app/main.py

"""FastAPI application entry point.

Configures the app instance, middleware, exception handlers, and startup hooks.
Launch with: uvicorn app.main:app
"""

import logging
import uuid
from contextlib import asynccontextmanager
from pathlib import Path

from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse

from app.api.v2.answer_evaluation import router as answer_evaluation_router
from app.api.v2.bloom import router as bloom_router
from app.api.v2.class_insights import router as class_insights_router
from app.api.v2.dependencies import ServiceContainer
from app.api.v2.health import router as health_router
from app.api.v2.knowledge_graph import router as knowledge_graph_router
from app.api.v2.learning_path import router as learning_path_router
from app.api.v2.lo_tagging import router as lo_tagging_router
from app.api.v2.mastery import router as mastery_router
from app.api.v2.monitoring import router as monitoring_router
from app.api.v2.recommendations import router as recommendations_router
from app.api.v2.risk import router as risk_router
from app.api.v2.student_profile import router as student_profile_router
from app.api.v2.teacher_feedback import router as teacher_feedback_router
from app.core.config import settings
from app.core.exceptions import DatasetError, EntityNotFoundError, ModelNotLoadedError
from app.data.loader import DatasetLoader
from app.models.registry import ModelRegistry
from app.monitoring.prediction_logger import PredictionLogger
from app.services.answer_evaluation_service import AnswerEvaluationService
from app.services.adaptive_learning_path_service import AdaptiveLearningPathService
from app.services.bloom_service import BloomService
from app.services.class_insights_service import ClassInsightsService
from app.services.explanation_service import ExplanationService
from app.services.knowledge_graph_service import KnowledgeGraphService
from app.services.lo_tagging_service import LOTaggingService
from app.services.mastery_service import MasteryService
from app.services.monitoring_service import MonitoringService
from app.services.recommendation_service import RecommendationService
from app.services.risk_service import RiskService
from app.services.student_profile_service import StudentProfileService
from app.services.teacher_feedback_service import TeacherFeedbackService

logger = logging.getLogger(__name__)

# Internal development origins — no wildcard "*" in production.
ALLOWED_ORIGINS = [
    "http://localhost:3000",
    "http://localhost:5173",
    "http://localhost:8000",
]


def _cache_dataset_metadata(app: FastAPI) -> None:
    """Load and cache dataset metadata for the data summary endpoint."""
    try:
        loader = DatasetLoader(settings.dataset_dir)
        metadata = loader.load_metadata()

        table_counts: dict[str, int] = metadata.get("table_counts", {})

        app.state.dataset_metadata = {
            "version": metadata.get("version", "unknown"),
            "table_counts": table_counts,
            "validation_status": "not_run",
            "total_issues": 0,
        }
        logger.info("Dataset metadata cached successfully — %d tables", len(table_counts))
    except DatasetError as exc:
        logger.warning("Failed to cache dataset metadata: %s", exc)
        app.state.dataset_metadata = {
            "version": "unknown",
            "table_counts": {},
            "validation_status": "not_run",
            "total_issues": 0,
        }


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan: startup and shutdown hooks."""
    # 1. Cache dataset metadata (existing)
    _cache_dataset_metadata(app)

    # 2. Initialize model registry and load all models
    registry = ModelRegistry(settings.model_artifact_dir)
    registry.load_all()

    # 3. Initialize shared components
    loader = DatasetLoader(settings.dataset_dir)
    explainer = ExplanationService()
    pred_logger = PredictionLogger(
        log_dir=Path(settings.metrics_dir),
        salt=settings.log_salt,
    )

    # 4. Initialize service container with DI
    knowledge_graph_service = KnowledgeGraphService(loader)
    app.state.services = ServiceContainer(
        registry=registry,
        loader=loader,
        explainer=explainer,
        pred_logger=pred_logger,
        lo_tagging=LOTaggingService(registry, loader, explainer, pred_logger),
        bloom=BloomService(registry, loader, explainer, pred_logger),
        mastery=MasteryService(registry, loader, explainer, pred_logger),
        risk=RiskService(registry, loader, explainer, pred_logger),
        recommendation=RecommendationService(registry, loader, explainer, pred_logger),
        answer_evaluation=AnswerEvaluationService(registry, loader, explainer, pred_logger),
        student_profile=StudentProfileService(loader),
        class_insights=ClassInsightsService(loader),
        teacher_feedback=TeacherFeedbackService(),
        monitoring=MonitoringService(registry, pred_logger),
        knowledge_graph=knowledge_graph_service,
        adaptive_learning_path=AdaptiveLearningPathService(loader, knowledge_graph_service),
    )

    logger.info("Service container initialized — model registry and shared components ready")

    yield


app = FastAPI(
    title=settings.ai_service_name,
    version=settings.ai_service_version,
    lifespan=lifespan,
)

# CORS middleware — internal origins only, no wildcard "*".
app.add_middleware(
    CORSMiddleware,
    allow_origins=ALLOWED_ORIGINS,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Include routers
app.include_router(health_router)
app.include_router(lo_tagging_router)
app.include_router(bloom_router)
app.include_router(mastery_router)
app.include_router(risk_router)
app.include_router(recommendations_router)
app.include_router(answer_evaluation_router)
app.include_router(student_profile_router)
app.include_router(class_insights_router)
app.include_router(teacher_feedback_router)
app.include_router(monitoring_router)
app.include_router(knowledge_graph_router)
app.include_router(learning_path_router)


# --- Exception Handlers ---


@app.exception_handler(DatasetError)
async def dataset_error_handler(request: Request, exc: DatasetError) -> JSONResponse:
    """Handle DatasetError — returns 500 with INTERNAL error code."""
    request_id = str(uuid.uuid4())
    logger.error("DatasetError [request_id=%s]: %s", request_id, exc)
    return JSONResponse(
        status_code=500,
        content={
            "error": {
                "code": "INTERNAL",
                "message": str(exc),
                "request_id": request_id,
            }
        },
    )


@app.exception_handler(ModelNotLoadedError)
async def model_not_loaded_handler(request: Request, exc: ModelNotLoadedError) -> JSONResponse:
    """Handle ModelNotLoadedError — returns 503 with MODEL_NOT_LOADED error code."""
    request_id = str(uuid.uuid4())
    logger.error("ModelNotLoadedError [request_id=%s]: %s", request_id, exc)
    return JSONResponse(
        status_code=503,
        content={
            "error": {
                "code": "MODEL_NOT_LOADED",
                "message": str(exc),
                "request_id": request_id,
            }
        },
    )


@app.exception_handler(EntityNotFoundError)
async def entity_not_found_handler(request: Request, exc: EntityNotFoundError) -> JSONResponse:
    """Handle EntityNotFoundError — returns 404 with NOT_FOUND error code."""
    request_id = str(uuid.uuid4())
    logger.warning("EntityNotFoundError [request_id=%s]: %s", request_id, exc)
    return JSONResponse(
        status_code=404,
        content={
            "error": {
                "code": "NOT_FOUND",
                "message": str(exc),
                "request_id": request_id,
            }
        },
    )