Improve train utility functionality

.dockerignore

@@ -0,0 +1,17 @@
+.git
+.agents
+.venv
+.pytest_cache
+**/__pycache__
+*.py[cod]
+.coverage
+build
+dist
+datasets
+models
+runs
+mlflow
+mlruns
+mlflow.db*
+*.pt
+.DS_Store

Dockerfile

@@ -9,14 +9,19 @@ RUN pip install --no-cache-dir uv==0.10.6
 # Set working directory
 WORKDIR /workspace
 
-# Install our dependencies into the system python without resolving/syncing
-# which would remove packages installed by ultralytics base image (like tensorrt).
-COPY pyproject.toml README.md ./
-RUN uv pip install --system fastapi mlflow uvicorn websockets
+ENV ULTRALYTICS_SAFE_LOAD=1
 
-# Copy source code and install the project
+# Keep the training/GPU stack supplied by the Ultralytics image, while installing
+# the remaining application dependencies at the exact versions recorded in uv.lock.
+COPY pyproject.toml uv.lock README.md ./
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv export --locked --no-dev --group build --no-emit-project --prune ultralytics \
+        --output-file /tmp/requirements.lock && \
+    uv pip install --system --requirement /tmp/requirements.lock
+
+# Copy source code and install only the project itself without re-resolving deps.
 COPY src ./src
-RUN uv pip install --system -e .
+RUN uv pip install --system --no-deps --no-build-isolation .
 
 # Expose Web UI port and MLflow port
 EXPOSE 8000

README.md

@@ -42,14 +42,17 @@ Ultralytics скачает веса. Пользовательские модел
 docker compose up --build
 ```
 
-WebUI будет доступен по `http://127.0.0.1:8000`. Compose намеренно публикует порт
-только на loopback. Не заменяйте адрес на `0.0.0.0` без аутентифицирующего reverse
-proxy: API позволяет запускать и останавливать ресурсоёмкие задачи.
+WebUI будет доступен по `http://127.0.0.1:8000`, а MLflow — по
+`http://127.0.0.1:5000`. Compose намеренно публикует оба порта только на loopback и
+хранит состояние MLflow в `./mlflow`. Не заменяйте адреса на `0.0.0.0` без
+аутентифицирующего reverse proxy: API позволяет запускать и останавливать
+ресурсоёмкие задачи.
 
 Для NVIDIA GPU раскомментируйте секцию `deploy.resources.reservations.devices` в
-`docker-compose.yml`. Образ устанавливает зафиксированные в `uv.lock` зависимости;
-для другого варианта PyTorch используйте отдельно сгенерированный и проверенный
-lock-файл.
+`docker-compose.yml`. Web/MLflow-зависимости устанавливаются в версиях из
+`uv.lock`, а PyTorch, Ultralytics и GPU runtime предоставляет базовый образ
+Ultralytics. Для полностью воспроизводимой production-сборки дополнительно
+зафиксируйте базовый образ по digest.
 
 ## Разрешённые пути
 
@@ -124,6 +127,7 @@ trainer и сохраняется при запуске Ultralytics DDP на н
 Проверка интеграции на минимальных датасетах для всех пяти задач:
 
 ```bash
+uv run scripts/create_yolo26_smoke_datasets.py
 uv run scripts/run_yolo26_smoke_training.py --mlflow
 uv run scripts/verify_mlflow_smoke.py
 ```

docker-compose.yml

@@ -5,19 +5,49 @@ services:
     image: yolo-train-webui:latest
     ports:
       # The training API has no built-in user accounts, so expose it locally only.
-      - "0.0.0.0:8000:8000"
+      - "127.0.0.1:8000:8000"
     volumes:
       - ./datasets:/workspace/datasets
       - ./runs:/workspace/runs
       - ./models:/workspace/models
       - ./models/.config:/root/.config/Ultralytics
+    environment:
+      # Use the Compose MLflow service so metadata and artifacts survive container recreation.
+      YOLO_WEBUI_MLFLOW_TRACKING_URI: http://mlflow:5000
+    depends_on:
+      - mlflow
     # Uncomment the block below on Linux with NVIDIA GPU to pass the graphics card into the container:
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: all
+    #           capabilities: [gpu]
     ipc: host
     restart: unless-stopped
+
+  mlflow:
+    build:
+      context: .
+    image: yolo-train-webui:latest
+    command:
+      - mlflow
+      - server
+      - --backend-store-uri
+      - sqlite:////mlflow/mlflow.db
+      - --artifacts-destination
+      - /mlflow/artifacts
+      - --host
+      - 0.0.0.0
+      - --port
+      - "5000"
+      - --workers
+      - "1"
+      - --allowed-hosts
+      - localhost:5000,127.0.0.1:5000,mlflow:5000
+    ports:
+      - "127.0.0.1:5000:5000"
+    volumes:
+      - ./mlflow:/mlflow
+    restart: unless-stopped

pyproject.toml

@@ -16,13 +16,16 @@ dependencies = [
 yolo-train-webui = "yolo_webui.app:main"
 
 [dependency-groups]
+build = [
+    "hatchling==1.27.0",
+]
 dev = [
     "pytest>=8.3",
     "httpx",
 ]
 
 [build-system]
-requires = ["hatchling"]
+requires = ["hatchling==1.27.0"]
 build-backend = "hatchling.build"
 
 [tool.hatch.build.targets.wheel]
@@ -31,4 +34,3 @@ packages = ["src/yolo_webui"]
 [tool.pytest.ini_options]
 addopts = "-q"
 testpaths = ["tests"]
-

src/yolo_webui/app.py

@@ -20,6 +20,7 @@ from fastapi.staticfiles import StaticFiles
 import uvicorn
 
 from yolo_webui.config import TrainingConfig
+from yolo_webui.export_runner import ExportConfig
 from yolo_webui.trainer import TrainingRunner
 
 # Set up logging
@@ -49,6 +50,17 @@ class LiveState:
         self.stop_requested = False
         self.last_event_kind = None
 
+    def snapshot(self) -> dict[str, Any]:
+        """Return a detached snapshot safe to serialize after releasing a lock."""
+        return {
+            "status": self.status,
+            "epoch": self.epoch,
+            "total_epochs": self.total_epochs,
+            "logs": list(self.logs),
+            "metrics": [dict(metric) for metric in self.metrics],
+            "output_dir": self.output_dir,
+        }
+
 
 class TrainingManager:
     """Manages the background training subprocess and WebSocket clients."""
@@ -355,6 +367,51 @@ def get_sessions_dir() -> Path:
     return path
 
 
+def write_json_atomic(path: Path, data: dict[str, Any]) -> None:
+    """Replace a JSON file atomically so concurrent readers never see partial data."""
+    temp_path: Path | None = None
+    try:
+        with tempfile.NamedTemporaryFile(
+            "w",
+            prefix=f".{path.name}.",
+            suffix=".tmp",
+            dir=path.parent,
+            delete=False,
+            encoding="utf-8",
+        ) as temp_file:
+            temp_path = Path(temp_file.name)
+            json.dump(data, temp_file, ensure_ascii=False, indent=2)
+        os.replace(temp_path, path)
+        temp_path = None
+    finally:
+        if temp_path is not None:
+            try:
+                temp_path.unlink()
+            except FileNotFoundError:
+                pass
+
+
+def parse_training_config(config_data: dict[str, Any]) -> TrainingConfig:
+    config = TrainingConfig.from_dict(config_data)
+    config.validate()
+    return config
+
+
+def parse_export_config(config_data: dict[str, Any]) -> dict[str, Any]:
+    config = ExportConfig.from_mapping(config_data)
+    return {
+        "model": str(config.model),
+        "format": config.export_format,
+        "imgsz": config.imgsz,
+        "half": config.half,
+        "int8": config.int8,
+        "dynamic": config.dynamic,
+        "simplify": config.simplify,
+        "batch": config.batch,
+        "workspace": config.workspace,
+    }
+
+
 def get_session_path(name: str, *, allow_last_run: bool = True) -> Path:
     if SESSION_NAME_PATTERN.fullmatch(name) is None:
         raise HTTPException(
@@ -432,13 +489,13 @@ async def list_models():
         try:
             for path in runs_dir.rglob("*.pt"):
                 items.append({
-                    "name": f"{path.parent.parent.parent.name}/{path.parent.parent.name}/{path.name}",
+                    "name": path.relative_to(runs_dir).as_posix(),
                     "path": str(path.absolute()),
                     "source": "runs"
                 })
             for path in runs_dir.rglob("*.pth"):
                 items.append({
-                    "name": f"{path.parent.parent.parent.name}/{path.parent.parent.name}/{path.name}",
+                    "name": path.relative_to(runs_dir).as_posix(),
                     "path": str(path.absolute()),
                     "source": "runs"
                 })
@@ -464,8 +521,13 @@ async def load_session(name: str):
 async def save_session(name: str, config_data: dict[str, Any]):
     file_path = get_session_path(name, allow_last_run=False)
     try:
-        with file_path.open("w", encoding="utf-8") as f:
-            json.dump(config_data, f, ensure_ascii=False, indent=2)
+        config = parse_training_config(config_data)
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=f"Некорректная конфигурация: {exc}")
+    try:
+        write_json_atomic(file_path, config.to_dict())
         return {"message": "Сессия успешно сохранена."}
     except Exception as exc:
         raise HTTPException(status_code=500, detail=f"Не удалось сохранить сессию: {exc}")
@@ -486,40 +548,32 @@ async def delete_session(name: str):
 @app.get("/api/train/status")
 async def get_status():
     with manager._lock:
-        return {
-            "status": manager.state.status,
-            "epoch": manager.state.epoch,
-            "total_epochs": manager.state.total_epochs,
-            "output_dir": manager.state.output_dir,
-            "metrics": manager.state.metrics,
-            "logs": manager.state.logs,
-        }
+        return manager.state.snapshot()
 
 
 @app.post("/api/train/start")
 async def start_training(config_data: dict[str, Any]):
     try:
-        config = TrainingConfig.from_dict(config_data)
-        config.validate()
+        config = parse_training_config(config_data)
     except ValueError as exc:
         raise HTTPException(status_code=400, detail=str(exc))
     except Exception as exc:
         raise HTTPException(status_code=400, detail=f"Некорректная конфигурация: {exc}")
 
-    # Auto-save last configuration on start
+    try:
+        manager.start_training(config)
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+    # Only a configuration accepted for execution becomes the last run.
     try:
         sessions_dir = get_sessions_dir()
         last_run_path = sessions_dir / "last_run.json"
-        with last_run_path.open("w", encoding="utf-8") as f:
-            json.dump(config_data, f, ensure_ascii=False, indent=2)
+        write_json_atomic(last_run_path, config.to_dict())
     except Exception as exc:
         logger.error(f"Failed to auto-save last run: {exc}")
 
-    try:
-        manager.start_training(config)
     return {"message": "Обучение запущено."}
-    except ValueError as exc:
-        raise HTTPException(status_code=400, detail=str(exc))
 
 
 @app.post("/api/train/stop")
@@ -572,8 +626,10 @@ class ExportManager:
                 future = loop.create_task(coroutine)
             else:
                 future = asyncio.run_coroutine_threadsafe(coroutine, loop)
+            future.add_done_callback(TrainingManager._log_broadcast_failure)
         except Exception:
             coroutine.close()
+            logger.exception("Failed to schedule export WebSocket broadcast")
 
     async def _send_payload(self, payload: str) -> None:
         broadcast_lock = self._broadcast_lock
@@ -621,19 +677,28 @@ class ExportManager:
         with self._lock:
             if self.state.status not in ("preparing", "exporting"):
                 return
+
+            process = self._process
+            if process is not None:
+                if process.poll() is not None:
+                    # The subprocess has already completed; let its real return code
+                    # determine the final status instead of relabeling it cancelled.
+                    return
+                try:
+                    process.terminate()
+                except Exception:
+                    logger.warning("Failed to terminate export subprocess", exc_info=True)
+                    return
+
             self.state.status = "stopping"
             self.state.stop_requested = True
-            if self._process is not None:
-                try:
-                    self._process.terminate()
-                except Exception:
-                    pass
         
         self.broadcast({"type": "status", "status": self.state.status})
         self.add_log("Запрошена остановка экспорта...", "warning")
 
     def _run_subprocess(self, config_data: dict[str, Any]) -> None:
         temp_config_path = None
+        process: subprocess.Popen[str] | None = None
         try:
             with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False, encoding="utf-8") as f:
                 json.dump(config_data, f)
@@ -641,17 +706,29 @@ class ExportManager:
 
             cmd = [sys.executable, "-u", "-m", "yolo_webui.export_runner", temp_config_path]
             with self._lock:
-                self._process = subprocess.Popen(
+                if self.state.stop_requested:
+                    cancelled_before_start = True
+                else:
+                    cancelled_before_start = False
+                    process = subprocess.Popen(
                         cmd,
                         stdout=subprocess.PIPE,
                         stderr=subprocess.STDOUT,
                         text=True,
                         bufsize=1,
                     )
+                    self._process = process
+
+            if cancelled_before_start:
+                self._finalize_process_result(0)
+                return
+
+            if process is None or process.stdout is None:
+                raise RuntimeError("Не удалось открыть stdout процесса экспорта.")
 
             buffer = ""
             while True:
-                char = self._process.stdout.read(1)
+                char = process.stdout.read(1)
                 if not char:
                     if buffer:
                         self._handle_subprocess_line(buffer)
@@ -664,25 +741,27 @@ class ExportManager:
                 else:
                     buffer += char
 
-            self._process.wait()
-            rc = self._process.returncode
+            process.wait()
+            rc = process.returncode
             
             with self._lock:
+                if self._process is process:
                     self._process = None
                 
             self._finalize_process_result(rc)
 
         except Exception as exc:
             logger.exception("Error in export process thread:")
-            if self._process is not None:
+            if process is not None:
                 try:
-                    if self._process.poll() is None:
-                        self._process.kill()
-                    self._process.wait(timeout=5)
+                    if process.poll() is None:
+                        process.kill()
+                    process.wait(timeout=5)
                 except Exception:
                     pass
             
             with self._lock:
+                if self._process is process:
                     self._process = None
                 self.state.status = "failed"
             self.add_log(f"Внутренняя ошибка менеджера: {exc}", "error")
@@ -703,8 +782,12 @@ class ExportManager:
             return
 
         if line_str == "__YOLO_WEBUI_READY__":
+            status_changed = False
             with self._lock:
+                if self.state.status == "preparing":
                     self.state.status = "exporting"
+                    status_changed = True
+            if status_changed:
                 self.broadcast({"type": "status", "status": "exporting"})
         elif line_str.startswith("__YOLO_WEBUI_RESULT__:"):
             with self._lock:
@@ -738,16 +821,18 @@ export_manager = ExportManager()
 @app.get("/api/export/status")
 async def get_export_status():
     with export_manager._lock:
+        snapshot = export_manager.state.snapshot()
         return {
-            "status": export_manager.state.status,
-            "output_dir": export_manager.state.output_dir,
-            "logs": export_manager.state.logs,
+            "status": snapshot["status"],
+            "output_dir": snapshot["output_dir"],
+            "logs": snapshot["logs"],
         }
 
 @app.post("/api/export/start")
 async def start_export(config_data: dict[str, Any]):
     try:
-        export_manager.start_export(config_data)
+        validated_data = parse_export_config(config_data)
+        export_manager.start_export(validated_data)
         return {"message": "Экспорт запущен."}
     except ValueError as exc:
         raise HTTPException(status_code=400, detail=str(exc))
@@ -763,11 +848,12 @@ async def export_websocket_endpoint(websocket: WebSocket):
     export_manager.add_websocket(websocket)
     
     with export_manager._lock:
+        snapshot = export_manager.state.snapshot()
         state_dict = {
             "type": "init",
-            "status": export_manager.state.status,
-            "output_dir": export_manager.state.output_dir,
-            "logs": [log.split(":", 1) for log in export_manager.state.logs if ":" in log],
+            "status": snapshot["status"],
+            "output_dir": snapshot["output_dir"],
+            "logs": [log.split(":", 1) for log in snapshot["logs"] if ":" in log],
         }
     try:
         await websocket.send_text(json.dumps(state_dict))
@@ -788,15 +874,16 @@ async def websocket_endpoint(websocket: WebSocket):
     
     # Send current state upon connection
     with manager._lock:
+        snapshot = manager.state.snapshot()
         state_dict = {
             "type": "init",
-            "status": manager.state.status,
-            "epoch": manager.state.epoch,
-            "total_epochs": manager.state.total_epochs,
-            "output_dir": manager.state.output_dir,
-            "metrics": manager.state.metrics,
+            "status": snapshot["status"],
+            "epoch": snapshot["epoch"],
+            "total_epochs": snapshot["total_epochs"],
+            "output_dir": snapshot["output_dir"],
+            "metrics": snapshot["metrics"],
             # We format log items for the UI
-            "logs": [log.split(":", 1) for log in manager.state.logs if ":" in log],
+            "logs": [log.split(":", 1) for log in snapshot["logs"] if ":" in log],
         }
     try:
         await websocket.send_text(json.dumps(state_dict))

src/yolo_webui/config.py

@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
+import math
 import os
-from pathlib import Path
+from pathlib import Path, PureWindowsPath
 import re
 from typing import Any, Literal
 
@@ -28,6 +29,58 @@ SAFE_IDENTIFIER = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_.-]*$")
 MODEL_SUFFIXES = {".pt", ".pth", ".yaml", ".yml"}
 
 
+def _default_mlflow_tracking_uri() -> str:
+    return os.environ.get(
+        "YOLO_WEBUI_MLFLOW_TRACKING_URI",
+        "sqlite:///mlflow.db",
+    )
+
+
+def _require_string(value: Any, label: str) -> str:
+    if not isinstance(value, str):
+        raise ValueError(f"{label} должен быть строкой.")
+    return value
+
+
+def _require_bool(value: Any, label: str) -> bool:
+    if not isinstance(value, bool):
+        raise ValueError(f"{label} должен быть логическим значением.")
+    return value
+
+
+def _require_finite_number(value: Any, label: str) -> float:
+    if isinstance(value, bool) or not isinstance(value, (int, float)):
+        raise ValueError(f"{label} должен быть числом.")
+    try:
+        number = float(value)
+    except OverflowError as exc:
+        raise ValueError(f"{label} должен быть конечным числом.") from exc
+    if not math.isfinite(number):
+        raise ValueError(f"{label} должен быть конечным числом.")
+    return number
+
+
+def _require_integer(value: Any, label: str) -> int:
+    if isinstance(value, bool) or not isinstance(value, int):
+        raise ValueError(f"{label} должен быть целым числом.")
+    return value
+
+
+def _validate_run_name(value: str) -> None:
+    normalized = value.strip()
+    if not normalized:
+        return
+    if (
+        normalized in {".", ".."}
+        or "/" in normalized
+        or "\\" in normalized
+        or PureWindowsPath(normalized).drive
+    ):
+        raise ValueError(
+            "Имя запуска должно быть одним именем каталога без '/' или '\\'."
+        )
+
+
 def _allowed_roots(defaults: tuple[str, ...], environment_name: str) -> tuple[Path, ...]:
     configured = [
         item
@@ -109,7 +162,7 @@ class AugmentationConfig:
     close_mosaic: int = 10
 
     def validate(self) -> None:
-        if not self.enabled:
+        if not _require_bool(self.enabled, "Флаг аугментации"):
             return
 
         fractions = {
@@ -129,20 +182,23 @@ class AugmentationConfig:
             "Erasing": self.erasing,
         }
         for label, value in fractions.items():
-            if not 0.0 <= value <= 1.0:
+            number = _require_finite_number(value, f"Параметр «{label}»")
+            if not 0.0 <= number <= 1.0:
                 raise ValueError(f"Параметр «{label}» должен быть от 0 до 1.")
-        if self.degrees < 0:
+        if _require_finite_number(self.degrees, "Угол поворота") < 0:
             raise ValueError("Угол поворота не может быть отрицательным.")
-        if self.shear < 0:
+        if _require_finite_number(self.shear, "Угол сдвига") < 0:
             raise ValueError("Угол сдвига не может быть отрицательным.")
-        if self.close_mosaic < 0:
+        if _require_integer(self.close_mosaic, "Close mosaic") < 0:
             raise ValueError("Close mosaic не может быть отрицательным.")
+        _require_string(self.copy_paste_mode, "Режим copy-paste")
         if self.copy_paste_mode not in SUPPORTED_COPY_PASTE_MODES:
             raise ValueError(f"Неизвестный режим copy-paste: {self.copy_paste_mode}.")
+        _require_string(self.auto_augment, "Политика AutoAugment")
         if self.auto_augment not in SUPPORTED_AUTO_AUGMENT_POLICIES:
             raise ValueError(f"Неизвестная политика AutoAugment: {self.auto_augment}.")
 
-    def train_kwargs(self) -> dict[str, str | int | float]:
+    def train_kwargs(self) -> dict[str, str | int | float | None]:
         if not self.enabled:
             return {}
         return {
@@ -171,11 +227,15 @@ class AugmentationConfig:
 @dataclass(frozen=True, slots=True)
 class MlflowConfig:
     enabled: bool = True
-    tracking_uri: str = "sqlite:///mlflow.db"
+    tracking_uri: str = field(default_factory=_default_mlflow_tracking_uri)
     experiment_name: str = "yolo-webui"
     run_name: str = ""
 
     def validate(self) -> None:
+        _require_bool(self.enabled, "Флаг MLflow")
+        _require_string(self.tracking_uri, "URI хранилища MLflow")
+        _require_string(self.experiment_name, "Название эксперимента MLflow")
+        _require_string(self.run_name, "Название запуска MLflow")
         if self.enabled and not self.tracking_uri.strip():
             raise ValueError("Укажите URI хранилища MLflow.")
         if self.enabled and not self.experiment_name.strip():
@@ -189,8 +249,14 @@ class DatasetSplitConfig:
     classes_path: str = ""
 
     def validate(self) -> None:
+        _require_bool(self.enabled, "Флаг разделения датасета")
+        ratio = _require_finite_number(
+            self.train_ratio,
+            "Доля обучающей выборки (Train)",
+        )
+        _require_string(self.classes_path, "Путь к файлу классов")
         if self.enabled:
-            if not 0.1 <= self.train_ratio <= 0.95:
+            if not 0.1 <= ratio <= 0.95:
                 raise ValueError("Доля обучающей выборки (Train) должна быть от 0.1 до 0.95.")
 
 
@@ -212,10 +278,27 @@ class TrainingConfig:
     split: DatasetSplitConfig = field(default_factory=DatasetSplitConfig)
 
     def validate(self) -> None:
-        if not self.dataset.strip():
+        dataset = _require_string(self.dataset, "Датасет")
+        model = _require_string(self.model, "Модель")
+        _require_string(self.task, "Тип задачи")
+        _require_string(self.device, "Устройство")
+        project = _require_string(self.project, "Каталог результатов")
+        run_name = _require_string(self.run_name, "Имя запуска")
+        if not isinstance(self.augmentation, AugmentationConfig):
+            raise ValueError("Параметры аугментации должны быть объектом.")
+        if not isinstance(self.mlflow, MlflowConfig):
+            raise ValueError("Параметры MLflow должны быть объектом.")
+        if not isinstance(self.split, DatasetSplitConfig):
+            raise ValueError("Параметры разделения датасета должны быть объектом.")
+        self.augmentation.validate()
+        self.mlflow.validate()
+        self.split.validate()
+
+        if not dataset.strip():
             raise ValueError("Укажите путь или имя датасета.")
-        if not self.model.strip():
+        if not model.strip():
             raise ValueError("Укажите путь или имя модели.")
+        _validate_run_name(run_name)
 
         data_roots = _allowed_roots(("datasets",), "YOLO_WEBUI_DATA_ROOTS")
         model_roots = _allowed_roots(
@@ -224,23 +307,29 @@ class TrainingConfig:
         )
         run_roots = _allowed_roots(("runs",), "YOLO_WEBUI_RUN_ROOTS")
         _validate_local_reference(
-            self.dataset,
+            dataset,
             label="Датасет",
             roots=data_roots,
             allow_identifier=True,
         )
         _validate_local_reference(
-            self.model,
+            model,
             label="Модель",
             roots=model_roots,
             allow_identifier=True,
             allowed_suffixes=MODEL_SUFFIXES,
         )
         _validate_local_reference(
-            self.project.strip() or "runs/train",
+            project.strip() or "runs/train",
             label="Каталог результатов",
             roots=run_roots,
         )
+        if run_name.strip():
+            _validate_local_reference(
+                str(Path(project.strip() or "runs/train") / run_name.strip()),
+                label="Каталог запуска",
+                roots=run_roots,
+            )
         if self.split.classes_path.strip():
             _validate_local_reference(
                 self.split.classes_path,
@@ -254,19 +343,24 @@ class TrainingConfig:
                 "Автоматическое разделение доступно только для YOLO-датасетов "
                 "с папками images/labels и не поддерживает задачу classify."
             )
-        if self.epochs < 1:
+        if _require_integer(self.epochs, "Количество эпох") < 1:
             raise ValueError("Количество эпох должно быть не меньше 1.")
-        if self.image_size < 32:
+        if _require_integer(self.image_size, "Размер изображения") < 32:
             raise ValueError("Размер изображения должен быть не меньше 32.")
-        if self.batch_size == 0 or self.batch_size < -1:
-            raise ValueError("Batch должен быть положительным числом или -1 для автоподбора.")
-        if self.workers < 0:
+        batch_size = _require_finite_number(self.batch_size, "Batch")
+        if isinstance(self.batch_size, int):
+            valid_batch = self.batch_size == -1 or self.batch_size >= 1
+        else:
+            valid_batch = 0 < batch_size < 1
+        if not valid_batch:
+            raise ValueError(
+                "Batch должен быть целым числом от 1, значением -1 для "
+                "автоподбора либо дробью от 0 до 1."
+            )
+        if _require_integer(self.workers, "Количество workers") < 0:
             raise ValueError("Количество workers не может быть отрицательным.")
-        if self.patience < 0:
+        if _require_integer(self.patience, "Patience") < 0:
             raise ValueError("Patience не может быть отрицательным.")
-        self.augmentation.validate()
-        self.mlflow.validate()
-        self.split.validate()
 
     def train_kwargs(self) -> dict[str, str | int | float | bool]:
         """Convert the form values to arguments accepted by YOLO.train()."""
@@ -303,12 +397,30 @@ class TrainingConfig:
 
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> TrainingConfig:
-        aug_data = data.get("augmentation", {})
-        mlflow_data = data.get("mlflow", {})
-        split_data = data.get("split", {})
+        if not isinstance(data, dict):
+            raise ValueError("Конфигурация должна быть JSON-объектом.")
+
+        sections: dict[str, dict[str, Any]] = {}
+        for key, label in (
+            ("augmentation", "аугментации"),
+            ("mlflow", "MLflow"),
+            ("split", "разделения датасета"),
+        ):
+            raw_section = data.get(key, {})
+            if not isinstance(raw_section, dict):
+                raise ValueError(f"Параметры {label} должны быть JSON-объектом.")
+            sections[key] = raw_section
+
+        try:
+            augmentation = AugmentationConfig(**sections["augmentation"])
+            mlflow = MlflowConfig(**sections["mlflow"])
+            split = DatasetSplitConfig(**sections["split"])
+        except TypeError as exc:
+            raise ValueError(f"Конфигурация содержит неизвестные параметры: {exc}") from exc
+
         return cls(
-            dataset=data["dataset"],
-            model=data["model"],
+            dataset=data.get("dataset", ""),
+            model=data.get("model", ""),
             task=data.get("task", "detect"),
             epochs=data.get("epochs", 100),
             image_size=data.get("image_size", 640),
@@ -318,7 +430,7 @@ class TrainingConfig:
             patience=data.get("patience", 100),
             project=data.get("project", "runs/train"),
             run_name=data.get("run_name", ""),
-            augmentation=AugmentationConfig(**aug_data) if aug_data else AugmentationConfig(),
-            mlflow=MlflowConfig(**mlflow_data) if mlflow_data else MlflowConfig(),
-            split=DatasetSplitConfig(**split_data) if split_data else DatasetSplitConfig(),
+            augmentation=augmentation,
+            mlflow=mlflow,
+            split=split,
         )

src/yolo_webui/dataset_splitter.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import math
 import random
 from pathlib import Path
 from typing import Any
@@ -8,6 +9,25 @@ from uuid import uuid4
 import yaml
 
 
+def _normalize_class_key(raw_key: Any, source: Path) -> int:
+    if isinstance(raw_key, bool):
+        raise ValueError(f"Некорректный ID класса в '{source}': {raw_key!r}.")
