cea5eda985
* Created two services: recommendation training and recommendation serving * Deleted Docker temporary * Added features based in signals information * Added method to get sessions features using PG * Added same utils and core elements into ml_trainer * Added checks before training models, added handler for model serving * Updated serving API and recommendation functions to use frontend signals features * reorganized modules to have base image and for both serving and training * Added Dockerfiles and base Dockerfile * Solved issue while ordering sessions by relevance * Added method to save user feedback of recommendations * Added security authorization * Updated Dockerfile * fixed issues with secret insertion to API * Updated feedback structure * Added git for dags * Solved issue of insertion on recommendation feedback * Changed update method from def to async def and it is called during startup * Solved issues of airflow running mlflow in dag * Changes sanity checks and added middleware params * base path renaming * Changed update method to a interval method which loads one model each 10s if there are models to download * Added sql files for recommendation service and trainer * Cleaned files and added documentation for methods and classes * Added README file * Renamed endpoints, changed None into empty array and updated readme * refactor(recommendation): optimized query * style(recommendation): changed import to top file, renamed endpoints parameters, function optimization * refactor(recommendation): .gitignore * refactor(recommendation): .gitignore * refactor(recommendation): Optimized Dockerfiles * refactor(recommendation): changed imports * refactor(recommendation): optimized requests * refactor(recommendation): optimized requests * Fixed boot for fastapi, updated some queries * Fixed issues while downloading models and while returning json response from API * limited number of recommendations and set a minimum score to present recommendations * fix(recommendation): fixed some queries and updated prediction method * Added env value to control number of predictions to make * docs(recommendation): Added third party libraries used in recommendation service * frozen requirements * Update base_crons.py added `misfire_grace_time` to recommendation crons --------- Co-authored-by: Taha Yassine Kraiem <tahayk2@gmail.com>
33 lines
No EOL
847 B
Python
33 lines
No EOL
847 B
Python
from fastapi.security import OAuth2PasswordBearer
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from fastapi import HTTPException, Depends, status
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from decouple import config
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oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
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class AuthHandler:
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def __init__(self):
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"""
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Authorization method using an API key.
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"""
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self.__api_keys = [config("API_AUTH_KEY")]
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def __contains__(self, api_key):
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return api_key in self.__api_keys
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def add_key(self, key):
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"""Adds new key for authentication."""
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self.__api_keys.append(key)
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auth_method = AuthHandler()
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def api_key_auth(api_key: str = Depends(oauth2_scheme)):
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"""Method to verify auth."""
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global auth_method
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if api_key not in auth_method:
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Forbidden"
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) |