best_engine_ai_helper.api module

api — FastAPI HTTP surface for best-engine-ai-helper.

Exposes the two calls the GUI needs:

GET /api/system — detected hardware + compute profile + memory budget.
POST /api/recommend — a free-text task -> the same report recommend() produces for the CLI’s report command, as JSON.

A minimal single-page GUI is served at GET /gui (GET / redirects there): it shows the machine’s characteristics and lets you type a task description to get the best local engine(s) for it. It is bilingual — French by default, English at GET /gui?lang=en — with a header link to switch between the two.

Install the extra to get the runtime dependencies:

pip install 'best-engine-ai-helper[api]'

Then run the app with any ASGI server:

uvicorn best_engine_ai_helper.api:app --port 8000
# or: best-engine-ai-helper gui

Author

Warith Harchaoui <warith.harchaoui@deraison.ai>

class best_engine_ai_helper.api.RecommendRequest(*, task=None, headroom=0.85)[source]

Bases: BaseModel

Body for POST /api/recommend.

Parameters:

task (str | None)
headroom (float)

headroom: float

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

task: str | None

best_engine_ai_helper.api.gui(lang='fr')

Parameters:: lang (str)
Return type:: str

best_engine_ai_helper.api.recommend(body)

Best local engine(s) for body.task on this machine’s hardware.

Parameters:: body (RecommendRequest)
Return type:: dict[str, Any]

best_engine_ai_helper.api.root()

Return type:: fastapi.responses.RedirectResponse

best_engine_ai_helper.api.system()

Detected hardware, compute profile, and usable memory budget.

Return type:: dict[str, Any]