Update app.py

app.py

@@ -1,10 +1,7 @@
 import datetime
-
 import pandas as pd
 import streamlit as st
 import timeago
-import plotly.graph_objects as go
-
 
 st.set_page_config(layout="wide")
 st.markdown(
@@ -19,41 +16,41 @@ st.markdown(
     unsafe_allow_html=True
 )
 
-now = datetime.datetime.now()
+# Set title and create a new tab for league history
+st.title("SoccerTwos Challenge Form Table! - Only last 24hours of games considered")
+tab_team, tab_history = st.tabs(["Form Table", "League History Overtimer"])
+
+# Fetch the match results from the last 24 hours
 MATCH_RESULTS_URL = "https://huggingface.co/datasets/huggingface-projects/bot-fight-data/raw/main/soccer_history.csv"
 
 
 @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
 
 
-def days_left():
-    end_date = datetime.date(2023, 4, 30)
-    today = datetime.date.today()
-    time_until_date = end_date - today
-    return time_until_date.days
-
+match_df = fetch_match_history()
 
+# Define a function to calculate the total number of matches played
 def num_matches_played():
     return match_df.shape[0]
 
-
-match_df = fetch_match_history()
+# Get a list of all teams that have played in the last 24 hours
 teams = sorted(
     list(pd.concat([match_df["home"], match_df["away"]]).unique()), key=str.casefold
 )
 
-st.title("🤗 SoccerTwos Challenge Analytics")
-
+# Create the form table, which shows the win percentage for each team
+st.header("Form Table")
 team_results = {}
 for i, row in match_df.iterrows():
     home_team = row["home"]
@@ -76,7 +73,7 @@ for i, row in match_df.iterrows():
         team_results[home_team][1] += 1
         team_results[away_team][1] += 1
 
-
+# Create a DataFrame from the results dictionary and calculate the win percentage
 df = pd.DataFrame.from_dict(
     team_results, orient="index", columns=["wins", "draws", "losses"]
 ).sort_index()
@@ -84,123 +81,10 @@ df[["owner", "team"]] = df.index.to_series().str.split("/", expand=True)
 df = df[["owner", "team", "wins", "draws", "losses"]]
 df["win_pct"] = (df["wins"] / (df["wins"] + df["draws"] + df["losses"])) * 100
 
-stats = df
-
-tab_team, tab_competition = st.tabs(["Results", "Competition stats"])
-
-
-def get_text_result(row, team_name):
-    if row["home"] == team_name:
-        if row["result"] == 1:
-            return "Win"
-        elif row["result"] == 0.5:
-            return "Draw"
-        else:
-            return "Loss"
-    elif row["away"] == team_name:
-        if row["result"] == 0:
-            return "Win"
-        elif row["result"] == 0.5:
-            return "Draw"
-        else:
-            return "Loss"
-
-
-with tab_team:
-    team = st.selectbox("Team", teams)
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        c1, c2, c3 = st.columns(3)
-        with c1:
-            st.metric("Wins", f"{stats.loc[[team]]['wins'][0]}")
-        with c2:
-            st.metric("Draws", f"{stats.loc[[team]]['draws'][0]}")
-        with c3:
-            st.metric("Losses", f"{stats.loc[[team]]['losses'][0]}")
-
-        st.write("Results")
-        res_df = match_df[(match_df["home"] == team) | (match_df["away"] == team)]
-        res_df["result"] = res_df.apply(lambda row: get_text_result(row, team), axis=1)
-        opponent_column = res_df.apply(
-            lambda row: row["away"] if row["home"] == team else row["home"], axis=1
-        )
-        res_df["vs"] = opponent_column
-        result_column = res_df["result"]
-        new_df = pd.concat([opponent_column, result_column], axis=1)
-        new_df.columns = ["vs", "result"]
-        res_df[["owner", "team"]] = res_df["vs"].str.split("/", expand=True)
-        res_df["played"] = res_df["timestamp"].apply(lambda x: timeago.format(x, now))
-        res_df.sort_values(by=["timestamp"], ascending=True, inplace=True)
-        disp_res_df = res_df.drop(["home", "away", "vs", "timestamp"], axis=1)
-
-        def highlight_results(s):
-            colour = {
-                "Win": "LightGreen",
-                "Draw": "LightYellow",
-                "Loss": "LightSalmon",
-            }
-            return [f"background-color: {colour[s.result]}"] * len(s)
-
-        # Create a friendly index.
-        disp_res_df.reset_index(inplace=True, drop=True)
-        disp_res_df.index += 1
-        disp_res_df = disp_res_df.iloc[::-1]
-
-        # Display the table.
-        st.dataframe(disp_res_df.style.apply(highlight_results, axis=1))
-
-    with col2:
-        c1, c2 = st.columns(2)
-        with c1:
-            st.metric("Win rate", f"{stats.loc[[team]]['win_pct'][0]:.2f}%")
-
-            joined = res_df["timestamp"].min()
-        with c2:
-            st.metric("Competing since", f"{timeago.format(joined, now)}")
-
-        grouped = (
-            res_df.groupby([res_df["timestamp"].dt.date, "result"])
-            .size()
-            .reset_index(name="count")
-        )
-
-        loss_trace = go.Bar(
-            x=grouped.loc[grouped["result"] == "Loss", "timestamp"],
-            y=grouped.loc[grouped["result"] == "Loss", "count"],
-            name="Losses",
-            marker=dict(color="red"),
-        )
-        draw_trace = go.Bar(
-            x=grouped.loc[grouped["result"] == "Draw", "timestamp"],
-            y=grouped.loc[grouped["result"] == "Draw", "count"],
-            name="Draws",
-            marker=dict(color="orange"),
-        )
-        win_trace = go.Bar(
-            x=grouped.loc[grouped["result"] == "Win", "timestamp"],
-            y=grouped.loc[grouped["result"] == "Win", "count"],
-            name="Wins",
-            marker=dict(color="green"),
-        )
-
-        fig = go.Figure(data=[loss_trace, draw_trace, win_trace])
-        fig.update_layout(barmode="stack")
-        st.plotly_chart(fig)
-
-
-with tab_competition:
-    col1, col2, col3 = st.columns(3)
-
-    col1.metric("Matches played", f"{num_matches_played():,d}")
-    col2.metric("Live models", f"{len(teams)}")
-    col3.metric("Season ends in", f"{days_left()} days")
-
-    match_counts = (
-        match_df.groupby(match_df["timestamp"].dt.date).size().reset_index(name="count")
-    )
-    match_counts["matches_played"] = match_counts["count"].cumsum()
-
-    st.title("Matches played")
-    st.area_chart(match_counts.set_index("timestamp")["matches_played"])
+# Display the DataFrame as a table, sorted by win percentage
+stats = df.sort_values(by="win_pct", ascending=False)
+st.dataframe(stats)
+
+# Create a new tab for league history over time
+with tab_history:
+    st.write("Coming soon!")