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File size: 3,340 Bytes
ee7d7b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | 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"])
@router.get("/{deploy_id}/metrics")
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))
@router.get("/{deploy_id}/drift")
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))
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