from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Dict, Any, Optional
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(
    title="VictoriaLogs ML Service",
    description="Machine Learning service for anomaly detection in logs",
    version="1.0.0"
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Request/Response models
class TrainRequest(BaseModel):
    model_name: str
    query: str
    start_time: str
    end_time: str
    contamination: float = 0.1

class DetectRequest(BaseModel):
    model_name: str
    logs: List[Dict[str, Any]]

class TrainResponse(BaseModel):
    status: str
    model_name: str
    model_path: str
    message: str

class DetectResponse(BaseModel):
    status: str
    anomalies: List[Dict[str, Any]]
    anomaly_count: int

@app.get("/")
async def root():
    return {
        "service": "VictoriaLogs ML Service",
        "status": "running",
        "version": "1.0.0"
    }

@app.get("/health")
async def health_check():
    return {"status": "healthy"}

@app.post("/api/ml/train", response_model=TrainResponse)
async def train_model(request: TrainRequest):
    """
    Train an anomaly detection model on historical logs
    """
    logger.info(f"Training model: {request.model_name}")

    # TODO: Implement model training
    # 1. Fetch logs from VictoriaLogs
    # 2. Extract features
    # 3. Train Isolation Forest model
    # 4. Save model to disk

    return TrainResponse(
        status="success",
        model_name=request.model_name,
        model_path=f"/app/models/{request.model_name}.pkl",
        message="Model training not yet implemented"
    )

@app.post("/api/ml/detect", response_model=DetectResponse)
async def detect_anomalies(request: DetectRequest):
    """
    Detect anomalies in a batch of logs using a trained model
    """
    logger.info(f"Detecting anomalies with model: {request.model_name}")

    # TODO: Implement anomaly detection
    # 1. Load trained model
    # 2. Extract features from logs
    # 3. Predict anomalies
    # 4. Return flagged logs with scores

    return DetectResponse(
        status="success",
        anomalies=[],
        anomaly_count=0
    )

@app.get("/api/ml/models")
async def list_models():
    """
    List all trained models
    """
    # TODO: List model files from disk
    return {
        "models": [],
        "count": 0
    }

@app.delete("/api/ml/models/{model_name}")
async def delete_model(model_name: str):
    """
    Delete a trained model
    """
    logger.info(f"Deleting model: {model_name}")

    # TODO: Delete model file
    return {
        "status": "success",
        "message": f"Model {model_name} deleted"
    }