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(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)