Wand2 size={22} />
+            </div>
+            <ResultPreview results={results.filter((item) => /predict|mask|comparison|prediction/i.test(item.relative_path) && ["png", "jpg", "jpeg"].includes(item.kind)).slice(0, 6)} />
+          </div>
+          <div className="panel insight">
+            <div className="panelHead">
+              <div>
+                <p className="eyebrow">Heatmap</p>
+                <h2>YOLO 热度图</h2>
+              </div>
+              <Zap size={22} />
+            </div>
+            <ResultPreview results={results.filter((item) => /heat|cam|grad/i.test(item.relative_path) && ["png", "jpg", "jpeg"].includes(item.kind)).slice(0, 6)} />
+          </div>
+          <div className="panel insight">
+            <div className="panelHead">
+              <div>
+                <p className="eyebrow">Curves</p>
+                <h2>Loss / 指标</h2>
+              </div>
+              <BarChart3 size={22} />
+            </div>
+            <div className="resultList tight">
+              {results.filter((item) => /loss|metric|miou|iou|csv|curve/i.test(item.relative_path)).slice(0, 10).map((item) => (
+                <a key={item.path} href={`${API_BASE}/api/artifacts/${item.relative_path}`} target="_blank" rel="noreferrer">
+                  <span>{item.name}</span>
+                  <small>{formatBytes(item.size)}</small>
+                </a>
+              ))}
+            </div>
+          </div>
+        </section>
+
         <section className="grid two">
           <div className="panel logPanel">
             <div className="panelHead">
@@ -380,9 +578,9 @@ function App() {
             </div>
             <div className="resultList">
               {results.map((item) => (
-                <a key={String(item.path)} href={`${API_BASE}/api/artifacts/${item.relative_path}`} target="_blank" rel="noreferrer">
+                <a key={item.path} href={`${API_BASE}/api/artifacts/${item.relative_path}`} target="_blank" rel="noreferrer">
-                  <span>{String(item.name)}</span>
+                  <span>{item.name}</span>
-                  <small>{formatBytes(Number(item.size))}</small>
+                  <small>{formatBytes(item.size)}</small>
                 </a>
               ))}
             </div>
@@ -393,6 +591,22 @@ function App() {
   );
 }
 
+function ResultPreview({ results }: { results: ResultItem[] }) {
+  if (!results.length) {
+    return <p className="muted">暂无结果，运行预测、热度图或分析任务后会自动出现在这里。</p>;
+  }
+  return (
+    <div className="previewGrid">
+      {results.map((item) => (
+        <a key={item.path} href={`${API_BASE}/api/artifacts/${item.relative_path}`} target="_blank" rel="noreferrer">
+          <img src={`${API_BASE}/api/artifacts/${item.relative_path}`} alt={item.name} />
+          <span>{item.name}</span>
+        </a>
+      ))}
+    </div>
+  );
+}
+
 createRoot(document.getElementById("root")!).render(
   <React.StrictMode>
     <App />

frontend/src/styles.css

@@ -32,6 +32,11 @@ button, textarea, select {
   font: inherit;
 }
 
+input {
+  font: inherit;
+  color: var(--ink);
+}
+
 button {
   border: 0;
   color: inherit;
@@ -154,6 +159,10 @@ h2 {
   font-weight: 700;
 }
 
+.primary.secondary {
+  background: var(--cyan);
+}
+
 .primary:disabled, .iconButton:disabled {
   opacity: 0.5;
   cursor: not-allowed;
@@ -283,6 +292,159 @@ textarea {
   font-size: 13px;
 }
 
