feat: support model memoization

Author: arthurhenrique

.github/workflows/ci.yaml

@@ -31,7 +31,7 @@ jobs:
       - name: Create Virtual Environment for UV
         run: |
           cd name-of-the-project
-          uv venv .venv  # Criando ambiente virtual
+          make install
           echo "VIRTUAL_ENV=$(pwd)/.venv" >> $GITHUB_ENV
           echo "$(pwd)/.venv/bin" >> $GITHUB_PATH
 
@@ -43,4 +43,4 @@ jobs:
       - name: Run Tests
         run: |
           cd name-of-the-project
-          make test
+          uv run pytest tests -vv --show-capture=all

setup.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-""" setup.py for cookiecutter-fastapi."""
+"""setup.py for cookiecutter-fastapi."""
 
 from setuptools import setup
 

{{cookiecutter.project_slug}}/.env.example

@@ -1,4 +1,5 @@
 SECRET_KEY=secret
 DEBUG=True
 MODEL_PATH={{cookiecutter.machine_learn_model_path}}
-MODEL_NAME={{cookiecutter.machine_learn_model_name}}
+MODEL_NAME={{cookiecutter.machine_learn_model_name}}
+MEMOIZATION_FLAG=True

{{cookiecutter.project_slug}}/Makefile

@@ -21,17 +21,18 @@ endif
 
 all: clean test install run deploy down
 
-test:
-	uv pip install -e ".[dev]"
+venv:
+	uv venv .venv
+
+test: venv
 	uv run pytest tests -vv --show-capture=all
 
-install: generate_dot_env
-	pip install --upgrade pip
-	pip install uv
+install: generate_dot_env venv
+	pip install uv --break-system-packages
 	uv pip install -e ".[dev]"
 
-run:
-	PYTHONPATH=app/ uvicorn main:app --reload --host 0.0.0.0 --port 8080
+run: venv
+	PYTHONPATH=app/ uv run uvicorn main:app --reload --host 0.0.0.0 --port 8080
 
 deploy: generate_dot_env
 	docker-compose build

{{cookiecutter.project_slug}}/app/api/__init__.py

@@ -1 +1 @@
-# mpmqtcc
+# mpmqtcc

{{cookiecutter.project_slug}}/app/api/routes/predictor.py

@@ -1,42 +1,47 @@
 import json
 
 import joblib
-from fastapi import APIRouter, HTTPException
-
 from core.config import INPUT_EXAMPLE
-from services.predict import MachineLearningModelHandlerScore as model
+from fastapi import APIRouter, HTTPException
 from models.prediction import (
     HealthResponse,
-    MachineLearningResponse,
     MachineLearningDataInput,
+    MachineLearningResponse,
 )
+from services.predict import MachineLearningModelHandlerScore as model
 
 router = APIRouter()
 
 
-## Change this portion for other types of models
-## Add the correct type hinting when completed
 def get_prediction(data_point):
     return model.predict(data_point, load_wrapper=joblib.load, method="predict")
 
 
+def get_prediction_label(prediction):
+    if prediction == 1:
+        return "label ok"
+    return "label nok"
+
+
 @router.post(
     "/predict",
     response_model=MachineLearningResponse,
     name="predict:get-data",
 )
 async def predict(data_input: MachineLearningDataInput):
-
     if not data_input:
         raise HTTPException(status_code=404, detail="'data_input' argument invalid!")
     try:
         data_point = data_input.get_np_array()
         prediction = get_prediction(data_point)
+        prediction_label = get_prediction_label(prediction)
 
     except Exception as err:
         raise HTTPException(status_code=500, detail=f"Exception: {err}")
 
-    return MachineLearningResponse(prediction=prediction)
+    return MachineLearningResponse(
+        prediction=prediction, prediction_label=prediction_label
+    )
 
 
 @router.get(

{{cookiecutter.project_slug}}/app/core/config.py

@@ -1,22 +1,22 @@
-import sys
 import logging
+import sys
 
+from core.logging import InterceptHandler
 from loguru import logger
 from starlette.config import Config
 from starlette.datastructures import Secret
 
-from core.logging import InterceptHandler
-
 config = Config(".env")
 
 API_PREFIX = "/api"
-VERSION = "{{cookiecutter.version}}"
+VERSION = "0.1.0"
 DEBUG: bool = config("DEBUG", cast=bool, default=False)
 MAX_CONNECTIONS_COUNT: int = config("MAX_CONNECTIONS_COUNT", cast=int, default=10)
 MIN_CONNECTIONS_COUNT: int = config("MIN_CONNECTIONS_COUNT", cast=int, default=10)
 SECRET_KEY: Secret = config("SECRET_KEY", cast=Secret, default="")
+MEMOIZATION_FLAG: bool = config("MEMOIZATION_FLAG", cast=bool, default=True)
 
-PROJECT_NAME: str = config("PROJECT_NAME", default="{{cookiecutter.project_name}}")
+PROJECT_NAME: str = config("PROJECT_NAME", default="manu")
 
 # logging configuration
 LOGGING_LEVEL = logging.DEBUG if DEBUG else logging.INFO
@@ -25,6 +25,8 @@
 )
 logger.configure(handlers=[{"sink": sys.stderr, "level": LOGGING_LEVEL}])
 
-MODEL_PATH = config("MODEL_PATH", default="{{cookiecutter.machine_learn_model_path}}")
-MODEL_NAME = config("MODEL_NAME", default="{{cookiecutter.machine_learn_model_name}}")
-INPUT_EXAMPLE = config("INPUT_EXAMPLE", default="{{cookiecutter.input_example_path}}")
+MODEL_PATH = config(
+    "MODEL_PATH", default="/Users/arthur.dasilva/repos/arthurhenrique/n"
+)
+MODEL_NAME = config("MODEL_NAME", default="pregnancy_model_local.joblib")
+INPUT_EXAMPLE = config("INPUT_EXAMPLE", default="./ml/model/examples/example.json")

{{cookiecutter.project_slug}}/app/core/errors.py

@@ -1,6 +1,4 @@
-class PredictException(BaseException):
-    ...
+class PredictException(BaseException): ...
 
 
-class ModelLoadException(BaseException):
-    ...
+class ModelLoadException(BaseException): ...

{{cookiecutter.project_slug}}/app/core/events.py

@@ -1,5 +1,6 @@
 from typing import Callable
 
+import joblib
 from fastapi import FastAPI
 
 
@@ -9,7 +10,7 @@ def preload_model():
     """
     from services.predict import MachineLearningModelHandlerScore
 
-    MachineLearningModelHandlerScore.get_model()
+    MachineLearningModelHandlerScore.get_model(joblib.load)
 
 
 def create_start_app_handler(app: FastAPI) -> Callable:

{{cookiecutter.project_slug}}/app/main.py

@@ -1,15 +1,13 @@
-from fastapi import FastAPI
-
 from api.routes.api import router as api_router
+from core.config import API_PREFIX, DEBUG, MEMOIZATION_FLAG, PROJECT_NAME, VERSION
 from core.events import create_start_app_handler
-from core.config import API_PREFIX, DEBUG, PROJECT_NAME, VERSION
+from fastapi import FastAPI
 
 
 def get_application() -> FastAPI:
     application = FastAPI(title=PROJECT_NAME, debug=DEBUG, version=VERSION)
     application.include_router(api_router, prefix=API_PREFIX)
-    pre_load = False
-    if pre_load:
+    if MEMOIZATION_FLAG:
         application.add_event_handler("startup", create_start_app_handler(application))
     return application