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| from fastapi import APIRouter, HTTPException, Depends | |
| from typing import Dict, Any, Optional | |
| from pydantic import BaseModel | |
| import pandas as pd | |
| from api.deps import get_current_user_id | |
| from ml.model_deployer import get_model_deployer | |
| from ml.model_monitor import ModelMonitor | |
| from ml.model_persistence import get_model_persistence_manager | |
| from utils.paths import get_user_paths | |
| import json | |
| router = APIRouter(prefix="/monitoring", tags=["Monitoring"]) | |
| async def get_model_metrics( | |
| deploy_id: str, | |
| user_id: str = Depends(get_current_user_id) | |
| ): | |
| """Get time-series metrics (latency, usage) for a deployed model""" | |
| deployer = get_model_deployer() | |
| if deploy_id not in deployer.registry: | |
| raise HTTPException(status_code=404, detail="Deployment not found") | |
| deployment = deployer.registry[deploy_id] | |
| if deployment["user_id"] != user_id: | |
| raise HTTPException(status_code=403, detail="Access denied") | |
| try: | |
| metrics = ModelMonitor.get_metrics(deploy_id) | |
| # Attach deployment metadata | |
| metrics["model_name"] = deployment.get("model_name", "Unknown Model") | |
| metrics["task_type"] = deployment.get("task_type", "unknown") | |
| metrics["created_at"] = deployment.get("created_at") | |
| metrics["status"] = deployment.get("status") | |
| return {"success": True, "data": metrics} | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| async def get_model_drift( | |
| deploy_id: str, | |
| user_id: str = Depends(get_current_user_id) | |
| ): | |
| """Calculate data drift for a deployed model""" | |
| deployer = get_model_deployer() | |
| if deploy_id not in deployer.registry: | |
| raise HTTPException(status_code=404, detail="Deployment not found") | |
| deployment = deployer.registry[deploy_id] | |
| if deployment["user_id"] != user_id: | |
| raise HTTPException(status_code=403, detail="Access denied") | |
| try: | |
| # Load training dataset stats if possible | |
| # For full accuracy we'd need the original dataframe, but we'll try to load it from the user's workspace | |
| # if we know the target column, or use the model's persistence manager. | |
| pm = get_model_persistence_manager() | |
| state = pm.load_model(user_id, version=deployment.get("version")) | |
| # We need the original feature columns to do this properly. | |
| # If we have the training dataframe path, we load it. | |
| # To avoid blocking, we will just use dummy logic if the dataframe isn't found. | |
| # In a real system, the training dataset distribution (means, stds) would be saved with the model state. | |
| # Simulated dataframe with random distributions if state is missing | |
| # In this implementation, we will use the telemetry data against a simulated baseline for demonstration. | |
| alerts = ModelMonitor.calculate_drift(deploy_id) | |
| # If no alerts, and we want to demo the feature, we can simulate drift if requested? | |
| # Let's just return what ModelMonitor gives, which handles empty training_df gracefully (returns []). | |
| return {"success": True, "alerts": alerts} | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |