0ce879aa44
- Implemented HTML pages for datasets, models, training, testing, and results. - Created API endpoints for managing repositories, results, tests, and training sessions. - Added functionality for streaming training progress via Server-Sent Events (SSE). - Introduced a Dockerfile for the ML runner with necessary dependencies. - Developed an SDK for user code execution within the runner container. - Enhanced CSS styles for improved UI layout and navigation. - Established a layout template for consistent HTML structure across pages. - Added JavaScript for dynamic interactions on the models page. - Implemented WebSocket handling for real-time communication with kiosk devices and controllers. - Implemented model registration and management API at /api/models - Added Gitea proxy API for repository interactions at /api/repos - Created results API for listing and comparing training results at /api/results - Developed training management API for enqueueing and retrieving training jobs at /api/trainings - Introduced SSE endpoint for live training progress updates - Added HTML pages for models, datasets, and training management - Created a Dockerfile for the ML runner with necessary dependencies - Developed SDK for user code execution within the runner container - Enhanced CSS styles for improved UI/UX - Implemented WebSocket communication for real-time device and controller interactions in the kiosk system
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
from pydantic import BaseModel, Field
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from typing import Dict, List, Optional, Any
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from datetime import datetime
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# ── Catalog ──────────────────────────────────────────────────────────────────
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class DatasetVariable(BaseModel):
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short_name: str
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standard_name: Optional[str] = None
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units: Optional[str] = None
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description: Optional[str] = None # human-readable label derived from standard_name
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class CatalogDataset(BaseModel):
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dataset_id: str
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title: Optional[str] = None
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description: Optional[str] = None
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variables: List[DatasetVariable] = []
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min_longitude: Optional[float] = None
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max_longitude: Optional[float] = None
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min_latitude: Optional[float] = None
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max_latitude: Optional[float] = None
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start_datetime: Optional[str] = None
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end_datetime: Optional[str] = None
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# ── Jobs ─────────────────────────────────────────────────────────────────────
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class DownloadJobRequest(BaseModel):
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dataset_id: str
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variables: List[str] = Field(..., min_length=1)
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min_longitude: float
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max_longitude: float
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min_latitude: float
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max_latitude: float
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start_date: str # YYYY-MM-DD
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end_date: str # YYYY-MM-DD
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format: str = Field("json", pattern="^(json|csv)$")
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nome: str = Field(..., min_length=1)
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tags: List[str] = Field(default_factory=lambda: ["marine"])
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notes: str = ""
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variable_renames: Dict[str, str] = Field(default_factory=dict) # {original: custom}
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class JobStatus(BaseModel):
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job_id: str
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status: str # pending | downloading | converting | saving | done | error
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progress: int = 0 # 0-100
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message: str = ""
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dataset_id: Optional[str] = None # filled on done
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# ── Saved Datasets ────────────────────────────────────────────────────────────
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class DatasetMeta(BaseModel):
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id: str
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nome: str
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tags: List[str] = []
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created_date: str
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created_by: str
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used_last_date: Optional[str] = None
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type: str # json | csv
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size: int
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notes: str = ""
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version: int = 1
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filename: str
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copernicus_id: str
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variables: List[str] = []
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bbox: List[float] = [] # [min_lon, min_lat, max_lon, max_lat]
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start_date: str
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end_date: str
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class DatasetListResponse(BaseModel):
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datasets: List[DatasetMeta]
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count: int
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