Implement entry point to get stats and stub out live stats with cache

src/stats.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass
 import pandas as pd
+import streamlit as st
 
 from domain.playoffs import PLAYOFF_TEAM_DEF_PLAYER
 from queries.nflverse.github_data import get_player_kicking_stats, get_player_stats, get_team_defense_stats
@@ -131,6 +132,8 @@ def add_stats_from_team_def_df_to_stat_map(df: pd.DataFrame, stat_map):
             stat_map[week][player_id].update(player_stats)
 
 
+# 24 hour cache
+@st.cache_data(ttl=60 * 60 * 24)
 def assemble_nflverse_stats() -> dict[int, dict[str, dict[str, float]]]:
     # map week -> player_id -> stat_key -> stat value
     stat_map: dict[int, dict[str, dict[str, float]]] = {w: {} for w in NFLVERSE_STAT_WEEK_TO_PLAYOFF_WEEK.values()}
@@ -144,3 +147,26 @@ def assemble_nflverse_stats() -> dict[int, dict[str, dict[str, float]]]:
     add_stats_from_team_def_df_to_stat_map(df_def_stats, stat_map)
 
     return stat_map
+
+
+# 10 minute cache
+@st.cache_data(ttl=60 * 10)
+def get_live_stats() -> dict[int, dict[str, dict[str, float]]]:
+    stat_map: dict[int, dict[str, dict[str, float]]] = {w: {} for w in NFLVERSE_STAT_WEEK_TO_PLAYOFF_WEEK.values()}
+    # TODO - implement live stats
+    return stat_map
+
+
+def get_stats_map() -> dict[int, dict[str, dict[str, float]]]:
+    # use live stats if available
+    stat_map = get_live_stats()
+
+    # use more permanent nflverse stats over live
+    nflverse_stats = assemble_nflverse_stats()
+
+    # we overwrite the live stats with nflverse stats if they exist for the same player
+    for week, week_stats in nflverse_stats.items():
+        for player_id, player_stats in week_stats.items():
+            stat_map[week][player_id] = player_stats
+
+    return stat_map