+    if isinstance(raw_key, int):
+        class_id = raw_key
+    elif isinstance(raw_key, str):
+        try:
+            class_id = int(raw_key.strip())
+        except ValueError as exc:
+            raise ValueError(
+                f"Некорректный ID класса в '{source}': {raw_key!r}."
+            ) from exc
+    else:
+        raise ValueError(f"Некорректный ID класса в '{source}': {raw_key!r}.")
+    if class_id < 0:
+        raise ValueError(f"ID класса в '{source}' не может быть отрицательным.")
+    return class_id
+
+
 def _normalize_names(names: Any, source: Path) -> dict[int, str]:
     if isinstance(names, list):
         items = enumerate(names)
@@ -15,14 +35,7 @@ def _normalize_names(names: Any, source: Path) -> dict[int, str]:
         normalized_items: list[tuple[int, Any]] = []
         seen: set[int] = set()
         for raw_key, value in names.items():
-            if isinstance(raw_key, bool):
-                raise ValueError(f"Некорректный ID класса в '{source}': {raw_key!r}.")
-            try:
-                class_id = int(raw_key)
-            except (TypeError, ValueError) as exc:
-                raise ValueError(
-                    f"Некорректный ID класса в '{source}': {raw_key!r}."
-                ) from exc
+            class_id = _normalize_class_key(raw_key, source)
             if class_id in seen:
                 raise ValueError(f"Повторяющийся ID класса {class_id} в '{source}'.")
             seen.add(class_id)
@@ -86,6 +99,36 @@ def _parse_classes_file(path: Path) -> dict[int, str]:
     )
 
 
+def _read_label_class_ids(path: Path) -> set[int]:
+    class_ids: set[int] = set()
+    try:
+        with path.open("r", encoding="utf-8") as label_file:
+            for line_number, line in enumerate(label_file, start=1):
+                parts = line.strip().split()
+                if not parts:
+                    continue
+                try:
+                    numeric_id = float(parts[0])
+                except ValueError as exc:
+                    raise ValueError(
+                        f"Некорректный ID класса в '{path}', строка {line_number}: "
+                        f"{parts[0]!r}."
+                    ) from exc
+                if (
+                    not math.isfinite(numeric_id)
+                    or not numeric_id.is_integer()
+                    or numeric_id < 0
+                ):
+                    raise ValueError(
+                        f"Некорректный ID класса в '{path}', строка {line_number}: "
+                        f"{parts[0]!r}."
+                    )
+                class_ids.add(int(numeric_id))
+    except (OSError, UnicodeError) as exc:
+        raise ValueError(f"Не удалось прочитать файл разметки '{path}': {exc}.") from exc
+    return class_ids
+
+
 def read_classes(dataset_dir: Path, custom_classes_path: str) -> dict[int, str]:
     # An explicit path is authoritative: typos and malformed files must not fall back.
     if custom_classes_path.strip():
@@ -119,17 +162,10 @@ def read_classes(dataset_dir: Path, custom_classes_path: str) -> dict[int, str]:
     class_ids: set[int] = set()
     labels_dir = dataset_dir / "labels"
     if labels_dir.exists():
-        for txt_file in labels_dir.rglob("*.txt"):
+        for txt_file in sorted(labels_dir.rglob("*.txt")):
             if txt_file.name == "classes.txt":
                 continue
-            try:
-                with txt_file.open("r", encoding="utf-8") as label_file:
-                    for line in label_file:
-                        parts = line.strip().split()
-                        if parts:
-                            class_ids.add(int(parts[0]))
-            except (OSError, ValueError):
-                continue
+            class_ids.update(_read_label_class_ids(txt_file))
 
     if class_ids:
         max_id = max(class_ids)
@@ -146,15 +182,52 @@ def _write_new(path: Path, content: str) -> None:
         output_file.write(content)
 
 
+def _validate_image_labels(
+    images_dir: Path,
+    labels_dir: Path,
+    image_files: list[Path],
+    classes: dict[int, str],
+) -> None:
+    checked_paths: set[Path] = set()
+    for image_path in image_files:
+        label_path = labels_dir / image_path.relative_to(images_dir).with_suffix(".txt")
+        if label_path in checked_paths:
+            continue
+        checked_paths.add(label_path)
+        if not label_path.exists():
+            # YOLO treats an image without a label file as a background image.
+            continue
+        if not label_path.is_file():
+            raise ValueError(f"Путь к разметке '{label_path}' не является файлом.")
+        for class_id in _read_label_class_ids(label_path):
+            if class_id not in classes:
+                raise ValueError(
+                    f"В файле '{label_path}' указан класс {class_id}, "
+                    f"но список классов содержит ID от 0 до {len(classes) - 1}."
+                )
+
+
 def split_dataset(
     dataset_dir: str, train_ratio: float, classes_path: str
 ) -> tuple[int, int, str]:
+    if not isinstance(dataset_dir, str):
+        raise ValueError("Каталог датасета должен быть строкой.")
     if not dataset_dir.strip():
         raise ValueError("Укажите каталог датасета.")
-    if not 0.1 <= train_ratio <= 0.95:
+    if not isinstance(classes_path, str):
+        raise ValueError("Путь к файлу классов должен быть строкой.")
+    if isinstance(train_ratio, bool) or not isinstance(train_ratio, (int, float)):
+        raise ValueError("Доля обучающей выборки должна быть числом.")
+    try:
+        ratio = float(train_ratio)
+    except OverflowError as exc:
+        raise ValueError("Доля обучающей выборки должна быть конечным числом.") from exc
+    if not math.isfinite(ratio):
+        raise ValueError("Доля обучающей выборки должна быть конечным числом.")
+    if not 0.1 <= ratio <= 0.95:
         raise ValueError("Доля обучающей выборки должна быть от 0.1 до 0.95.")
 
-    base_dir = Path(dataset_dir.strip()).expanduser().absolute()
+    base_dir = Path(dataset_dir.strip()).expanduser().resolve(strict=False)
     images_dir = base_dir / "images"
     labels_dir = base_dir / "labels"
 
@@ -185,13 +258,14 @@ def split_dataset(
     rng = random.Random(42)
     rng.shuffle(image_files)
 
-    split_idx = int(len(image_files) * train_ratio)
+    split_idx = int(len(image_files) * ratio)
     split_idx = max(1, min(split_idx, len(image_files) - 1))
     train_images = image_files[:split_idx]
     val_images = image_files[split_idx:]
 
     # Resolve classes before creating output so invalid input leaves no partial split.
     classes = read_classes(base_dir, classes_path)
+    _validate_image_labels(images_dir, labels_dir, image_files, classes)
 
     relative_split_dir = Path(".yolo-webui") / "splits" / uuid4().hex
     split_dir = base_dir / relative_split_dir

src/yolo_webui/export_runner.py

@@ -1,14 +1,213 @@
+from __future__ import annotations
+
 import json
+import math
 import os
 import sys
 import traceback
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
 from pathlib import Path
+from typing import Any
 
-# Force headless Matplotlib to avoid any thread/process GUI issues
+from yolo_webui.config import MODEL_SUFFIXES, _allowed_roots, _is_within
+
+# Force headless Matplotlib to avoid any thread/process GUI issues.
 os.environ["MPLBACKEND"] = "Agg"
+# Restrict PyTorch checkpoint deserialization for direct CLI invocation too.
+os.environ["ULTRALYTICS_SAFE_LOAD"] = "1"
 
-def main() -> int:
-    args = sys.argv[1:]
+SUPPORTED_EXPORT_FORMATS = frozenset(
+    {
+        "coreml",
+        "engine",
+        "onnx",
+        "openvino",
+        "pb",
+        "saved_model",
+        "tflite",
+        "torchscript",
+    }
+)
+EXPORT_CONFIG_FIELDS = frozenset(
+    {
+        "model",
+        "format",
+        "imgsz",
+        "half",
+        "int8",
+        "dynamic",
+        "simplify",
+        "batch",
+        "workspace",
+    }
+)
+
+
+def _bounded_int(
+    data: Mapping[str, Any],
+    key: str,
+    default: int,
+    *,
+    minimum: int,
+    maximum: int,
+) -> int:
+    value = data.get(key, default)
+    if type(value) is not int or not minimum <= value <= maximum:
+        raise ValueError(
+            f"Параметр {key} должен быть целым числом от {minimum} до {maximum}."
+        )
+    return value
+
+
+def _boolean(data: Mapping[str, Any], key: str, default: bool = False) -> bool:
+    value = data.get(key, default)
+    if type(value) is not bool:
+        raise ValueError(f"Параметр {key} должен быть логическим значением.")
+    return value
+
+
+def _bounded_number(
+    data: Mapping[str, Any],
+    key: str,
+    default: float,
+    *,
+    minimum: float,
+    maximum: float,
+) -> float:
+    value = data.get(key, default)
+    if type(value) not in (int, float):
+        raise ValueError(
+            f"Параметр {key} должен быть числом от {minimum:g} до {maximum:g}."
+        )
+    try:
+        number = float(value)
+    except OverflowError as exc:
+        raise ValueError(
+            f"Параметр {key} должен быть числом от {minimum:g} до {maximum:g}."
+        ) from exc
+    if not math.isfinite(number) or not minimum <= number <= maximum:
+        raise ValueError(
+            f"Параметр {key} должен быть числом от {minimum:g} до {maximum:g}."
+        )
+    return number
+
+
+def _model_path(value: Any) -> Path:
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError("Укажите путь к модели.")
+
+    normalized = value.strip()
+    if any(character in normalized for character in ("\0", "\r", "\n")):
+        raise ValueError("Путь к модели содержит недопустимые управляющие символы.")
+
+    candidate = Path(normalized).expanduser()
+    if not candidate.is_absolute():
+        if len(candidate.parts) == 1:
+            candidate = Path("models") / candidate
+        candidate = Path.cwd() / candidate
+
+    try:
+        resolved = candidate.resolve(strict=False)
+    except (OSError, RuntimeError, ValueError) as exc:
+        raise ValueError(f"Некорректный путь к модели: {normalized}") from exc
+
+    roots = _allowed_roots(("models", "runs"), "YOLO_WEBUI_MODEL_ROOTS")
+    if not _is_within(resolved, roots):
+        allowed = ", ".join(str(root) for root in roots)
+        raise ValueError(
+            f"Модель должна находиться в разрешённом каталоге: {allowed}."
+        )
+    try:
+        is_file = resolved.is_file()
+    except OSError as exc:
+        raise ValueError(f"Не удалось проверить файл модели: {normalized}") from exc
+    if not is_file:
+        raise ValueError(f"Файл модели не найден: {normalized}")
+    if resolved.suffix.lower() not in MODEL_SUFFIXES:
+        expected = ", ".join(sorted(MODEL_SUFFIXES))
+        raise ValueError(f"Модель должна иметь расширение {expected}.")
+    return resolved
+
+
+@dataclass(frozen=True, slots=True)
+class ExportConfig:
+    model: Path
+    export_format: str
+    imgsz: int
+    half: bool
+    int8: bool
+    dynamic: bool
+    simplify: bool
+    batch: int
+    workspace: float
+
+    @classmethod
+    def from_mapping(cls, data: Mapping[str, Any]) -> ExportConfig:
+        unknown = set(data) - EXPORT_CONFIG_FIELDS
+        if unknown:
+            fields = ", ".join(sorted(map(str, unknown)))
+            raise ValueError(f"Неизвестные параметры экспорта: {fields}.")
+
+        raw_format = data.get("format", "onnx")
+        if not isinstance(raw_format, str):
+            raise ValueError("Параметр format должен быть строкой.")
+        export_format = raw_format.strip().lower()
+        if export_format not in SUPPORTED_EXPORT_FORMATS:
+            supported = ", ".join(sorted(SUPPORTED_EXPORT_FORMATS))
+            raise ValueError(
+                f"Неподдерживаемый формат экспорта: {raw_format}. "
+                f"Доступны: {supported}."
+            )
+
+        half = _boolean(data, "half")
+        int8 = _boolean(data, "int8")
+        if half and int8:
+            raise ValueError("Параметры half и int8 нельзя включать одновременно.")
+
+        return cls(
+            model=_model_path(data.get("model")),
+            export_format=export_format,
+            imgsz=_bounded_int(data, "imgsz", 640, minimum=32, maximum=8192),
+            half=half,
+            int8=int8,
+            dynamic=_boolean(data, "dynamic"),
+            simplify=_boolean(data, "simplify"),
+            batch=_bounded_int(data, "batch", 1, minimum=1, maximum=1024),
+            workspace=_bounded_number(
+                data,
+                "workspace",
+                4,
+                minimum=1,
+                maximum=64,
+            ),
+        )
+
+
+def _result_path(value: Any) -> Path:
+    if isinstance(value, (list, tuple)):
+        if not value:
+            raise RuntimeError("Ultralytics не вернул путь к экспортированной модели.")
+        value = value[0]
+    if not isinstance(value, (str, os.PathLike)):
+        raise RuntimeError("Ultralytics не вернул путь к экспортированной модели.")
+
+    raw_path = os.fspath(value)
+    if not raw_path or any(character in raw_path for character in ("\0", "\r", "\n")):
+        raise RuntimeError("Ultralytics вернул некорректный путь результата.")
