async def configure_model(self, request):
model_name = request.match_info["model"]
label = request.match_info["label"]
config = await request.json()
try:
model = Model.load_labeled(f"{label}={model_name}")
except EntrypointNotFound as error:
self.logger.error(
f"/configure/model/ failed to load model: {error}")
return web.json_response(
{"error": f"model {model_name} not found"},
status=HTTPStatus.NOT_FOUND,
)
try:
model = model.withconfig(config)
except MissingConfig as error:
self.logger.error(
f"failed to configure model {model_name}: {error}")
return web.json_response({"error": str(error)},
status=HTTPStatus.BAD_REQUEST)
# DFFML objects all follow a double context entry pattern
exit_stack = request.app["exit_stack"]
model = await exit_stack.enter_async_context(model)
request.app["models"][label] = model
return web.json_response(OK)
async def list_models(self, request):
return web.json_response(
{
model.ENTRY_POINT_ORIG_LABEL: model.args({})
for model in Model.load()
},
dumps=partial(json.dumps, cls=JSONEncoder),
)
