added support of inputs parameters that are recognised by the API.

Models are now loaded in separate endpoints for the inputs to be easier to recognise

source/api/__init__.py

@@ -1,3 +1 @@
-from . import route
-
 from .Application import Application

source/api/route/__init__.py

@@ -1 +0,0 @@
-from . import models

source/api/route/models.py

@@ -1,74 +0,0 @@
-import sys
-import traceback
-
-import fastapi
-import pydantic
-
-from source.api import Application
-from source import manager
-
-
-class InferenceRequest(pydantic.BaseModel):
-    """
-    Represent a request made when inferring a model
-    """
-
-    request: dict
-
-
-def load(application: Application, model_manager: manager.ModelManager):
-    @application.get("/models")
-    async def get_models() -> list[str]:
-        """
-        Get the list of models available
-        :return: the list of models available
-        """
-
-        # reload the model list
-        model_manager.reload()
-        # list the models found
-        return list(model_manager.models.keys())
-
-    @application.get("/models/{model_name}")
-    async def get_model(model_name: str) -> dict:
-        """
-        Get information about a specific model
-        :param model_name: the name of the model
-        :return: the information about the corresponding model
-        """
-
-        # get the corresponding model
-        model = model_manager.models.get(model_name)
-        if model is None:
-            raise fastapi.HTTPException(status_code=404, detail="Model not found")
-
-        # return the model information
-        return model.get_information()
-
-
-    @application.post("/models/{model_name}/infer")
-    async def infer_model(model_name: str, request: InferenceRequest) -> fastapi.Response:
-        """
-        Run an inference through the selected model
-        :param model_name: the name of the model
-        :param request: the data to infer to the model
-        :return: the model response
-        """
-
-        # get the corresponding model
-        model = model_manager.models.get(model_name)
-        if model is None:
-            raise fastapi.HTTPException(status_code=404, detail="Model not found")
-
-        # infer the data through the model
-        try:
-            response = model.infer(request.request)
-        except Exception:
-            print(traceback.format_exc(), file=sys.stderr)
-            raise fastapi.HTTPException(status_code=500, detail="An error occurred while inferring the model.")
-
-        # pack the model response into a fastapi response
-        return fastapi.Response(
-            content=response,
-            media_type=model.response_mimetype,
-        )