+    path = Path(raw_path).expanduser()
+    if not path.is_absolute():
+        path = Path.cwd() / path
+    try:
+        return path.resolve(strict=True)
+    except (FileNotFoundError, OSError, RuntimeError, ValueError) as exc:
+        raise RuntimeError(
+            f"Экспорт завершился без ожидаемого артефакта: {raw_path}"
+        ) from exc
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    args = list(sys.argv[1:] if argv is None else argv)
     if not args:
         print(
             "Usage: python -m yolo_webui.export_runner <config_json_path>",
@@ -16,63 +215,58 @@ def main() -> int:
         )
         return 1
 
-    config_path = Path(args[0])
-    
-    # The parent waits for this marker
+    # The parent waits for this marker before treating the process as started.
     print("__YOLO_WEBUI_READY__", flush=True)
 
     try:
-        with config_path.open("r", encoding="utf-8") as config_file:
-            config_dict = json.load(config_file)
+        with Path(args[0]).open("r", encoding="utf-8") as config_file:
+            raw_config = json.load(config_file)
+        if not isinstance(raw_config, Mapping):
+            raise ValueError("Конфигурация экспорта должна быть JSON-объектом.")
+        config = ExportConfig.from_mapping(raw_config)
     except Exception as exc:
-        print(f"Error loading config: {exc}", file=sys.stderr)
-        return 1
-
-    model_path = config_dict.get("model")
-    export_format = config_dict.get("format", "onnx")
-    imgsz = config_dict.get("imgsz", 640)
-    half = config_dict.get("half", False)
-    int8 = config_dict.get("int8", False)
-    dynamic = config_dict.get("dynamic", False)
-    simplify = config_dict.get("simplify", False)
-    batch = config_dict.get("batch", 1)
-    workspace = config_dict.get("workspace", 4)
-
-    if not model_path or not Path(model_path).exists():
-        print(f"Model file not found: {model_path}")
+        print(f"Ошибка конфигурации экспорта: {exc}", file=sys.stderr)
         return 1
 
     try:
+        os.environ["ULTRALYTICS_SAFE_LOAD"] = "1"
         from ultralytics import YOLO
 
-        print(f"Загрузка модели {model_path}...", flush=True)
-        model = YOLO(model_path)
+        print(f"Загрузка модели {config.model}...", flush=True)
+        model = YOLO(str(config.model))
 
-        print(f"Запуск экспорта в формат {export_format}...", flush=True)
-        print(f"Параметры: imgsz={imgsz}, half={half}, int8={int8}, dynamic={dynamic}, simplify={simplify}, batch={batch}, workspace={workspace}", flush=True)
-        
-        exported_path = model.export(
-            format=export_format,
-            imgsz=imgsz,
-            half=half,
-            int8=int8,
-            dynamic=dynamic,
-            simplify=simplify,
-            batch=batch,
-            workspace=workspace
+        print(
+            f"Запуск экспорта в формат {config.export_format}...",
+            flush=True,
+        )
+        print(
+            "Параметры: "
+            f"imgsz={config.imgsz}, half={config.half}, int8={config.int8}, "
+            f"dynamic={config.dynamic}, simplify={config.simplify}, "
+            f"batch={config.batch}, workspace={config.workspace}",
+            flush=True,
         )
 
-        print(f"Экспорт завершен успешно.", flush=True)
-        if exported_path:
-            # ultralytics returns either a single path (string) or list of paths depending on the format.
-            if isinstance(exported_path, list):
-                exported_path = exported_path[0]
-            print(f"__YOLO_WEBUI_RESULT__:{exported_path}", flush=True)
+        exported_path = model.export(
+            format=config.export_format,
+            imgsz=config.imgsz,
+            half=config.half,
+            int8=config.int8,
+            dynamic=config.dynamic,
+            simplify=config.simplify,
+            batch=config.batch,
+            workspace=config.workspace,
+        )
+        result_path = _result_path(exported_path)
+
+        print("Экспорт завершен успешно.", flush=True)
+        print(f"__YOLO_WEBUI_RESULT__:{result_path}", flush=True)
         return 0
-    except Exception as e:
-        print(f"Ошибка при экспорте модели: {e}", flush=True)
+    except Exception as exc:
+        print(f"Ошибка при экспорте модели: {exc}", flush=True)
         traceback.print_exc()
         return 1
 
+
 if __name__ == "__main__":
     raise SystemExit(main())

src/yolo_webui/static/app.js

@@ -35,6 +35,10 @@ document.addEventListener('DOMContentLoaded', () => {
     };
     let discoveredModels = [];
 
+    function getDiscoveredModelValue(model) {
+        return model.path || model.name;
+    }
+
     function updateModelOptions() {
         const task = taskSelect.value;
         const stdModels = standardModels[task] || [];
@@ -54,13 +58,13 @@ document.addEventListener('DOMContentLoaded', () => {
         modelSelect.appendChild(stdGroup);
         
         // Group 2: Discovered Models
-        const localModels = discoveredModels.filter(m => !stdModels.includes(m.name));
+        const localModels = discoveredModels.filter(m => getDiscoveredModelValue(m));
         if (localModels.length > 0) {
             const localGroup = document.createElement('optgroup');
             localGroup.label = 'Локальные/скачанные модели';
             localModels.forEach(m => {
                 const opt = document.createElement('option');
-                opt.value = m.name;
+                opt.value = getDiscoveredModelValue(m);
                 opt.textContent = m.name;
                 localGroup.appendChild(opt);
             });
@@ -74,8 +78,8 @@ document.addEventListener('DOMContentLoaded', () => {
         modelSelect.appendChild(customOpt);
         
         // Match selection if valid
-        const allAvailable = [...stdModels, ...localModels.map(m => m.name)];
-        if (allAvailable.includes(currentSelectVal)) {
+        const allAvailable = [...stdModels, ...localModels.map(getDiscoveredModelValue)];
+        if (currentSelectVal === '__custom__' || allAvailable.includes(currentSelectVal)) {
             modelSelect.value = currentSelectVal;
         } else {
             modelSelect.value = stdModels[0] || '__custom__';
@@ -129,7 +133,11 @@ document.addEventListener('DOMContentLoaded', () => {
     let trainingTimer = null;
     let secondsElapsed = 0;
     let socket = null;
+    let socketReconnectTimer = null;
     let isTrainingActive = false;
+    let isTrainingStartPending = false;
+    let trainingStatusRevision = 0;
+    let currentTrainingStatus = 'idle';
 
     // --- View Tab Switching ---
     const viewBtns = document.querySelectorAll('.view-btn');
@@ -215,7 +223,17 @@ document.addEventListener('DOMContentLoaded', () => {
     function updateMlflowHeaderLink() {
         const uri = trackingUriInput.value.trim();
         if (uri.startsWith('http://') || uri.startsWith('https://')) {
-            mlflowHeaderLink.href = uri;
+            let browserUri = uri;
+            try {
+                const parsedUri = new URL(uri);
+                if (parsedUri.hostname === 'mlflow') {
+                    parsedUri.hostname = window.location.hostname || 'localhost';
+                    browserUri = parsedUri.href;
+                }
+            } catch (error) {
+                console.error('Invalid MLflow tracking URI:', error);
+            }
+            mlflowHeaderLink.href = browserUri;
             mlflowHeaderLink.style.opacity = '1';
             mlflowHeaderLink.style.pointerEvents = 'auto';
         } else {
@@ -343,41 +361,75 @@ document.addEventListener('DOMContentLoaded', () => {
     }
 
     // --- WebSocket Sync ---
+    function scheduleWebSocketReconnect() {
+        if (socketReconnectTimer !== null) return;
+        socketReconnectTimer = setTimeout(() => {
+            socketReconnectTimer = null;
+            connectWebSocket();
+        }, 5000);
+    }
+
+    function parseWebSocketMessage(event, label, logLine) {
+        try {
+            const data = JSON.parse(event.data);
+            if (!data || typeof data !== 'object' || Array.isArray(data)) {
+                throw new Error('WebSocket payload must be an object');
+            }
+            return data;
+        } catch (error) {
+            console.error(`${label} message error:`, error);
+            logLine('Получено некорректное сообщение от сервера.', 'warning');
+            return null;
+        }
+    }
+
     function connectWebSocket() {
         const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
         const wsUrl = `${protocol}//${window.location.host}/api/ws`;
 
-        socket = new WebSocket(wsUrl);
+        let nextSocket;
+        try {
+            nextSocket = new WebSocket(wsUrl);
+        } catch (error) {
+            console.error('WS connection error:', error);
+            addLogLine('Не удалось подключиться к серверу. Повторная попытка через 5 секунд...', 'warning');
+            scheduleWebSocketReconnect();
+            return;
+        }
+        socket = nextSocket;
         
-        socket.onopen = () => {
+        nextSocket.onopen = () => {
             addLogLine('Соединение с сервером установлено.', 'info');
         };
         
-        socket.onclose = () => {
+        nextSocket.onclose = () => {
+            if (socket !== nextSocket) return;
+            socket = null;
             addLogLine('Соединение потеряно. Повторная попытка через 5 секунд...', 'warning');
-            setTimeout(connectWebSocket, 5000);
+            scheduleWebSocketReconnect();
         };
         
-        socket.onerror = (err) => {
+        nextSocket.onerror = (err) => {
             console.error('WS Error:', err);
         };
         
-        socket.onmessage = (event) => {
-            const data = JSON.parse(event.data);
+        nextSocket.onmessage = (event) => {
+            const data = parseWebSocketMessage(event, 'Training WebSocket', addLogLine);
+            if (!data) return;
             
             if (data.type === 'init') {
                 updateUIStatus(data.status);
                 
                 // Load logs
                 logContainer.innerHTML = '';
-                data.logs.forEach(([levelCode, msg]) => {
+                (Array.isArray(data.logs) ? data.logs : []).forEach(([levelCode, msg]) => {
                     const level = levelCode.replace('__LOG_LEVEL_', '').replace('__', '').toLowerCase();
                     addLogLine(msg, level);
                 });
                 
                 // Draw initial chart points
                 initChart();
-                if (data.metrics && data.metrics.length > 0) {
+                if (Array.isArray(data.metrics) && data.metrics.length > 0) {
                     data.metrics.forEach(m => {
                         updateChart(m.epoch, m);
                     });
@@ -424,6 +476,8 @@ document.addEventListener('DOMContentLoaded', () => {
     }
 
     function updateUIStatus(status) {
+        trainingStatusRevision++;
+        currentTrainingStatus = status;
         statusCard.className = `status-${status}`;
         
         switch (status) {
@@ -505,6 +559,25 @@ document.addEventListener('DOMContentLoaded', () => {
         return Number.isNaN(value) ? fallback : value;
     }
 
+    function readValidatedNumber(id, label, {integer = false, min, max} = {}) {
+        const rawValue = document.getElementById(id).value.trim();
+        const value = Number(rawValue);
+        const outOfRange = (min !== undefined && value < min)
+            || (max !== undefined && value > max);
+        if (
+            rawValue === ''
+            || !Number.isFinite(value)
+            || (integer && !Number.isInteger(value))
+            || outOfRange
+        ) {
+            const range = max === undefined ? `не меньше ${min}` : `от ${min} до ${max}`;
+            const integerHint = integer ? 'целым числом ' : '';
+            showNotification(`${label} должен быть ${integerHint}${range}.`, 'warning');
+            return null;
+        }
+        return value;
+    }
+
     function getFormConfig() {
         return {
             dataset: document.getElementById('dataset').value.trim(),
@@ -561,11 +634,18 @@ document.addEventListener('DOMContentLoaded', () => {
         const modelVal = data.model || 'yolo11n.pt';
         const task = data.task || 'detect';
         const stdModels = standardModels[task] || [];
-        const allAvailable = [...stdModels, ...discoveredModels.map(m => m.name)];
-        if (allAvailable.includes(modelVal)) {
+        const matchedLocalModel = discoveredModels.find(
+            model => model.path === modelVal || model.name === modelVal
+        );
+        if (stdModels.includes(modelVal)) {
             modelSelect.value = modelVal;
             modelCustomWrapper.style.display = 'none';
             document.getElementById('model').value = modelVal;
+        } else if (matchedLocalModel) {
+            const discoveredValue = getDiscoveredModelValue(matchedLocalModel);
+            modelSelect.value = discoveredValue;
+            modelCustomWrapper.style.display = 'none';
+            document.getElementById('model').value = discoveredValue;
         } else {
             modelSelect.value = '__custom__';
             modelCustomWrapper.style.display = 'block';
@@ -831,9 +911,9 @@ document.addEventListener('DOMContentLoaded', () => {
     });
 
     sessionSaveBtn.addEventListener('click', async () => {
-        const name = sessionNameInput.value.trim().replace(/[^a-zA-Z0-9_\-]/g, "");
-        if (!name) {
-            showNotification('Введите корректное имя профиля (латиница, цифры, дефисы).', 'warning');
+        const name = sessionNameInput.value.trim();
+        if (!/^[a-zA-Z0-9_-]+$/.test(name)) {
+            showNotification('Введите корректное имя профиля (латиница, цифры, дефисы и подчёркивания).', 'warning');
             return;
         }
         if (name === "last_run") {
@@ -888,8 +968,12 @@ document.addEventListener('DOMContentLoaded', () => {
 
     // --- Form submit ---
     async function startTraining() {
-        if (isTrainingActive) return;
+        if (isTrainingActive || isTrainingStartPending) return;
+        isTrainingStartPending = true;
+        startBtn.disabled = true;
         const config = getFormConfig();
+        const statusRevisionAtStart = trainingStatusRevision;
+        let started = false;
 
         try {
             const res = await fetch('/api/train/start', {
@@ -902,10 +986,21 @@ document.addEventListener('DOMContentLoaded', () => {
             if (!res.ok) {
                 throw new Error(data.detail || 'Failed to start training');
             }
+            started = true;
+            const terminalStatusReceived = trainingStatusRevision !== statusRevisionAtStart
+                && ['finished', 'succeeded', 'cancelled', 'failed'].includes(currentTrainingStatus);
+            if (!isTrainingActive && !terminalStatusReceived) {
+                updateUIStatus('preparing');
+            }
             showNotification('Обучение успешно запущено!', 'success');
         } catch (err) {
             console.error('Start error:', err);
             showNotification(err.message, 'error');
+        } finally {
+            isTrainingStartPending = false;
+            if (!started && !isTrainingActive) {
+                startBtn.disabled = false;
+            }
         }
     }
 
@@ -1002,7 +1097,11 @@ document.addEventListener('DOMContentLoaded', () => {
     const exportAutoscrollCheck = document.getElementById('export-autoscroll');
     
     let isExportActive = false;
+    let isExportStartPending = false;
     let exportSocket = null;
+    let exportSocketReconnectTimer = null;
+    let exportStatusRevision = 0;
+    let currentExportStatus = 'idle';
 
     function addExportLogLine(message, level = 'info') {
         const line = document.createElement('div');
@@ -1019,32 +1118,52 @@ document.addEventListener('DOMContentLoaded', () => {
         exportLogContainer.innerHTML = '';
     });
 
+    function scheduleExportWebSocketReconnect() {
+        if (exportSocketReconnectTimer !== null) return;
+        exportSocketReconnectTimer = setTimeout(() => {
+            exportSocketReconnectTimer = null;
+            connectExportWebSocket();
+        }, 5000);
+    }
+
     function connectExportWebSocket() {
         const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
         const wsUrl = `${protocol}//${window.location.host}/api/export/ws`;
 
-        exportSocket = new WebSocket(wsUrl);
+        let nextSocket;
+        try {
+            nextSocket = new WebSocket(wsUrl);
+        } catch (error) {
+            console.error('Export WS connection error:', error);
+            addExportLogLine('Не удалось подключиться к серверу. Повторная попытка через 5 секунд...', 'warning');
+            scheduleExportWebSocketReconnect();
+            return;
+        }
+        exportSocket = nextSocket;
         
-        exportSocket.onopen = () => {
+        nextSocket.onopen = () => {
             addExportLogLine('Соединение с сервером установлено.', 'info');
         };
         
-        exportSocket.onclose = () => {
+        nextSocket.onclose = () => {
+            if (exportSocket !== nextSocket) return;
+            exportSocket = null;
             addExportLogLine('Соединение потеряно. Повторная попытка через 5 секунд...', 'warning');
-            setTimeout(connectExportWebSocket, 5000);
+            scheduleExportWebSocketReconnect();
         };
         
-        exportSocket.onerror = (err) => {
+        nextSocket.onerror = (err) => {
             console.error('Export WS Error:', err);
         };
         
-        exportSocket.onmessage = (event) => {
-            const data = JSON.parse(event.data);
+        nextSocket.onmessage = (event) => {
+            const data = parseWebSocketMessage(event, 'Export WebSocket', addExportLogLine);
+            if (!data) return;
             
             if (data.type === 'init') {
                 updateExportUIStatus(data.status);
                 exportLogContainer.innerHTML = '';
-                data.logs.forEach(([levelCode, msg]) => {
+                (Array.isArray(data.logs) ? data.logs : []).forEach(([levelCode, msg]) => {
                     const level = levelCode.replace('__LOG_LEVEL_', '').replace('__', '').toLowerCase();
                     addExportLogLine(msg, level);
                 });
@@ -1060,6 +1179,8 @@ document.addEventListener('DOMContentLoaded', () => {
     }
 
     function updateExportUIStatus(status) {
+        exportStatusRevision++;
+        currentExportStatus = status;
         exportStatusCard.className = `status-${status}`;
         
         switch (status) {
@@ -1110,7 +1231,7 @@ document.addEventListener('DOMContentLoaded', () => {
     }
 
     async function startExport() {
-        if (isExportActive) return;
+        if (isExportActive || isExportStartPending) return;
         
         const modelVal = document.getElementById('export-model').value.trim();
         if (!modelVal) {
@@ -1118,17 +1239,47 @@ document.addEventListener('DOMContentLoaded', () => {
             return;
         }
 
+        const imgsz = readValidatedNumber(
+            'export-imgsz',
+            'Размер изображения',
+            {integer: true, min: 32, max: 8192}
+        );
+        if (imgsz === null) return;
+        const batch = readValidatedNumber(
+            'export-batch',
+            'Размер батча',
+            {integer: true, min: 1, max: 1024}
+        );
+        if (batch === null) return;
+        const workspace = readValidatedNumber(
+            'export-workspace',
+            'Workspace',
+            {min: 1, max: 64}
+        );
+        if (workspace === null) return;
+
+        const half = document.getElementById('export-half').checked;
+        const int8 = document.getElementById('export-int8').checked;
+        if (half && int8) {
+            showNotification('FP16 и INT8 нельзя включать одновременно.', 'warning');
+            return;
+        }
+
+        isExportStartPending = true;
+        exportStartBtn.disabled = true;
         const config = {
             model: modelVal,
             format: document.getElementById('export-format').value,
-            imgsz: readNumber('export-imgsz', 640, true),
-            half: document.getElementById('export-half').checked,
-            int8: document.getElementById('export-int8').checked,
+            imgsz,
+            half,
+            int8,
             dynamic: document.getElementById('export-dynamic').checked,
             simplify: document.getElementById('export-simplify').checked,
-            batch: readNumber('export-batch', 1, true),
-            workspace: readNumber('export-workspace', 4, true)
+            batch,
+            workspace
         };
+        const statusRevisionAtStart = exportStatusRevision;
+        let started = false;
 
         try {
             const res = await fetch('/api/export/start', {
@@ -1141,10 +1292,21 @@ document.addEventListener('DOMContentLoaded', () => {
             if (!res.ok) {
                 throw new Error(data.detail || 'Failed to start export');
             }
+            started = true;
+            const terminalStatusReceived = exportStatusRevision !== statusRevisionAtStart
+                && ['succeeded', 'cancelled', 'failed'].includes(currentExportStatus);
+            if (!isExportActive && !terminalStatusReceived) {
+                updateExportUIStatus('preparing');
+            }
             showNotification('Экспорт успешно запущен!', 'success');
         } catch (err) {
             console.error('Export start error:', err);
             showNotification(err.message, 'error');
+        } finally {
+            isExportStartPending = false;
+            if (!started && !isExportActive) {
+                exportStartBtn.disabled = false;
+            }
         }
     }
 

src/yolo_webui/static/index.html

@@ -30,7 +30,7 @@
             </div>
         </div>
         <div class="header-actions">
-            <a href="http://localhost:5000" target="_blank" class="mlflow-link" id="mlflow-header-link">
+            <a href="http://localhost:5000" target="_blank" rel="noopener noreferrer" class="mlflow-link" id="mlflow-header-link">
                 <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="link-icon">
                     <path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6"></path>
                     <polyline points="15 3 21 3 21 9"></polyline>
@@ -70,7 +70,7 @@
                     <div class="session-row">
                         <div class="session-field">
                             <label for="session-name">Сохранить текущие настройки как профиль</label>
-                            <input type="text" id="session-name" placeholder="Введите имя профиля...">
+                            <input type="text" id="session-name" pattern="[A-Za-z0-9_-]+" autocomplete="off" placeholder="Введите имя профиля...">
                         </div>
                         <button type="button" id="session-save-btn" class="btn-action btn-save" title="Сохранить настройки">
                             <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="btn-icon-small"><path d="M19 21H5a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h11l5 5v11a2 2 0 0 1-2 2z"></path><polyline points="17 21 17 13 7 13 7 21"></polyline></svg>
@@ -433,11 +433,11 @@
                                 <option value="onnx">ONNX (.onnx)</option>
                                 <option value="engine">TensorRT (.engine)</option>
                                 <option value="openvino">OpenVINO (_openvino_model/)</option>
-                                <option value="triton">Triton Inference Server (triton/)</option>
                                 <option value="torchscript">TorchScript (.torchscript)</option>
                                 <option value="coreml">CoreML (.mlpackage)</option>
                                 <option value="tflite">TFLite (.tflite)</option>
-                                <option value="pb">TensorFlow SavedModel (_saved_model/)</option>
+                                <option value="saved_model">TensorFlow SavedModel (_saved_model/)</option>
+                                <option value="pb">TensorFlow GraphDef (.pb)</option>
                             </select>
                         </div>
                         
@@ -445,15 +445,15 @@
                         <div class="row">
                             <div class="field">
                                 <label for="export-imgsz">Размер изображения (imgsz)</label>
-                                <input type="number" id="export-imgsz" name="export-imgsz" value="640">
+                                <input type="number" id="export-imgsz" name="export-imgsz" value="640" min="32" max="8192" step="1" required>
                             </div>
                             <div class="field">
-                                <label for="export-batch" title="Максимальный размер батча. Важен при Dynamic=True для TensorRT и Triton.">Макс. батч (batch)</label>
-                                <input type="number" id="export-batch" name="export-batch" value="1" min="1">
+                                <label for="export-batch" title="Максимальный размер батча. Важен при Dynamic=True для TensorRT.">Макс. батч (batch)</label>
+                                <input type="number" id="export-batch" name="export-batch" value="1" min="1" max="1024" step="1" required>
                             </div>
                             <div class="field">
                                 <label for="export-workspace" title="Размер памяти для сборки TensorRT (ГБ).">Workspace (ГБ)</label>
-                                <input type="number" id="export-workspace" name="export-workspace" value="4" min="1" max="64">
+                                <input type="number" id="export-workspace" name="export-workspace" value="4" min="1" max="64" step="0.5" required>
                             </div>
                         </div>
                         <div class="row">

src/yolo_webui/static/style.css

@@ -171,12 +171,14 @@ body {
     height: 100%;
 }
 
-#config-pane {
+#config-pane,
+#export-config-pane {
     padding: 1.25rem;
     max-height: 100%;
 }
 
-#run-pane {
+#run-pane,
+#export-run-pane {
     padding: 1.25rem;
     display: flex;
     flex-direction: column;
@@ -187,28 +189,36 @@ body {
 
 /* Custom Scrollbars */
 #run-pane::-webkit-scrollbar,
+#export-run-pane::-webkit-scrollbar,
 .form-container::-webkit-scrollbar,
-#log-container::-webkit-scrollbar {
+#log-container::-webkit-scrollbar,
+#export-log-container::-webkit-scrollbar {
     width: 6px;
     height: 6px;
 }
 
 #run-pane::-webkit-scrollbar-track,
+#export-run-pane::-webkit-scrollbar-track,
 .form-container::-webkit-scrollbar-track,
-#log-container::-webkit-scrollbar-track {
+#log-container::-webkit-scrollbar-track,
+#export-log-container::-webkit-scrollbar-track {
     background: transparent;
 }
 
