test / app.py
christopher
async
95f7578
raw
history blame
6.18 kB
import logging
from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import Dict, Optional, List, Any
import uuid
from datetime import datetime
from contextlib import asynccontextmanager
import asyncio
from models.embedding import EmbeddingModel
from models.summarization import SummarizationModel
from models.nlp import NLPModel
from database.query import DatabaseService
from database.query_processor import QueryProcessor
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler()]
)
# Initialize models
embedding_model = None
summarization_model = None
nlp_model = None
db_service = None
@asynccontextmanager
async def lifespan(app: FastAPI):
global embedding_model, summarization_model, nlp_model, db_service
# Model initialization
logging.info("Initializing models...")
try:
embedding_model = EmbeddingModel()
summarization_model = SummarizationModel()
nlp_model = NLPModel()
db_service = DatabaseService()
logging.info("All models initialized successfully")
except Exception as e:
logging.error(f"Model initialization failed: {str(e)}")
raise
yield
# Cleanup
logging.info("Shutting down application...")
if db_service:
try:
await db_service.close()
logging.info("Database connection closed successfully")
except Exception as e:
logging.error(f"Error closing database connection: {str(e)}")
app = FastAPI(
title="Kairos News API",
version="1.0",
lifespan=lifespan
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# In-memory job storage
jobs_db: Dict[str, Dict] = {}
class PostRequest(BaseModel):
query: str
topic: Optional[str] = None
start_date: Optional[str] = None
end_date: Optional[str] = None
class JobStatus(BaseModel):
id: str
status: str
created_at: datetime
completed_at: Optional[datetime] = None
request: PostRequest
result: Optional[Dict[str, Any]] = None
@app.get("/")
async def root(request: Request):
"""Root endpoint with API information"""
logging.info(f"Root access from {request.client.host}")
return {
"message": "Kairos News API is running",
"endpoints": {
"create_job": {"method": "POST", "path": "/index"},
"check_status": {"method": "GET", "path": "/loading"},
"get_logs": {"method": "GET", "path": "/logs"}
}
}
@app.get("/logs")
async def get_logs(log_type: str = None):
"""Endpoint for log retrieval"""
if log_type == "container":
return {"message": "Container logs endpoint", "status": "Not implemented"}
return {
"message": "Available log types: container",
"usage": "/logs?log_type=container"
}
@app.exception_handler(404)
async def not_found_exception_handler(request: Request, exc: HTTPException):
"""Custom 404 handler"""
logging.warning(f"404 Not Found: {request.url}")
return JSONResponse(
status_code=404,
content={
"message": "Endpoint not found",
"available_endpoints": [
{"path": "/", "method": "GET"},
{"path": "/index", "method": "POST"},
{"path": "/loading", "method": "GET"},
{"path": "/logs", "method": "GET"}
]
},
)
@app.post("/index", response_model=JobStatus)
async def create_job(request: PostRequest, background_tasks: BackgroundTasks):
job_id = str(uuid.uuid4())
logging.info(f"Creating new job {job_id} with request: {request.dict()}")
print(asyncio.get_running_loop())
jobs_db[job_id] = {
"id": job_id,
"status": "processing",
"created_at": datetime.now(),
"completed_at": None,
"request": request.dict(),
"result": None
}
background_tasks.add_task(
process_job,
job_id,
request,
embedding_model,
summarization_model,
nlp_model,
db_service
)
logging.info(f"Job {job_id} created and processing started")
return jobs_db[job_id]
@app.get("/loading", response_model=JobStatus)
async def get_job_status(id: str):
logging.info(f"Checking status for job {id}")
if id not in jobs_db:
logging.warning(f"Job {id} not found")
raise HTTPException(status_code=404, detail="Job not found")
logging.info(f"Returning status for job {id}: {jobs_db[id]['status']}")
return jobs_db[id]
async def process_job(
job_id: str,
request: PostRequest,
embedding_model: EmbeddingModel,
summarization_model: SummarizationModel,
nlp_model: NLPModel,
db_service: DatabaseService
):
try:
logging.info(f"Starting processing for job {job_id}")
processor = QueryProcessor(
embedding_model=embedding_model,
summarization_model=summarization_model,
nlp_model=nlp_model,
db_service=db_service
)
logging.debug(f"Processing query: {request.query}")
result = await processor.process(
query=request.query,
topic=request.topic,
start_date=request.start_date,
end_date=request.end_date
)
jobs_db[job_id].update({
"status": "completed",
"completed_at": datetime.now(),
"result": result if result else {"message": "No results found"}
})
logging.info(f"Job {job_id} completed successfully")
except Exception as e:
logging.error(f"Error processing job {job_id}: {str(e)}", exc_info=True)
jobs_db[job_id].update({
"status": "failed",
"completed_at": datetime.now(),
"result": {"error": str(e)}
})
logging.info(f"Job {job_id} marked as failed")