Update app.py

app.py

@@ -23,12 +23,13 @@ MATCH_RESULTS_URL = "https://huggingface.co/datasets/huggingface-projects/bot-fi
 @st.cache_data(ttl=1800)
 def fetch_match_history():
     """
-    Fetch match history.
+    Fetch the match results from the last 24 hours.
     Cache the result for 30min to avoid unnecessary requests.
     Return a DataFrame.
     """
     df = pd.read_csv(MATCH_RESULTS_URL)
     df["timestamp"] = pd.to_datetime(df.timestamp, unit="s")
+    df = df[df["timestamp"] >= pd.Timestamp.now() - pd.Timedelta(hours=24)]
     df.columns = ["home", "away", "timestamp", "result"]
     return df
 
@@ -42,7 +43,7 @@ teams = sorted(
     list(pd.concat([match_df["home"], match_df["away"]]).unique()), key=str.casefold
 )
 
-st.title("🤗 SoccerTwos Challenge Analytics")
+st.title("🤗 SoccerTwos Challenge Different Metrics - Just for Fun! - Only last 24hours of games considered")
 
 team_results = {}
 for i, row in match_df.iterrows():
@@ -59,7 +60,7 @@ for i, row in match_df.iterrows():
     if result == 0:
         team_results[home_team][2] += 1
         team_results[away_team][0] += 1
-        team_results[home_team][3] += 3
+        team_results[home_team][3] += 1
     elif result == 1:
         team_results[home_team][0] += 1
         team_results[away_team][2] += 1
@@ -67,18 +68,16 @@ for i, row in match_df.iterrows():
     else:
         team_results[home_team][1] += 1
         team_results[away_team][1] += 1
-        team_results[home_team][3] += 1
-        team_results[away_team][3] += 1
-
+        team_results[away_team][3] += 3
 
 df = pd.DataFrame.from_dict(
     team_results, orient="index", columns=["wins", "draws", "losses", "points"]
-).sort_index()
+).sort_values(by="points", ascending=False)
 df[["owner", "team"]] = df.index.to_series().str.split("/", expand=True)
 df = df[["owner", "team", "wins", "draws", "losses", "points"]]
 df["win_pct"] = (df["wins"] / (df["wins"] + df["draws"] + df["losses"])) * 100
 
-stats = df.sort_values(by='points', ascending=False)
+stats = df
 
 st.header("League Table")
 st.dataframe(stats)