 #run-pane::-webkit-scrollbar-thumb,
+#export-run-pane::-webkit-scrollbar-thumb,
 .form-container::-webkit-scrollbar-thumb,
-#log-container::-webkit-scrollbar-thumb {
+#log-container::-webkit-scrollbar-thumb,
+#export-log-container::-webkit-scrollbar-thumb {
     background: var(--border-color);
     border-radius: 3px;
 }
 
 #run-pane::-webkit-scrollbar-thumb:hover,
+#export-run-pane::-webkit-scrollbar-thumb:hover,
 .form-container::-webkit-scrollbar-thumb:hover,
-#log-container::-webkit-scrollbar-thumb:hover {
+#log-container::-webkit-scrollbar-thumb:hover,
+#export-log-container::-webkit-scrollbar-thumb:hover {
     background: var(--border-hover);
 }
 
@@ -543,7 +553,8 @@ body {
 }
 
 /* Status Cards & Themes */
-#status-card {
+#status-card,
+#export-status-card {
     border-radius: 8px;
     padding: 1.25rem;
     border-left: 5px solid var(--text-dim);
@@ -602,7 +613,8 @@ body {
     100% { transform: scale(0.9); opacity: 0.6; }
 }
 
-#status-title {
+#status-title,
+#export-status-title {
     font-size: 1rem;
     font-weight: 700;
     letter-spacing: 0.05em;
@@ -624,12 +636,33 @@ body {
     color: var(--text-muted);
 }
 
-#status-text {
+#status-text,
+#export-status-text {
     font-size: 0.9rem;
     color: var(--text-main);
     margin-bottom: 1rem;
 }
 
+#export-status-card.status-preparing,
+#export-status-card.status-stopping { border-left-color: var(--warning); }
+#export-status-card.status-exporting,
+#export-status-card.status-succeeded { border-left-color: var(--success); }
+#export-status-card.status-cancelled { border-left-color: var(--warning); }
+#export-status-card.status-failed { border-left-color: var(--error); }
+#export-status-card.status-preparing .status-dot,
+#export-status-card.status-stopping .status-dot { background-color: var(--warning); box-shadow: 0 0 8px var(--warning); }
+#export-status-card.status-exporting .status-dot,
+#export-status-card.status-succeeded .status-dot { background-color: var(--success); box-shadow: 0 0 8px var(--success); }
+#export-status-card.status-cancelled .status-dot { background-color: var(--warning); box-shadow: 0 0 8px var(--warning); }
+#export-status-card.status-failed .status-dot { background-color: var(--error); box-shadow: 0 0 8px var(--error); }
+#export-status-card.status-preparing #export-status-title,
+#export-status-card.status-stopping #export-status-title { color: var(--warning); }
+#export-status-card.status-exporting #export-status-title,
+#export-status-card.status-succeeded #export-status-title { color: var(--success); }
+#export-status-card.status-cancelled #export-status-title { color: var(--warning); }
+#export-status-card.status-failed #export-status-title { color: var(--error); }
+
+
 /* Progress bar inside status card */
 .progress-container {
     display: flex;

src/yolo_webui/trainer.py

@@ -191,8 +191,11 @@ class TrainingRunner:
         from ultralytics import YOLO, settings
         from .ultralytics_trainers import trainer_for_task
 
+        previous_mlflow_setting = settings["mlflow"]
+        setting_changed = previous_mlflow_setting is not config.mlflow.enabled
+        if setting_changed:
             settings.update({"mlflow": config.mlflow.enabled})
-
+        try:
             with mlflow_environment(config.mlflow):
                 model = YOLO(config.resolved_model, task=config.task)
                 with self._state_lock:
@@ -210,6 +213,9 @@ class TrainingRunner:
                 finally:
                     with self._state_lock:
                         self._model = None
+        finally:
+            if setting_changed:
+                settings.update({"mlflow": previous_mlflow_setting})
 
     def _on_train_start(self, on_event: EventHandler) -> Callable[[Any], None]:
         def callback(trainer: Any) -> None:

tests/frontend_smoke.js

@@ -76,7 +76,25 @@ global.localStorage = {
     removeItem(key) { storage.delete(key); }
 };
 global.confirm = () => true;
-global.window = {location: {protocol: 'http:', host: '127.0.0.1:8000'}};
+global.window = {
+    location: {
+        protocol: 'http:',
+        host: '127.0.0.1:8000',
+        hostname: '127.0.0.1'
+    }
+};
+
+let nextTimerId = 1;
+const scheduledTimers = [];
+global.setTimeout = (handler, delay) => {
+    const timer = {id: nextTimerId++, handler, delay, cancelled: false, fired: false};
+    scheduledTimers.push(timer);
+    return timer.id;
+};
+global.clearTimeout = id => {
+    const timer = scheduledTimers.find(item => item.id === id);
+    if (timer) timer.cancelled = true;
+};
 
 class FakeChart {
     static instances = [];
@@ -106,8 +124,25 @@ const response = (ok, data) => ({
     ok,
     async json() { return data; }
 });
-global.fetch = async url => {
+const fetchCalls = [];
+let holdTrainingStart = false;
+let holdExportStart = false;
+global.fetch = async (url, options = {}) => {
+    fetchCalls.push({url, options});
+    if (url === '/api/train/start' && holdTrainingStart) {
+        return new Promise(() => {});
+    }
+    if (url === '/api/export/start' && holdExportStart) {
+        return new Promise(() => {});
+    }
     if (url === '/api/datasets' || url === '/api/models' || url === '/api/sessions') {
+        if (url === '/api/models') {
+            return response(true, [{
+                name: 'detect/experiment/best.pt',
+                path: '/workspace/runs/detect/experiment/weights/best.pt',
+                source: 'runs'
+            }]);
+        }
         return response(true, []);
     }
     if (url === '/api/sessions/last_run') return response(false, {});
@@ -119,7 +154,7 @@ global.fetch = async url => {
             workers: 8,
             patience: 100,
             augmentation: {enabled: true, close_mosaic: 10},
-            mlflow: {enabled: false},
+            mlflow: {enabled: false, tracking_uri: 'http://mlflow:5000'},
             split: {enabled: false}
         });
     }
@@ -133,11 +168,30 @@ async function flushPromises() {
     await new Promise(resolve => setImmediate(resolve));
 }
 
+function descendants(node) {
+    return node.children.flatMap(child => [child, ...descendants(child)]);
+}
+
+function runTimer(timer) {
+    timer.fired = true;
+    timer.handler();
+}
+
 (async () => {
     assert.equal(typeof domReady, 'function');
     domReady();
     await flushPromises();
 
+    assert.equal(element('mlflow-header-link').href, 'http://127.0.0.1:5000/');
+
+    const localModelPath = '/workspace/runs/detect/experiment/weights/best.pt';
+    const localModelOption = descendants(element('model-select'))
+        .find(item => item.textContent === 'detect/experiment/best.pt');
+    assert.ok(localModelOption);
+    assert.equal(localModelOption.value, localModelPath);
+
+    element('model-select').value = localModelOption.value;
+    element('model-select').listeners.change();
     element('workers').value = '0';
     element('patience').value = '0';
     element('close-mosaic').value = '0';
@@ -148,9 +202,28 @@ async function flushPromises() {
     assert.equal(savedConfig.patience, 0);
     assert.equal(savedConfig.augmentation.close_mosaic, 0);
     assert.equal(savedConfig.augmentation.auto_augment, 'none');
+    assert.equal(savedConfig.model, localModelPath);
+
+    element('model-select').value = '__custom__';
+    element('model-select').listeners.change();
+    element('model').value = '/workspace/models/custom-segment.pt';
+    element('task').value = 'segment';
+    element('task').listeners.change();
+    assert.equal(element('model-select').value, '__custom__');
+    assert.equal(element('model').value, '/workspace/models/custom-segment.pt');
 
     assert.equal(FakeWebSocket.instances.length, 2);
     const socket = FakeWebSocket.instances[0];
+    const exportSocket = FakeWebSocket.instances[1];
+    const originalConsoleError = console.error;
+    console.error = () => {};
+    try {
+        assert.doesNotThrow(() => socket.onmessage({data: '{broken'}));
+        assert.doesNotThrow(() => exportSocket.onmessage({data: 'null'}));
+    } finally {
+        console.error = originalConsoleError;
+    }
+
     socket.onmessage({
         data: JSON.stringify({
             type: 'init',
@@ -168,6 +241,77 @@ async function flushPromises() {
     assert.deepEqual(chart.data.datasets.map(item => item.label), ['mAP50', 'loss']);
     assert.deepEqual(chart.data.datasets[0].data, [0.5, null]);
     assert.deepEqual(chart.data.datasets[1].data, [null, 0.2]);
+
+    socket.onclose();
+    socket.onclose();
+    let activeReconnectTimers = scheduledTimers.filter(
+        timer => timer.delay === 5000 && !timer.cancelled && !timer.fired
+    );
+    assert.equal(activeReconnectTimers.length, 1);
+    runTimer(activeReconnectTimers[0]);
+    assert.equal(FakeWebSocket.instances.length, 3);
+    assert.equal(FakeWebSocket.instances[2].url, 'ws://127.0.0.1:8000/api/ws');
+
+    exportSocket.onclose();
+    exportSocket.onclose();
+    activeReconnectTimers = scheduledTimers.filter(
+        timer => timer.delay === 5000 && !timer.cancelled && !timer.fired
+    );
+    assert.equal(activeReconnectTimers.length, 1);
+    runTimer(activeReconnectTimers[0]);
+    assert.equal(FakeWebSocket.instances.length, 4);
+    assert.equal(FakeWebSocket.instances[3].url, 'ws://127.0.0.1:8000/api/export/ws');
+
+    element('session-name').value = 'profile with spaces';
+    await element('session-save-btn').listeners.click();
+    assert.equal(
+        fetchCalls.filter(call => call.options.method === 'POST' && call.url.startsWith('/api/sessions/')).length,
+        0
+    );
+
+    element('export-model').value = localModelPath;
+    element('export-format').value = 'pb';
+    element('export-imgsz').value = '31';
+    element('export-batch').value = '1';
+    element('export-workspace').value = '4';
+    await element('export-start-btn').listeners.click();
+    assert.equal(fetchCalls.filter(call => call.url === '/api/export/start').length, 0);
+
+    element('export-imgsz').value = '640';
+    element('export-workspace').value = '1.5';
+    element('export-half').checked = true;
+    element('export-int8').checked = true;
+    await element('export-start-btn').listeners.click();
+    assert.equal(fetchCalls.filter(call => call.url === '/api/export/start').length, 0);
+
+    element('export-half').checked = false;
+    element('export-int8').checked = false;
+    holdExportStart = true;
+    element('export-start-btn').listeners.click();
+    element('export-start-btn').listeners.click();
+    const exportStartCalls = fetchCalls.filter(call => call.url === '/api/export/start');
+    assert.equal(exportStartCalls.length, 1);
+    assert.equal(element('export-start-btn').disabled, true);
+    assert.deepEqual(
+        JSON.parse(exportStartCalls[0].options.body),
+        {
+            model: localModelPath,
+            format: 'pb',
+            imgsz: 640,
+            half: false,
+            int8: false,
+            dynamic: false,
+            simplify: false,
+            batch: 1,
+            workspace: 1.5
+        }
+    );
+
+    holdTrainingStart = true;
+    element('start-btn').listeners.click();
+    element('start-btn').listeners.click();
+    assert.equal(fetchCalls.filter(call => call.url === '/api/train/start').length, 1);
+    assert.equal(element('start-btn').disabled, true);
 })().catch(error => {
     console.error(error);
     process.exitCode = 1;

tests/test_app.py

@@ -5,8 +5,16 @@ import json
 import threading
 
 from fastapi.testclient import TestClient
+import pytest
 
-from yolo_webui.app import TrainingManager, app
+import yolo_webui.app as app_module
+from yolo_webui.app import (
+    ExportManager,
+    LiveState,
+    TrainingManager,
+    app,
+    write_json_atomic,
+)
 
 
 def test_get_config_defaults() -> None:
@@ -94,6 +102,28 @@ def test_sessions_flow(monkeypatch, tmp_path) -> None:
     assert response.status_code == 404
 
 
+def test_invalid_session_config_is_not_saved(monkeypatch, tmp_path) -> None:
+    client = TestClient(app)
+    monkeypatch.setattr("yolo_webui.app.get_sessions_dir", lambda: tmp_path)
+
+    response = client.post("/api/sessions/broken", json={"unexpected": True})
+
+    assert response.status_code == 400
+    assert "датасета" in response.json()["detail"].lower()
+    assert not (tmp_path / "broken.json").exists()
+
+
+def test_atomic_json_write_cleans_temp_file_after_serialization_error(tmp_path) -> None:
+    destination = tmp_path / "session.json"
+    destination.write_text('{"old": true}', encoding="utf-8")
+
+    with pytest.raises(TypeError):
+        write_json_atomic(destination, {"invalid": object()})
+
+    assert destination.read_text(encoding="utf-8") == '{"old": true}'
+    assert list(tmp_path.iterdir()) == [destination]
+
+
 def test_reserved_session_name_cannot_be_overwritten(monkeypatch, tmp_path) -> None:
     client = TestClient(app)
     monkeypatch.setattr("yolo_webui.app.get_sessions_dir", lambda: tmp_path)
@@ -104,6 +134,107 @@ def test_reserved_session_name_cannot_be_overwritten(monkeypatch, tmp_path) -> N
     assert "зарезервировано" in response.json()["detail"]
 
 
+def test_rejected_busy_start_does_not_overwrite_last_run(
+    monkeypatch,
+    tmp_path,
+) -> None:
+    client = TestClient(app)
+    previous = {"dataset": "previous.yaml", "model": "previous.pt"}
+    last_run_path = tmp_path / "last_run.json"
+    last_run_path.write_text(json.dumps(previous), encoding="utf-8")
+    monkeypatch.setattr("yolo_webui.app.get_sessions_dir", lambda: tmp_path)
+
+    def reject_start(_config) -> None:
+        raise ValueError("Обучение уже выполняется.")
