first commit

app.py

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+import pandas as pd
+import streamlit as st
+
+# Set page title and favicon
+st.set_page_config(page_icon=":soccer:",layout="wide")
+
+
+st.markdown(
+    """
+            <style>
+                .block-container {
+                    padding-top: 1rem;
+                }
+                #MainMenu {visibility: hidden;}
+            </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Set title and create a new tab for league history
+st.title("⚽ SoccerTwos Challenge Analytics Extra!⚽ ")
+tab_team, tab_owners = st.tabs(["Form Table", "Games by Author",])
+
+# Match Results
+MATCH_RESULTS_URL = "https://huggingface.co/datasets/huggingface-projects/bot-fight-data/raw/main/soccer_history.csv"
+
+
+@st.cache(ttl=1800)
+def 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
+
+
+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]
+
+# 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
+)
+
+# 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"]
+    away_team = row["away"]
+    result = row["result"]
+
+    if home_team not in team_results:
+        team_results[home_team] = [0, 0, 0]
+
+    if away_team not in team_results:
+        team_results[away_team] = [0, 0, 0]
+
+    if result == 0:
+        team_results[home_team][2] += 1
+        team_results[away_team][0] += 1
+    elif result == 1:
+        team_results[home_team][0] += 1
+        team_results[away_team][2] += 1
+    else:
+        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()
+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
+
+
+# Get a list of all teams that have played in the last 24 hours
+
+
+@st.cache_data(ttl=1800)
+def fetch_owners():
+    """
+    Fetch a list of all owners who have played in the matches, along with the number of teams they own
+    and the number of unique teams they played with.
+    """
+    # Extract the owner name and team name from each home and away team name in the DataFrame
+    team_owners = match_df["home"].apply(lambda x: x.split('/')[0]).tolist() + match_df['away'].apply(lambda x: x.split('/')[0]).tolist()
+    teams = match_df["home"].apply(lambda x: x.split('/')[1]).tolist() + match_df['away'].apply(lambda x: x.split('/')[1]).tolist()
+
+    # Count the number of games played by each owner and the number of unique teams they played with
+    owner_team_counts = {}
+    owner_team_set = {}
+    for i, team_owner in enumerate(team_owners):
+        owner = team_owner.split(' ')[0]
+        if owner not in owner_team_counts:
+            owner_team_counts[owner] = 1
+            owner_team_set[owner] = {teams[i]}
+        else:
+            owner_team_counts[owner] += 1
+            owner_team_set[owner].add(teams[i])
+
+    # Create a DataFrame from the dictionary
+    owners_df = pd.DataFrame.from_dict(owner_team_counts, orient="index", columns=["Games played by owner"])
+    owners_df["Unique teams by owner"] = owners_df.index.map(lambda x: len(owner_team_set[x]))
+
+    # Return the DataFrame
+    return owners_df
+
+
+
+
+
+
+# Display the DataFrame as a table, sorted by win percentage
+with tab_team:
+    st.write("Form Table for previous 24 hours, ranked by win percentage")
+    stats = df.sort_values(by="win_pct", ascending=False)
+    styled_stats = stats.style.set_table_attributes("style='font-size: 20px'").set_table_styles([dict(selector='th', props=[('max-width', '200px')])])
+    styled_stats = styled_stats.set_table_attributes("style='max-height: 1200px; overflow: auto'")
+    st.dataframe(styled_stats)
+
+
+# Create a DataFrame from the list of owners and their number of teams
+owners_df = fetch_owners()
+
+# Display the DataFrame as a table
+with tab_owners:
+    
+    st.dataframe(owners_df)
+    
+
+    

requirements.txt

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+streamlit
+pandas