refactor: MCP 공통 함수/httpx 싱글톤, CORS 수정, ML 이벤트루프 블로킹 해소 (#4~#7)

backend/app/services/tensorflow_predictor.py

@@ -1,3 +1,5 @@
+import asyncio
+import functools
 import logging
 from typing import List, Dict, Any, Optional
 
@@ -19,6 +21,9 @@ class TensorFlowPredictor:
     - If TensorFlow or model/scaler files are missing, raises TensorFlowNotAvailable
       so callers can handle gracefully (e.g., return 503 or a friendly message).
     - Fetches recent daily closes from KIS API.
+
+    이슈 7: model.predict()는 CPU-bound 동기 호출이므로 predict_stock() 내에서
+    asyncio.to_thread()로 워커 스레드에 위임해 이벤트 루프 블로킹을 방지한다.
     """
 
     def __init__(self, model_path: str = "models/lstm_model.h5", scaler_path: str = "models/scaler.pkl", time_steps: int = 60):
@@ -27,7 +32,6 @@ class TensorFlowPredictor:
         self.time_steps = time_steps
 
         try:
-            # Local import to avoid hard dependency when not installed
             import tensorflow as tf  # noqa: F401
             from tensorflow import keras  # noqa: F401
             import joblib  # noqa: F401
@@ -35,7 +39,6 @@ class TensorFlowPredictor:
             logger.warning(f"TensorFlow or joblib not available: {e}")
             raise TensorFlowNotAvailable("TensorFlow/joblib not installed in backend environment")
 
-        # Load resources
         try:
             from tensorflow import keras
             import joblib
@@ -47,20 +50,16 @@ class TensorFlowPredictor:
             raise TensorFlowNotAvailable(f"Model or scaler not found: {e}")
 
     async def fetch_kis_closes(self, stock_code: str, days: int = 60) -> np.ndarray:
-        """Fetch recent daily closes from KIS. Returns array oldest→latest.
-        Uses korea_investment_api.get_daily_chart(stock_code, "D").
-        """
+        """Fetch recent daily closes from KIS. Returns array oldest→latest."""
         try:
             resp = await korea_investment_api.get_daily_chart(stock_code, "D")
             output = resp.get("output") or []
             if not output:
                 raise RuntimeError("empty_chart")
-            # KIS usually returns newest first; reverse to chronological
             closes_desc = [float(item.get("stck_clpr", 0) or 0) for item in output]
             closes = list(reversed(closes_desc))
             arr = np.array(closes[-days:], dtype=float)
             if arr.size < days:
-                # If fewer than requested, still return what's available
                 logger.info(f"{stock_code}: only {arr.size} closes available (requested {days})")
             return arr
         except Exception as e:
@@ -68,26 +67,34 @@ class TensorFlowPredictor:
             raise
 
     async def predict_stock(self, stock_code: str) -> Dict[str, Any]:
-        """Return prediction dict with expected_return and simple signal."""
+        """Return prediction dict with expected_return and simple signal.
+
+        model.predict()를 asyncio.to_thread()로 워커 스레드에서 실행해
+        Keras/TF의 GIL-holding 연산이 이벤트 루프를 막지 않게 한다.
+        """
         try:
             prices = await self.fetch_kis_closes(stock_code, days=self.time_steps)
             if prices.size == 0:
                 raise RuntimeError("no_price_data")
 
-            # Normalize and shape for LSTM (1, time_steps, 1)
             scaled = self.scaler.transform(prices.reshape(-1, 1))
             if scaled.shape[0] < self.time_steps:
-                # Pad at the front if needed
                 pad = np.repeat(scaled[0:1], self.time_steps - scaled.shape[0], axis=0)
                 scaled = np.vstack([pad, scaled])
             X_input = scaled[-self.time_steps:].reshape(1, self.time_steps, 1)
 
-            # Predict
-            predicted_scaled = self.model.predict(X_input, verbose=0)
+            # ── 이슈 7: CPU-bound model.predict → 워커 스레드로 분리 ──────
+            predict_fn = functools.partial(self.model.predict, X_input, verbose=0)
+            predicted_scaled = await asyncio.to_thread(predict_fn)
+            # ─────────────────────────────────────────────────────────────
+
             predicted_price = float(self.scaler.inverse_transform(predicted_scaled)[0][0])
             current_price = float(prices[-1])
 
-            expected_return = ((predicted_price - current_price) / current_price * 100.0) if current_price > 0 else 0.0
+            expected_return = (
+                (predicted_price - current_price) / current_price * 100.0
+                if current_price > 0 else 0.0
+            )
             signal = "BUY" if predicted_price > current_price * 1.02 else "HOLD"
 
             return {
@@ -101,7 +108,6 @@ class TensorFlowPredictor:
             raise
         except Exception as e:
             logger.error(f"Prediction failed for {stock_code}: {e}")
-            # Degrade gracefully
             return {
                 "stock_code": stock_code,
                 "error": str(e),