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models/model.py — Pydantic domain models (schema contract for API + internal).
Single source of truth for data shapes between all modules.
"""
from __future__ import annotations
import json
from datetime import datetime
from typing import Any
from pydantic import BaseModel, Field, field_validator, model_validator
# ── Enumerations ──────────────────────────────────────────────────────────────
ModelTask = str # detection|classification|segmentation|generation|embedding|nlp
ModelFramework = str # pytorch|onnx|tensorflow|tflite|coreml
ModelSource = str # hf|onnx|local
ModelStatus = str # available|downloading|cached|error
HardwareTarget = str # gpu|cpu|edge|tpu
# ── Sub-models ────────────────────────────────────────────────────────────────
class ModelMetrics(BaseModel):
latency_ms: float | None = None
mAP: float | None = None
accuracy: float | None = None
top1: float | None = None
vram_gb: float | None = None
fps: float | None = None
flops: float | None = None
class Config:
extra = "allow"
class ModelVersion(BaseModel):
version: str
label: str = "Stable" # Latest|Stable|Legacy|Nano|Small|Medium|Large|XLarge
description: str | None = None
releaseDate: str = ""
changelog: str | None = None
# ── Core Model ────────────────────────────────────────────────────────────────
class Model(BaseModel):
id: str
name: str
variant: str | None = None
task: ModelTask
framework: ModelFramework
size: int = 0 # bytes
size_label: str = "0 B"
tags: list[str] = Field(default_factory=list)
source: ModelSource = "hf"
provider: str = ""
description: str = ""
download_url: str | None = None # explicit download source (HF repo URL, ONNX direct URL, etc.)
local_path: str | None = None
project_id: str | None = None
downloaded: bool = False
status: ModelStatus = "available"
hardware: list[HardwareTarget] = Field(default_factory=list)
metrics: ModelMetrics = Field(default_factory=ModelMetrics)
versions: list[ModelVersion] = Field(default_factory=list)
active_version: str | None = None
rating: float | None = None
downloads: int | None = None
liked: bool = False
created_at: str | None = None
updated_at: str | None = None
class ModelSummary(BaseModel):
"""Lightweight projection returned in list endpoints."""
id: str
name: str
task: ModelTask
framework: ModelFramework
source: ModelSource
provider: str
size_label: str
status: ModelStatus
downloaded: bool
downloads: int | None = None
rating: float | None = None
tags: list[str]
hardware: list[HardwareTarget]
metrics: ModelMetrics
# ── DB Row → Model ────────────────────────────────────────────────────────────
def row_to_model(row: Any, versions: list[ModelVersion] | None = None) -> Model:
"""Convert an aiosqlite Row dict to a Model instance."""
d = dict(row)
metrics_raw = d.get("metrics") or "{}"
# metrics may come from model_versions join or not exist on models row
if isinstance(metrics_raw, str):
metrics_raw = json.loads(metrics_raw)
return Model(
id = d["id"],
name = d["name"],
variant = d.get("variant"),
task = d["task"],
framework = d["framework"],
source = d.get("source", "hf"),
provider = d.get("provider", ""),
description = d.get("description", ""),
download_url= d.get("download_url"),
size = d.get("size", 0),
size_label = d.get("size_label", "0 B"),
tags = json.loads(d.get("tags") or "[]"),
hardware = json.loads(d.get("hardware") or "[]"),
status = d.get("status", "available"),
downloaded = bool(d.get("downloaded", 0)),
local_path = d.get("local_path"),
project_id = d.get("project_id"),
downloads = d.get("downloads"),
rating = d.get("rating"),
liked = bool(d.get("liked", 0)),
metrics = ModelMetrics(**metrics_raw),
versions = versions or [],
active_version = d.get("active_version"),
created_at = d.get("created_at"),
updated_at = d.get("updated_at"),
)
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