+.field.compact {
+  margin-top: 0;
+}
+
+.field input {
+  height: 38px;
+  padding: 0 10px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: var(--field);
+}
+
+.datasetForm {
+  display: grid;
+  gap: 12px;
+}
+
+.segmented {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 6px;
+  padding: 4px;
+  border: 1px solid var(--line);
+  background: #101310;
+  border-radius: 7px;
+}
+
+.segmented button {
+  height: 34px;
+  border-radius: 5px;
+  background: transparent;
+  color: var(--muted);
+}
+
+.segmented button.active {
+  background: var(--green);
+  color: #0b0d0b;
+  font-weight: 760;
+}
+
+.drop {
+  min-height: 118px;
+  display: grid;
+  place-items: center;
+  gap: 8px;
+  position: relative;
+  border: 1px dashed #526052;
+  border-radius: 8px;
+  background: rgba(13, 16, 13, 0.8);
+  color: var(--muted);
+  text-align: center;
+  overflow: hidden;
+}
+
+.drop input {
+  position: absolute;
+  inset: 0;
+  opacity: 0;
+  cursor: pointer;
+}
+
+.buttonRow {
+  display: flex;
+  gap: 10px;
+}
+
+.opGrid {
+  display: grid;
+  grid-template-columns: repeat(4, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.opGrid button {
+  min-width: 0;
+  height: 42px;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  gap: 6px;
+  padding: 0 8px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+  color: var(--muted);
+  font-size: 12px;
+}
+
+.opGrid button:hover {
+  color: var(--ink);
+  border-color: var(--green);
+}
+
+.opGrid span {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.datasetList {
+  display: grid;
+  gap: 10px;
+  max-height: 420px;
+  overflow: auto;
+}
+
+.datasetCard {
+  padding: 12px;
+  border: 1px solid var(--line);
+  border-radius: 7px;
+  background: #101310;
+}
+
+.datasetCardHead {
+  display: flex;
+  justify-content: space-between;
+  gap: 12px;
+  margin-bottom: 10px;
+}
+
+.datasetCardHead span {
+  color: var(--muted);
+  font-size: 12px;
+}
+
+.sampleStrip {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.sampleStrip a {
+  min-width: 0;
+  padding: 8px;
+  border-radius: 5px;
+  border: 1px solid var(--line);
+  text-decoration: none;
+  color: var(--ink);
+  background: #0b0d0b;
+}
+
+.sampleStrip span,
+.sampleStrip small {
+  display: block;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.sampleStrip span {
+  color: var(--green);
+  font-size: 11px;
+}
+
 .jobList, .resultList {
   display: grid;
   gap: 8px;
@@ -382,11 +544,53 @@ meter {
   border-color: var(--green);
 }
 
+.resultList.tight {
+  max-height: 290px;
+}
+
+.insight {
+  min-height: 350px;
+}
+
+.previewGrid {
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 10px;
+}
+
+.previewGrid a {
+  min-width: 0;
+  border: 1px solid var(--line);
+  border-radius: 7px;
+  overflow: hidden;
+  color: var(--ink);
+  text-decoration: none;
+  background: #0b0d0b;
+}
+
+.previewGrid img {
+  display: block;
+  width: 100%;
+  aspect-ratio: 16 / 10;
+  object-fit: cover;
+  background: #060806;
+}
+
+.previewGrid span {
+  display: block;
+  padding: 8px;
+  color: var(--muted);
+  font-size: 12px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
 @media (max-width: 1180px) {
   body { min-width: 960px; }
   .shell { grid-template-columns: 220px 1fr; }
   .taskColumns { grid-template-columns: repeat(3, minmax(0, 1fr)); }
+  .opGrid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
   .grid.three { grid-template-columns: 1fr; }
   .grid.two { grid-template-columns: 1fr; }
 }
-

scripts/run_agents.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "backend"))
+
+from app.agents.evaluation_agent import evaluate_project  # noqa: E402
+from app.agents.validation_agent import validate_project  # noqa: E402
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Run local evaluation and validation agents.")
+    parser.add_argument("--build", action="store_true", help="also run pytest and frontend build")
+    parser.add_argument("--out", default="var/agent_reports/latest.json")
+    args = parser.parse_args()
+    report = {
+        "evaluation": evaluate_project(),
+        "validation": validate_project(run_build=args.build),
+    }
+    out = ROOT / args.out
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(json.dumps(report, ensure_ascii=False, indent=2))
+    if not report["validation"]["passed"]:
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()
+

scripts/run_backend.sh

@@ -2,10 +2,9 @@
 set -euo pipefail
 
 ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
-BACKEND_ENV="${SEG_BACKEND_CONDA_ENV:-seg_server}"
+BACKEND_ENV="${SEG_BACKEND_CONDA_ENV:-seg_smp}"
 HOST="${SEG_BACKEND_HOST:-0.0.0.0}"
-PORT="${SEG_BACKEND_PORT:-8000}"
+PORT="${SEG_BACKEND_PORT:-8010}"
 
 cd "${ROOT_DIR}"
 exec conda run -n "${BACKEND_ENV}" uvicorn app.main:app --app-dir backend --host "${HOST}" --port "${PORT}" --reload
-