"""
Federated Learning API endpoints for handling model updates and client interactions.
"""

from typing import Dict, List
from fastapi import FastAPI, Request
from pydantic import BaseModel
from slowapi import Limiter
from slowapi.util import get_remote_address

app = FastAPI()
limiter = Limiter(key_func=get_remote_address)

class ModelWeights(BaseModel):
    """Model weights data structure for federated learning updates."""
    weights: List[float]
    client_id: str

class FederatedModelUpdate(BaseModel):
    """Structure for federated learning model updates from clients."""
    model_weights: ModelWeights
    round_number: int
    metrics: Dict[str, float]

@limiter.limit("10/minute")
async def get_global_model(request: Request):  # pylint: disable=unused-argument
    """
    Retrieve the current global model weights.
    
    Args:
        request: FastAPI request object for rate limiting
        
    Returns:
        dict: Current global model weights
    """
    return {"weights": [0.1, 0.2, 0.3]}  # Example weights

@app.post("/update")
async def process_update(request: Request):  # pylint: disable=unused-argument
    """
    Process a model update from a client.
    
    Args:
        request: FastAPI request object
    
    Returns:
        dict: Status of the update processing
    """
    return {"status": "success"}

@app.get("/get_model")
@limiter.limit("10/minute")
async def serve_model(request: Request):
    """
    Serve the current model to clients.
    
    Args:
        request: FastAPI request object
    
    Returns:
        dict: Current model state
    """
    return await get_global_model(request)

def process_client_update(update: FederatedModelUpdate) -> dict:
    """
    Process a client's model update.
    
    Args:
        update: Client's model update data
    
    Returns:
        dict: Processing result
    """
    return {"status": "processed"}

@app.get("/health")
async def health_check():
    """
    API health check endpoint.
    
    Returns:
        dict: Health status
    """
    return {"status": "healthy"}

@app.get("/metrics")
async def get_metrics():
    """
    Get training metrics.
    
    Returns:
        dict: Current metrics
    """
    return {"accuracy": 0.95, "loss": 0.05}

def create_federated_router() -> FastAPI:
    """Create and return the federated learning router"""
    router = FastAPI()
    router.add_api_route("/update", process_update, methods=["POST"])
    router.add_api_route("/get_model", serve_model, methods=["GET"])
    router.add_api_route("/health", health_check, methods=["GET"])
    router.add_api_route("/metrics", get_metrics, methods=["GET"])
    return router
