Upload 5 files

app.py

@@ -22,6 +22,7 @@ from nifty_backend.runtime import (
     refresh_daily_data,
     refresh_first5_prediction,
     seconds_until_next_ist_run,
+    warm_dashboard_payload_cache,
 )
 
 
@@ -204,6 +205,13 @@ async def refresh_current_session_once() -> None:
         print(f"[startup] daily refresh failed: {exc}", flush=True)
 
 
+async def warm_dashboard_payload_cache_once() -> None:
+    try:
+        await asyncio.to_thread(warm_dashboard_payload_cache)
+    except Exception as exc:
+        print(f"[startup] dashboard payload warmup failed: {exc}", flush=True)
+
+
 @app.on_event("startup")
 async def start_scheduler() -> None:
     global market_status
@@ -224,6 +232,7 @@ async def start_scheduler() -> None:
         market_status = "Prediction Pending"
         
     asyncio.create_task(refresh_current_session_once())
+    asyncio.create_task(warm_dashboard_payload_cache_once())
     asyncio.create_task(daily_ist_refresh_loop())
 
 

nifty_backend/runtime.py

@@ -1,9 +1,12 @@
 from __future__ import annotations
 
 import json
+import copy
 import sys
+import threading
 from dataclasses import dataclass
 from datetime import date, datetime, time, timedelta
+from functools import lru_cache
 from pathlib import Path
 from typing import Any
 from zoneinfo import ZoneInfo
@@ -46,7 +49,10 @@ DECISION_OVERLAYS = [
     },
 ]
 
+_dashboard_payload_lock = threading.Lock()
 
+
+@lru_cache(maxsize=1)
 def _nse_calendar():
     if mcal is None:
         return None
@@ -58,6 +64,7 @@ def _nse_calendar():
     return None
 
 
+@lru_cache(maxsize=64)
 def trading_schedule(start: date, end: date) -> pd.DataFrame:
     calendar = _nse_calendar()
     if calendar is None:
@@ -344,7 +351,30 @@ def predict_row(row: pd.DataFrame) -> Prediction:
     return prediction
 
 
+def _file_cache_key(path: Path) -> tuple[str, int | None, int | None]:
+    try:
+        stat = path.stat()
+    except FileNotFoundError:
+        return (str(path), None, None)
+    return (str(path), stat.st_mtime_ns, stat.st_size)
+
+
+@lru_cache(maxsize=16)
+def _latest_saved_prediction_cached(latest_key: tuple[str, int | None, int | None], summary_key: tuple[str, int | None, int | None]) -> dict[str, Any]:
+    latest_path = Path(latest_key[0])
+    if latest_path.exists():
+        return pd.read_csv(latest_path).iloc[-1].to_dict()
+    summary_path = Path(summary_key[0])
+    if summary_path.exists():
+        return json.loads(summary_path.read_text(encoding="utf-8"))
+    raise FileNotFoundError("No latest prediction is available yet.")
+
+
 def latest_saved_prediction() -> dict[str, Any]:
+    return dict(_latest_saved_prediction_cached(_file_cache_key(LATEST_PATH), _file_cache_key(MODEL_DIR / "summary.json")))
+
+
+def _latest_saved_prediction_uncached() -> dict[str, Any]:
     if LATEST_PATH.exists():
         return pd.read_csv(LATEST_PATH).iloc[-1].to_dict()
     summary_path = MODEL_DIR / "summary.json"
@@ -387,8 +417,27 @@ def load_test_predictions() -> pd.DataFrame:
 
 
 def dashboard_payload() -> dict[str, Any]:
+    key = (
+        _file_cache_key(MODEL_DIR / "summary.json"),
+        _file_cache_key(LATEST_PATH),
+        _file_cache_key(TEST_PREDICTIONS_PATH),
+        _file_cache_key(NIFTY_1D_PATH),
+        _file_cache_key(OPENING_DATASET_PATH),
+        _file_cache_key(MODEL_DIR / "candidate_results.csv"),
+        _file_cache_key(NIFTY_1M_PATH),
+    )
+    with _dashboard_payload_lock:
+        return copy.deepcopy(_dashboard_payload_cached(key))
+
+
+def warm_dashboard_payload_cache() -> None:
+    dashboard_payload()
+
+
+@lru_cache(maxsize=4)
+def _dashboard_payload_cached(key: tuple[tuple[str, int | None, int | None], ...]) -> dict[str, Any]:
     summary = load_model_summary()
-    latest = latest_saved_prediction()
+    latest = _latest_saved_prediction_uncached()
     test = load_test_predictions()
     daily = pd.read_parquet(NIFTY_1D_PATH)
     daily["date"] = pd.to_datetime(daily["date"], errors="coerce")