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>
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
from fastapi import FastAPI, Depends
|
|
from contextlib import asynccontextmanager
|
|
from utils import pg_client
|
|
from core.model_handler import recommendation_model
|
|
from utils.declarations import FeedbackRecommendation
|
|
from apscheduler.schedulers.asyncio import AsyncIOScheduler
|
|
from crons.base_crons import cron_jobs
|
|
from auth.auth_key import api_key_auth
|
|
from core import feedback
|
|
from fastapi.middleware.cors import CORSMiddleware
|
|
|
|
|
|
@asynccontextmanager
|
|
async def lifespan(app: FastAPI):
|
|
await pg_client.init()
|
|
await feedback.init()
|
|
await recommendation_model.update()
|
|
app.schedule.start()
|
|
for job in cron_jobs:
|
|
app.schedule.add_job(id=job['func'].__name__, **job)
|
|
yield
|
|
app.schedule.shutdown(wait=False)
|
|
await feedback.terminate()
|
|
await pg_client.terminate()
|
|
|
|
app = FastAPI(lifespan=lifespan)
|
|
app.schedule = AsyncIOScheduler()
|
|
|
|
origins = [
|
|
"*"
|
|
]
|
|
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=origins,
|
|
allow_credentials=True,
|
|
allow_methods=["*"],
|
|
allow_headers=["*"],
|
|
)
|
|
|
|
# @app.on_event('startup')
|
|
# async def startup():
|
|
# await pg_client.init()
|
|
# await feedback.init()
|
|
# await recommendation_model.update()
|
|
# app.schedule.start()
|
|
# for job in cron_jobs:
|
|
# app.schedule.add_job(id=job['func'].__name__, **job)
|
|
#
|
|
#
|
|
# @app.on_event('shutdown')
|
|
# async def shutdown():
|
|
# app.schedule.shutdown(wait=False)
|
|
# await feedback.terminate()
|
|
# await pg_client.terminate()
|
|
|
|
|
|
@app.get('/recommendations/{user_id}/{project_id}', dependencies=[Depends(api_key_auth)])
|
|
async def get_recommended_sessions(user_id: int, project_id: int):
|
|
recommendations = recommendation_model.get_recommendations(user_id, project_id)
|
|
return {'userId': user_id,
|
|
'projectId': project_id,
|
|
'recommendations': recommendations
|
|
}
|
|
|
|
|
|
@app.get('/recommendations/{projectId}/{viewerId}/{sessionId}', dependencies=[Depends(api_key_auth)])
|
|
async def already_gave_feedback(projectId: int, viewerId: int, sessionId: int):
|
|
return feedback.has_feedback((viewerId, sessionId, projectId))
|
|
|
|
|
|
@app.post('/recommendations/feedback', dependencies=[Depends(api_key_auth)])
|
|
async def get_feedback(data: FeedbackRecommendation):
|
|
try:
|
|
feedback.global_queue.put(tuple(data.dict().values()))
|
|
except Exception as e:
|
|
return {'error': e}
|
|
return {'success': 1}
|
|
|
|
|
|
@app.get('/')
|
|
async def health():
|
|
return {'status': 200}
|