fix: implement real health checks in ML service /health endpoint

- Execute SELECT 1 against PostgreSQL via SQLAlchemy session to verify DB connectivity
- HTTP GET to ${MLFLOW_TRACKING_URI}/health (default: http://localhost:5000/health) to verify MLflow
- Return HTTP 503 if any component is unhealthy, HTTP 200 only when both are healthy
- Values: "healthy" / "unhealthy" for database and mlflow fields
- Add `import requests as http_requests` and `sa_text` imports

Closes task 5.6 in openspec/changes/code-review-fix/tasks.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

openspec/changes/code-review-fix/tasks.md

@@ -46,7 +46,7 @@
 - [x] 5.3 `[sonnet]` Add `_model_swap_lock` to prediction reads (not just writes) in `services/ml/app/main.py` for thread-safe model access
 - [x] 5.4 `[sonnet]` Add date range validation (max 1 year) to `POST /predict/batch` in `services/ml/app/main.py`
 - [x] 5.5 `[sonnet]` Add candle time-sort validation/auto-sort to `POST /predict` in `services/ml/app/main.py`
-- [ ] 5.6 `[sonnet]` Implement real health checks: `SELECT 1` for PostgreSQL, MLflow API ping in `services/ml/app/main.py:396-409`
+- [x] 5.6 `[sonnet]` Implement real health checks: `SELECT 1` for PostgreSQL, MLflow API ping in `services/ml/app/main.py:396-409`
 - [ ] 5.7 `[sonnet]` Add training resource limits: 500MB dataset size check, 30-minute timeout with status update on expiry in `services/ml/app/main.py:907-1030`
 - [ ] 5.8 `[haiku]` Add `run_id` format validation to `DELETE /training/runs/{run_id}` and `GET /training/runs/{run_id}` endpoints
 

services/ml/app/main.py

@@ -15,7 +15,9 @@ from typing import Optional, Dict, Any, List
 from datetime import datetime
 import json
 
-from fastapi import FastAPI, HTTPException, Header, Depends, Security, status
+import requests as http_requests
+
+from fastapi import FastAPI, HTTPException, Header, Depends, Security, status, Response
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 import numpy as np
@@ -24,7 +26,7 @@ import joblib
 import mlflow
 import mlflow.sklearn
 import mlflow.xgboost
-from sqlalchemy import update as sa_update, desc
+from sqlalchemy import update as sa_update, desc, text as sa_text
 
 from app.config import load_config, PipelineConfig, get_default_config
 from app.db import get_db, TrainingRun, init_db
@@ -183,8 +185,8 @@ class HealthResponse(BaseModel):
     """Health check response."""
     status: str = Field(..., description="healthy, degraded, or unhealthy")
     model_loaded: bool
-    mlflow: str = Field(..., description="connected or disconnected")
-    database: str = Field(..., description="connected or disconnected")
+    mlflow: str = Field(..., description="healthy or unhealthy")
+    database: str = Field(..., description="healthy or unhealthy")
 
 
 # --- Model Loading Functions ---
@@ -473,43 +475,56 @@ async def startup_event():
 # --- Health Check ---
 
 @app.get("/health", response_model=HealthResponse)
-async def health_check():
+async def health_check(response: Response):
     """
     Health check endpoint.
-    
+
     Returns service status, model loaded status, and dependency health.
+    Returns HTTP 503 if any component (database or MLflow) is unhealthy.
     """
     model_loaded = state.model is not None
-    
-    # Check MLflow connection
-    mlflow_status = "disconnected"
+
+    # Check database connection with a real SELECT 1
+    db_status = "unhealthy"
     try:
-        # TODO: Actually check MLflow connection
-        mlflow_status = "connected"
-    except Exception:
-        pass
-    
-    # Check database connection
-    db_status = "disconnected"
+        with get_db() as db:
+            db.execute(sa_text("SELECT 1"))
+        db_status = "healthy"
+    except Exception as exc:
+        logger.warning(f"Health check: database unhealthy: {exc}")
+
+    # Check MLflow connection via HTTP GET to its health endpoint
+    mlflow_status = "unhealthy"
     try:
-        # TODO: Actually check database connection
-        db_status = "connected"
-    except Exception:
-        pass
-    
+        mlflow_tracking_uri = os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000")
+        mlflow_health_url = f"{mlflow_tracking_uri.rstrip('/')}/health"
+        resp = http_requests.get(mlflow_health_url, timeout=3)
+        if resp.status_code == 200:
+            mlflow_status = "healthy"
+        else:
+            logger.warning(
+                f"Health check: MLflow returned HTTP {resp.status_code}"
+            )
+    except Exception as exc:
+        logger.warning(f"Health check: MLflow unreachable: {exc}")
+
     # Determine overall status
-    if model_loaded and mlflow_status == "connected" and db_status == "connected":
+    if db_status == "healthy" and mlflow_status == "healthy":
         overall_status = "healthy"
     elif model_loaded:
         overall_status = "degraded"
     else:
         overall_status = "unhealthy"
-    
+
+    # Return HTTP 503 if any dependency is unhealthy
+    if db_status != "healthy" or mlflow_status != "healthy":
+        response.status_code = status.HTTP_503_SERVICE_UNAVAILABLE
+
     return HealthResponse(
         status=overall_status,
         model_loaded=model_loaded,
         mlflow=mlflow_status,
-        database=db_status
+        database=db_status,
     )