faraphel
7bd84c8570
Models are now loaded in separate endpoints for the inputs to be easier to recognise
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import json
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import os
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import typing
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import warnings
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from pathlib import Path
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import fastapi
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from source import model, api
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class ModelManager:
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def __init__(self, application: api.Application, model_library: os.PathLike | str):
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self.application: api.Application = application
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self.model_library: Path = Path(model_library)
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# the model types
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self.model_types: dict[str, typing.Type[model.base.BaseModel]] = {}
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# the models
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self.models: dict[str, model.base.BaseModel] = {}
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# the currently loaded model
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# TODO(Faraphel): load more than one model at a time ? require a way more complex manager to handle memory issue
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self.current_loaded_model: typing.Optional[model.base.BaseModel] = None
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@self.application.get("/models")
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async def get_models() -> list[str]:
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"""
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Get the list of models available
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:return: the list of models available
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"""
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# list the models found
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return list(self.models.keys())
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@self.application.get("/models/{model_name}")
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async def get_model(model_name: str) -> dict:
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"""
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Get information about a specific model
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:param model_name: the name of the model
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:return: the information about the corresponding model
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"""
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# get the corresponding model
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model = self.models.get(model_name)
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if model is None:
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raise fastapi.HTTPException(status_code=404, detail="Model not found")
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# return the model information
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return model.get_information()
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def register_model_type(self, name: str, model_type: "typing.Type[model.base.BaseModel]"):
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self.model_types[name] = model_type
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def reload(self):
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# reset the model list
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for model in self.models.values():
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model.unload()
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self.models.clear()
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# load all the models in the library
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for model_path in self.model_library.iterdir():
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model_name: str = model_path.name
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model_configuration_path: Path = model_path / "config.json"
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# check if the configuration file exists
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if not model_configuration_path.exists():
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warnings.warn(f"Model {model_name!r} is missing a config.json file.")
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continue
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# load the configuration file
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model_configuration = json.loads(model_configuration_path.read_text())
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# get the model type for this model
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model_type_name: str = model_configuration.get("type")
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if model_type_name not in self.model_types:
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warnings.warn("Field 'type' missing from the model configuration file.")
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continue
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# get the class of this model type
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model_type = self.model_types.get(model_type_name)
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if model_type is None:
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warnings.warn(f"Model type {model_type_name!r} does not exists. Has it been registered ?")
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continue
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# load the model
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self.models[model_name] = model_type(self, model_configuration, model_path)
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