+
+    monkeypatch.setattr(app_module.manager, "start_training", reject_start)
+
+    response = client.post(
+        "/api/train/start",
+        json={"dataset": "coco8.yaml", "model": "yolo11n.pt"},
+    )
+
+    assert response.status_code == 400
+    assert json.loads(last_run_path.read_text(encoding="utf-8")) == previous
+
+
+def test_start_api_rejects_non_finite_numeric_value() -> None:
+    client = TestClient(app)
+
+    response = client.post(
+        "/api/train/start",
+        content='{"dataset":"coco8.yaml","model":"yolo11n.pt","epochs":NaN}',
+        headers={"content-type": "application/json"},
+    )
+
+    assert response.status_code == 400
+    assert "целым числом" in response.json()["detail"]
+
+
+def test_list_models_uses_path_relative_to_runs(monkeypatch, tmp_path) -> None:
+    monkeypatch.chdir(tmp_path)
+    direct_model = tmp_path / "runs" / "direct.pt"
+    nested_model = tmp_path / "runs" / "detect" / "train" / "weights" / "best.pt"
+    direct_model.parent.mkdir()
+    nested_model.parent.mkdir(parents=True)
+    direct_model.write_bytes(b"")
+    nested_model.write_bytes(b"")
+
+    response = TestClient(app).get("/api/models")
+
+    assert response.status_code == 200
+    run_names = {
+        item["name"] for item in response.json() if item["source"] == "runs"
+    }
+    assert run_names == {"direct.pt", "detect/train/weights/best.pt"}
+
+
+def test_export_start_rejects_invalid_config_synchronously() -> None:
+    response = TestClient(app).post(
+        "/api/export/start",
+        json={"model": "missing.pt", "format": "not-a-format"},
+    )
+
+    assert response.status_code == 400
+    assert "Неподдерживаемый формат" in response.json()["detail"]
+
+
+def test_export_start_passes_canonical_validated_config(
+    monkeypatch,
+    tmp_path,
+) -> None:
+    monkeypatch.chdir(tmp_path)
+    model_path = tmp_path / "models" / "model.pt"
+    model_path.parent.mkdir()
+    model_path.write_bytes(b"checkpoint")
+    received: list[dict[str, object]] = []
+    monkeypatch.setattr(app_module.export_manager, "start_export", received.append)
+
+    response = TestClient(app).post(
+        "/api/export/start",
+        json={
+            "model": "model.pt",
+            "format": "ONNX",
+            "workspace": 2.5,
+        },
+    )
+
+    assert response.status_code == 200
+    assert received == [
+        {
+            "model": str(model_path),
+            "format": "onnx",
+            "imgsz": 640,
+            "half": False,
+            "int8": False,
+            "dynamic": False,
+            "simplify": False,
+            "batch": 1,
+            "workspace": 2.5,
+        }
+    ]
+
+
 def test_started_event_does_not_deadlock() -> None:
     training_manager = TrainingManager()
     training_manager.state.status = "preparing"
@@ -173,3 +304,55 @@ def test_process_result_distinguishes_success_cancellation_and_failure() -> None
     failed_after_stop.state.stop_requested = True
     failed_after_stop._finalize_process_result(1)
     assert failed_after_stop.state.status == "failed"
+
+
+def test_live_state_snapshot_is_detached() -> None:
+    state = LiveState(
+        status="training",
+        logs=["first"],
+        metrics=[{"epoch": 1, "loss": 0.5}],
+    )
+
+    snapshot = state.snapshot()
+    state.logs.append("second")
+    state.metrics[0]["loss"] = 0.25
+
+    assert snapshot["logs"] == ["first"]
+    assert snapshot["metrics"] == [{"epoch": 1, "loss": 0.5}]
+
+
+def test_export_stop_does_not_relabel_completed_failure_as_cancelled() -> None:
+    class CompletedProcess:
+        returncode = 1
+
+        def poll(self) -> int:
+            return self.returncode
+
+        def terminate(self) -> None:
+            raise AssertionError("completed process must not be terminated")
+
+    export = ExportManager()
+    export.state.status = "exporting"
+    export._process = CompletedProcess()  # type: ignore[assignment]
+
+    export.stop_export()
+    export._finalize_process_result(1)
+
+    assert export.state.stop_requested is False
+    assert export.state.status == "failed"
+
+
+def test_export_stopped_while_preparing_never_starts_process(monkeypatch) -> None:
+    export = ExportManager()
+    export.state.status = "preparing"
+    export.stop_export()
+
+    def unexpected_popen(*_args, **_kwargs):
+        raise AssertionError("subprocess must not start after cancellation")
+
+    monkeypatch.setattr(app_module.subprocess, "Popen", unexpected_popen)
+
+    export._run_subprocess({"model": "unused.pt", "format": "onnx"})
+
+    assert export.state.status == "cancelled"
+    assert export._process is None

tests/test_config.py

@@ -72,14 +72,102 @@ def test_augmentation_probabilities_are_validated(field: str) -> None:
         augmentation.validate()
 
 
-@pytest.mark.parametrize("batch", [0, -2])
-def test_invalid_batch_is_rejected(batch: int) -> None:
+@pytest.mark.parametrize("batch", [0, -2, -0.5, 1.5])
+def test_invalid_batch_is_rejected(batch: int | float) -> None:
     config = TrainingConfig(dataset="dataset.yaml", model="model.pt", batch_size=batch)
 
     with pytest.raises(ValueError, match="Batch"):
         config.validate()
 
 
+@pytest.mark.parametrize("batch", [-1, 1, 16, 0.25])
+def test_supported_batch_modes_are_accepted(batch: int | float) -> None:
+    TrainingConfig(
+        dataset="dataset.yaml",
+        model="model.pt",
+        batch_size=batch,
+    ).validate()
+
+
+@pytest.mark.parametrize(
+    ("field", "value", "message"),
+    [
+        ("epochs", float("nan"), "целым числом"),
+        ("epochs", True, "целым числом"),
+        ("workers", 1.5, "целым числом"),
+        ("patience", float("inf"), "целым числом"),
+    ],
+)
+def test_training_numeric_fields_reject_wrong_types_and_non_finite_values(
+    field: str,
+    value: object,
+    message: str,
+) -> None:
+    values = {"dataset": "dataset.yaml", "model": "model.pt", field: value}
+
+    with pytest.raises(ValueError, match=message):
+        TrainingConfig(**values).validate()
+
+
+def test_augmentation_rejects_non_finite_unbounded_values() -> None:
+    config = TrainingConfig(
+        dataset="dataset.yaml",
+        model="model.pt",
+        augmentation=AugmentationConfig(degrees=float("inf")),
+    )
+
+    with pytest.raises(ValueError, match="конечным числом"):
+        config.validate()
+
+
+def test_augmentation_rejects_integer_too_large_for_float() -> None:
+    config = TrainingConfig(
+        dataset="dataset.yaml",
+        model="model.pt",
+        augmentation=AugmentationConfig(degrees=10**10_000),
+    )
+
+    with pytest.raises(ValueError, match="конечным числом"):
+        config.validate()
+
+
+@pytest.mark.parametrize(
+    "run_name",
+    ["../escape", "/tmp/escape", r"..\\escape", "C:escape"],
+)
+def test_run_name_cannot_escape_project(run_name: str) -> None:
+    config = TrainingConfig(
+        dataset="dataset.yaml",
+        model="model.pt",
+        run_name=run_name,
+    )
+
+    with pytest.raises(ValueError, match="Имя запуска"):
+        config.validate()
+
+
+def test_nested_config_sections_must_be_objects() -> None:
+    with pytest.raises(ValueError, match="разделения датасета.*JSON-объектом"):
+        TrainingConfig.from_dict(
+            {
+                "dataset": "dataset.yaml",
+                "model": "model.pt",
+                "split": None,
+            }
+        )
+
+
+def test_classes_path_type_is_validated_before_path_operations() -> None:
+    config = TrainingConfig(
+        dataset="dataset.yaml",
+        model="model.pt",
+        split=DatasetSplitConfig(classes_path=123),  # type: ignore[arg-type]
+    )
+
+    with pytest.raises(ValueError, match="Путь к файлу классов должен быть строкой"):
+        config.validate()
+
+
 def test_mlflow_environment_is_restored(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setenv("MLFLOW_TRACKING_URI", "previous")
     config = MlflowConfig(
@@ -95,6 +183,14 @@ def test_mlflow_environment_is_restored(monkeypatch: pytest.MonkeyPatch) -> None
         assert os.environ["MLFLOW_RUN"] == config.run_name
 
     assert os.environ["MLFLOW_TRACKING_URI"] == "previous"
+
+
+def test_mlflow_default_tracking_uri_can_be_overridden_by_environment(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setenv("YOLO_WEBUI_MLFLOW_TRACKING_URI", "http://mlflow:5000")
+
+    assert MlflowConfig().tracking_uri == "http://mlflow:5000"
     assert "MLFLOW_EXPERIMENT_NAME" not in os.environ
     assert "MLFLOW_RUN" not in os.environ
 
@@ -113,7 +209,7 @@ def test_metrics_summary_skips_non_numeric_values() -> None:
     assert summary == "mAP50=0.8123 · loss=0.1235 · precision=0.9568"
 
 
-@pytest.mark.parametrize("ratio", [0.05, 0.98])
+@pytest.mark.parametrize("ratio", [0.05, 0.98, float("nan")])
 def test_dataset_split_ratio_is_validated(ratio: float) -> None:
     config = DatasetSplitConfig(enabled=True, train_ratio=ratio)
     with pytest.raises(ValueError, match="Доля обучающей выборки"):

tests/test_deployment.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_compose_is_local_and_does_not_require_nvidia() -> None:
+    compose = yaml.safe_load(
+        (PROJECT_ROOT / "docker-compose.yml").read_text(encoding="utf-8")
+    )
+    webui = compose["services"]["webui"]
+
+    assert webui["ports"] == ["127.0.0.1:8000:8000"]
+    assert "deploy" not in webui
+
+    mlflow = compose["services"]["mlflow"]
+    assert mlflow["ports"] == ["127.0.0.1:5000:5000"]
+    assert mlflow["volumes"] == ["./mlflow:/mlflow"]
+    assert "server" in mlflow["command"]
+    assert webui["environment"]["YOLO_WEBUI_MLFLOW_TRACKING_URI"] == "http://mlflow:5000"
+
+
+def test_docker_dependencies_are_exported_from_lock_file() -> None:
+    dockerfile = (PROJECT_ROOT / "Dockerfile").read_text(encoding="utf-8")
+
+    assert "COPY pyproject.toml uv.lock README.md ./" in dockerfile
+    assert "uv export --locked --no-dev" in dockerfile
+    assert "--group build" in dockerfile
+    assert "--prune ultralytics" in dockerfile
+    assert "uv pip install --system --no-deps --no-build-isolation ." in dockerfile
+    assert "ULTRALYTICS_SAFE_LOAD=1" in dockerfile
+
+
+def test_docker_context_excludes_local_training_artifacts() -> None:
+    ignored = {
+        line.strip()
+        for line in (PROJECT_ROOT / ".dockerignore").read_text(encoding="utf-8").splitlines()
+        if line.strip() and not line.lstrip().startswith("#")
+    }
+
+    assert {".git", ".venv", "datasets", "models", "runs", "mlruns"} <= ignored

tests/test_export_runner.py

@@ -0,0 +1,244 @@
+from __future__ import annotations
+
+import json
+import os
+import sys
+from pathlib import Path
+from types import ModuleType
+from typing import Any
+
+import pytest
+
+from yolo_webui import export_runner
+
+
+def _write_config(tmp_path: Path, data: Any) -> Path:
+    path = tmp_path / "export.json"
+    path.write_text(json.dumps(data), encoding="utf-8")
+    return path
+
+
+def _write_model(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> Path:
+    monkeypatch.chdir(tmp_path)
+    models = tmp_path / "models"
+    models.mkdir()
+    model = models / "model.pt"
+    model.write_bytes(b"checkpoint")
+    return model
+
+
+def _install_fake_ultralytics(
+    monkeypatch: pytest.MonkeyPatch,
+    result: Any,
+) -> tuple[list[str], list[dict[str, Any]]]:
+    constructed: list[str] = []
+    export_calls: list[dict[str, Any]] = []
+
+    class FakeYOLO:
+        def __init__(self, model: str) -> None:
+            assert os.environ["ULTRALYTICS_SAFE_LOAD"] == "1"
+            constructed.append(model)
+
+        def export(self, **kwargs: Any) -> Any:
+            export_calls.append(kwargs)
+            return result
+
+    fake_ultralytics = ModuleType("ultralytics")
+    fake_ultralytics.YOLO = FakeYOLO  # type: ignore[attr-defined]
+    monkeypatch.setitem(sys.modules, "ultralytics", fake_ultralytics)
+    return constructed, export_calls
+
+
+def test_main_validates_config_and_reports_existing_export(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    model = _write_model(tmp_path, monkeypatch)
+    result = tmp_path / "models" / "model.onnx"
+    result.write_bytes(b"onnx")
+    constructed, export_calls = _install_fake_ultralytics(monkeypatch, result)
+    monkeypatch.setenv("ULTRALYTICS_SAFE_LOAD", "0")
+    config = _write_config(
+        tmp_path,
+        {
+            "model": model.name,
+            "format": "ONNX",
+            "imgsz": 320,
+            "half": False,
+            "int8": False,
+            "dynamic": True,
+            "simplify": True,
+            "batch": 2,
+            "workspace": 2.5,
+        },
+    )
+
+    return_code = export_runner.main([str(config)])
+
+    output = capsys.readouterr()
+    assert return_code == 0
+    assert constructed == [str(model)]
+    assert export_calls == [
+        {
+            "format": "onnx",
+            "imgsz": 320,
+            "half": False,
+            "int8": False,
+            "dynamic": True,
+            "simplify": True,
+            "batch": 2,
+            "workspace": 2.5,
+        }
+    ]
+    assert f"__YOLO_WEBUI_RESULT__:{result}" in output.out
+
+
+@pytest.mark.parametrize(
+    "override",
+    [
+        {"format": "onnxx"},
+        {"format": ["onnx"]},
+        {"imgsz": "640"},
+        {"imgsz": True},
+        {"half": 1},
+        {"batch": 0},
+        {"workspace": float("inf")},
+        {"workspace": 10**400},
+        {"workspace": 64.5},
+        {"unexpected": "value"},
+    ],
+)
+def test_invalid_export_values_are_rejected_before_loading_ultralytics(
+    override: dict[str, Any],
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    model = _write_model(tmp_path, monkeypatch)
+    constructed, _ = _install_fake_ultralytics(monkeypatch, model)
+    config = _write_config(
+        tmp_path,
+        {
+            "model": str(model),
+            "format": "onnx",
+            **override,
+        },
+    )
+
+    return_code = export_runner.main([str(config)])
+
+    output = capsys.readouterr()
+    assert return_code == 1
+    assert constructed == []
+    assert "Ошибка конфигурации экспорта:" in output.err
+
+
+def test_non_object_json_is_reported_as_invalid_config(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    monkeypatch.chdir(tmp_path)
+
+    return_code = export_runner.main([str(_write_config(tmp_path, ["model.pt"]))])
+
+    output = capsys.readouterr()
+    assert return_code == 1
+    assert "JSON-объектом" in output.err
+
+
+def test_model_must_be_a_regular_file_inside_an_allowed_root(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    monkeypatch.chdir(tmp_path)
+    (tmp_path / "models").mkdir()
+    outside = tmp_path / "outside.pt"
+    outside.write_bytes(b"checkpoint")
+
+    return_code = export_runner.main(
+        [str(_write_config(tmp_path, {"model": str(outside), "format": "onnx"}))]
+    )
+
+    output = capsys.readouterr()
+    assert return_code == 1
+    assert "разрешённом каталоге" in output.err
+
+
+def test_symlink_cannot_escape_an_allowed_model_root(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    monkeypatch.chdir(tmp_path)
+    models = tmp_path / "models"
+    models.mkdir()
+    outside = tmp_path / "outside.pt"
+    outside.write_bytes(b"checkpoint")
+    link = models / "linked.pt"
+    link.symlink_to(outside)
+
+    return_code = export_runner.main(
+        [str(_write_config(tmp_path, {"model": str(link), "format": "onnx"}))]
+    )
+
+    output = capsys.readouterr()
+    assert return_code == 1
+    assert "разрешённом каталоге" in output.err
+
+
+def test_half_and_int8_cannot_be_enabled_together(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    model = _write_model(tmp_path, monkeypatch)
+
+    return_code = export_runner.main(
+        [
+            str(
+                _write_config(
+                    tmp_path,
+                    {
+                        "model": str(model),
+                        "format": "onnx",
+                        "half": True,
+                        "int8": True,
+                    },
+                )
+            )
+        ]
+    )
+
+    output = capsys.readouterr()
+    assert return_code == 1
+    assert "нельзя включать одновременно" in output.err
+
+
+@pytest.mark.parametrize("result", [None, [], "/missing/export.onnx"])
+def test_missing_export_artifact_is_a_failure(
+    result: Any,
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+) -> None:
+    model = _write_model(tmp_path, monkeypatch)
+    _install_fake_ultralytics(monkeypatch, result)
+
+    return_code = export_runner.main(
+        [
+            str(
+                _write_config(
+                    tmp_path,
+                    {"model": str(model), "format": "onnx"},
+                )
+            )
+        ]
+    )
+
+    output = capsys.readouterr()
+    assert return_code == 1
+    assert "__YOLO_WEBUI_RESULT__:" not in output.out
+    assert "Ошибка при экспорте модели:" in output.out

tests/test_frontend.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from html.parser import HTMLParser
 from pathlib import Path
 import shutil
 import subprocess
@@ -9,6 +10,37 @@ import pytest
 
 NODE = shutil.which("node")
 APP_JS = Path("src/yolo_webui/static/app.js")
+INDEX_HTML = Path("src/yolo_webui/static/index.html")
+STYLE_CSS = Path("src/yolo_webui/static/style.css")
+
+
+class _MarkupCollector(HTMLParser):
+    def __init__(self) -> None:
+        super().__init__()
+        self.by_id: dict[str, dict[str, str | None]] = {}
+        self.options: dict[str, str] = {}
+        self._option_value: str | None = None
+
+    def handle_starttag(
+        self,
+        tag: str,
+        attrs: list[tuple[str, str | None]],
+    ) -> None:
+        attributes = dict(attrs)
+        if element_id := attributes.get("id"):
+            self.by_id[element_id] = attributes
+        if tag == "option":
+            self._option_value = attributes.get("value")
+
+    def handle_data(self, data: str) -> None:
+        if self._option_value is not None:
+            self.options[self._option_value] = (
+                self.options.get(self._option_value, "") + data
+            ).strip()
+
+    def handle_endtag(self, tag: str) -> None:
+        if tag == "option":
+            self._option_value = None
 
 
 @pytest.mark.skipif(NODE is None, reason="Node.js is required for frontend checks")
@@ -17,5 +49,30 @@ def test_frontend_javascript_syntax() -> None:
 
 
 @pytest.mark.skipif(NODE is None, reason="Node.js is required for frontend checks")
-def test_frontend_zero_values_and_dynamic_chart_series() -> None:
+def test_frontend_interactions_smoke() -> None:
     subprocess.run([NODE, "tests/frontend_smoke.js"], check=True)
+
+
+def test_export_controls_have_correct_formats_constraints_and_styles() -> None:
+    parser = _MarkupCollector()
+    parser.feed(INDEX_HTML.read_text(encoding="utf-8"))
+
+    assert parser.options["saved_model"].startswith("TensorFlow SavedModel")
+    assert parser.options["pb"] == "TensorFlow GraphDef (.pb)"
+    assert "triton" not in parser.options
+    assert parser.by_id["export-imgsz"]["min"] == "32"
+    assert parser.by_id["export-imgsz"]["max"] == "8192"
+    assert parser.by_id["export-imgsz"]["step"] == "1"
+    assert parser.by_id["export-batch"]["min"] == "1"
+    assert parser.by_id["export-batch"]["max"] == "1024"
+    assert parser.by_id["export-batch"]["step"] == "1"
+    assert parser.by_id["export-workspace"]["min"] == "1"
+    assert parser.by_id["export-workspace"]["max"] == "64"
+    assert parser.by_id["export-workspace"]["step"] == "0.5"
+
+    css = STYLE_CSS.read_text(encoding="utf-8")
+    assert "#export-config-pane" in css
+    assert "#export-run-pane" in css
+    assert "#export-log-container::-webkit-scrollbar" in css
+    assert "#export-status-card.status-exporting" in css
+    assert "#export-status-card.status-failed" in css

tests/test_splitter.py

@@ -35,6 +35,14 @@ def test_read_classes_custom_yaml(
     assert read_classes(tmp_path, str(custom_file)) == expected
 
 
+def test_read_classes_rejects_fractional_yaml_class_id(tmp_path: Path) -> None:
+    custom_file = tmp_path / "custom_classes.yaml"
+    custom_file.write_text("names:\n  0.5: person\n", encoding="utf-8")
+
+    with pytest.raises(ValueError, match="Некорректный ID класса"):
+        read_classes(tmp_path, str(custom_file))
+
+
 def test_missing_custom_classes_path_does_not_fall_back(tmp_path: Path) -> None:
     (tmp_path / "classes.txt").write_text("fallback\n", encoding="utf-8")
 
@@ -145,6 +153,59 @@ def test_split_dataset_rejects_single_image_without_writing_output(
     assert not (tmp_path / ".yolo-webui").exists()
 
 
+def test_split_dataset_rejects_label_id_missing_from_classes(
+    tmp_path: Path,
+) -> None:
+    images_dir = tmp_path / "images"
+    labels_dir = tmp_path / "labels"
+    images_dir.mkdir()
+    labels_dir.mkdir()
+    for index in range(2):
+        (images_dir / f"image-{index}.jpg").write_bytes(b"")
+        (labels_dir / f"image-{index}.txt").write_text(
+            "1 0.5 0.5 0.2 0.2\n",
+            encoding="utf-8",
+        )
+    (tmp_path / "classes.txt").write_text("only-class\n", encoding="utf-8")
+
+    with pytest.raises(ValueError, match="указан класс 1"):
+        split_dataset(str(tmp_path), 0.5, "")
+
+    assert not (tmp_path / ".yolo-webui").exists()
+
+
+@pytest.mark.parametrize("invalid_id", ["-1", "0.5", "nan", "class-a"])
+def test_split_dataset_rejects_invalid_label_class_id(
+    tmp_path: Path,
+    invalid_id: str,
+) -> None:
+    images_dir = tmp_path / "images"
+    labels_dir = tmp_path / "labels"
+    images_dir.mkdir()
+    labels_dir.mkdir()
+    for index in range(2):
+        (images_dir / f"image-{index}.jpg").write_bytes(b"")
+        (labels_dir / f"image-{index}.txt").write_text(
+            f"{invalid_id} 0.5 0.5 0.2 0.2\n",
+            encoding="utf-8",
+        )
+    (tmp_path / "classes.txt").write_text("item\n", encoding="utf-8")
+
+    with pytest.raises(ValueError, match="Некорректный ID класса"):
+        split_dataset(str(tmp_path), 0.5, "")
+
+    assert not (tmp_path / ".yolo-webui").exists()
+
+
+def test_split_dataset_rejects_non_finite_ratio_before_writing(
+    tmp_path: Path,
+) -> None:
+    with pytest.raises(ValueError, match="конечным числом"):
+        split_dataset(str(tmp_path), float("nan"), "")
+
+    assert not (tmp_path / ".yolo-webui").exists()
+
+
 def test_split_dataset_finds_nested_images_and_labels(tmp_path: Path) -> None:
     images_dir = tmp_path / "images" / "day"
     labels_dir = tmp_path / "labels" / "day"

tests/test_trainer.py

@@ -6,6 +6,8 @@ from pathlib import Path
 from types import ModuleType, SimpleNamespace
 from typing import Any
 
+import pytest
+
 from yolo_webui.config import MlflowConfig, TrainingConfig
 from yolo_webui.trainer import TrainingEvent, TrainingRunner
 
@@ -37,8 +39,15 @@ def test_runner_wires_yolo_callbacks_and_returns_output(
     train_arguments: list[dict[str, Any]] = []
 
     class FakeSettings:
+        def __init__(self) -> None:
+            self.values = {"mlflow": True}
+
+        def __getitem__(self, key: str) -> bool:
+            return self.values[key]
+
         def update(self, values: dict[str, bool]) -> None:
             settings_updates.append(values)
+            self.values.update(values)
 
     class FakeYOLO:
         def __init__(self, model: str, task: str) -> None:
@@ -97,7 +106,7 @@ def test_runner_wires_yolo_callbacks_and_returns_output(
 
     assert output == tmp_path / "run"
     assert constructed == [("models/model.pt", "pose")]
-    assert settings_updates == [{"mlflow": False}]
+    assert settings_updates == [{"mlflow": False}, {"mlflow": True}]
     assert train_arguments[0]["data"] == "dataset.yaml"
     assert train_arguments[0]["verbose"] is True
     assert train_arguments[0]["trainer"] is FakeTrainer
@@ -106,6 +115,48 @@ def test_runner_wires_yolo_callbacks_and_returns_output(
     assert "mAP50=0.6" in events[3].message
 
 
+def test_ultralytics_mlflow_setting_is_restored_when_model_loading_fails(
+    monkeypatch: Any,
+) -> None:
+    settings_updates: list[bool] = []
+
+    class FakeSettings:
+        def __init__(self) -> None:
+            self.mlflow = True
+
+        def __getitem__(self, key: str) -> bool:
+            assert key == "mlflow"
+            return self.mlflow
+
+        def update(self, values: dict[str, bool]) -> None:
+            self.mlflow = values["mlflow"]
+            settings_updates.append(self.mlflow)
+
+    class FailingYOLO:
+        def __init__(self, _model: str, task: str) -> None:
+            assert task == "detect"
+            raise RuntimeError("model load failed")
+
+    fake_ultralytics = ModuleType("ultralytics")
+    fake_ultralytics.YOLO = FailingYOLO  # type: ignore[attr-defined]
+    fake_ultralytics.settings = FakeSettings()  # type: ignore[attr-defined]
+    fake_trainers = ModuleType("yolo_webui.ultralytics_trainers")
+    fake_trainers.trainer_for_task = lambda _task: object  # type: ignore[attr-defined]
+    monkeypatch.setitem(sys.modules, "ultralytics", fake_ultralytics)
+    monkeypatch.setitem(sys.modules, "yolo_webui.ultralytics_trainers", fake_trainers)
+    config = TrainingConfig(
+        dataset="dataset.yaml",
+        model="model.pt",
+        mlflow=MlflowConfig(enabled=False),
+    )
+
+    with pytest.raises(RuntimeError, match="model load failed"):
+        TrainingRunner().train(config, lambda _event: None)
+
+    assert settings_updates == [False, True]
+    assert fake_ultralytics.settings["mlflow"] is True  # type: ignore[index]
+
+
 def test_final_checkpoint_validation_is_not_reported_as_an_extra_epoch() -> None:
     events: list[TrainingEvent] = []
     trainer = SimpleNamespace(

uv.lock

@@ -834,7 +834,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
 wheels = [
@@ -1232,7 +1232,7 @@ name = "gunicorn"
 version = "26.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "packaging" },
+    { name = "packaging", marker = "sys_platform != 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6d/b7/a4a3f632f823e432ce6bc65f62961b7980c898c77f075a2f7118cb3846fe/gunicorn-26.0.0.tar.gz", hash = "sha256:ca9346f85e3a4aeeb64d491045c16b9a35647abd37ea15efe53080eb8b090baf", size = 727286, upload-time = "2026-05-05T06:38:25.529Z" }
 wheels = [
@@ -1248,6 +1248,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
 ]
 
+[[package]]
+name = "hatchling"
+version = "1.27.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "pluggy" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "trove-classifiers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8f/8a/cc1debe3514da292094f1c3a700e4ca25442489731ef7c0814358816bb03/hatchling-1.27.0.tar.gz", hash = "sha256:971c296d9819abb3811112fc52c7a9751c8d381898f36533bb16f9791e941fd6", size = 54983, upload-time = "2024-12-15T17:08:11.894Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/e7/ae38d7a6dfba0533684e0b2136817d667588ae3ec984c1a4e5df5eb88482/hatchling-1.27.0-py3-none-any.whl", hash = "sha256:d3a2f3567c4f926ea39849cdf924c7e99e6686c9c8e288ae1037c8fa2a5d937b", size = 75794, upload-time = "2024-12-15T17:08:10.364Z" },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -2500,6 +2516,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/70/44/5191d2e4026f86a2a109053e194d3ba7a31a2d10a9c2348368c63ed4e85a/pandas-2.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87", size = 13202175, upload-time = "2025-09-29T23:31:59.173Z" },
 ]
 
+[[package]]
+name = "pathspec"
+version = "1.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/82/42f767fc1c1143d6fd36efb827202a2d997a375e160a71eb2888a925aac1/pathspec-1.1.1.tar.gz", hash = "sha256:17db5ecd524104a120e173814c90367a96a98d07c45b2e10c2f3919fff91bf5a", size = 135180, upload-time = "2026-04-27T01:46:08.907Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/d9/7fb5aa316bc299258e68c73ba3bddbc499654a07f151cba08f6153988714/pathspec-1.1.1-py3-none-any.whl", hash = "sha256:a00ce642f577bf7f473932318056212bc4f8bfdf53128c78bbd5af0b9b20b189", size = 57328, upload-time = "2026-04-27T01:46:07.06Z" },
+]
+
 [[package]]
 name = "pillow"
 version = "12.3.0"
@@ -3802,6 +3827,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f0/ac/229b7d4589d2e5937310e72c6d46e89599d16a4a12b479ffa1499fee8eb8/triton-3.7.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10ba85fa2cca4a2fbdeb36bf1cb082f2c252bda55bf9fccd74f65ec5bc647e68", size = 197824404, upload-time = "2026-06-17T19:53:42.772Z" },
 ]
 
+[[package]]
+name = "trove-classifiers"
+version = "2026.6.1.19"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c2/e3/7ca82ee24c82d344584abd5b8637b3bd056f2900226e8d82fc22f1184b92/trove_classifiers-2026.6.1.19.tar.gz", hash = "sha256:c5132b4b61a829d11cfbd2d72e97f20a45ed6edb95e45c5efdeb5e00836b2745", size = 17059, upload-time = "2026-06-01T19:41:34.649Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7c/a4/81502f486f01db95bc8320646a8a12511f5e556cb63d5e224d91816605c4/trove_classifiers-2026.6.1.19-py3-none-any.whl", hash = "sha256:ab4c4ec93cc4a4e7815fa759906e05e6bb3f2fbd92ea0f897288c6a43efd15b3", size = 14211, upload-time = "2026-06-01T19:41:33.434Z" },
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.16.0"
@@ -4171,6 +4205,9 @@ dependencies = [
 ]
 
 [package.dev-dependencies]
+build = [
+    { name = "hatchling" },
+]
 dev = [
     { name = "httpx" },
     { name = "pytest" },
@@ -4186,6 +4223,7 @@ requires-dist = [
 ]
 
 [package.metadata.requires-dev]
+build = [{ name = "hatchling", specifier = "==1.27.0" }]
 dev = [
     { name = "httpx" },
     { name = "pytest", specifier = ">=8.3" },