import scipy  # To use create_distplot() as ff has a requirement of scipy import
import streamlit as st
import pandas as pd
import preprocessor
import helper
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.figure_factory as ff

df = pd.read_csv("athlete_events.csv")
region_df = pd.read_csv("noc_regions.csv")

df = preprocessor.preprocess(df, region_df)

st.sidebar.title("Olympics Analysis")
st.sidebar.image(
    "https://e7.pngegg.com/pngimages/1020/402/png-clipart-2024-summer-olympics-brand-circle-area-olympic"
    "-rings-olympics-logo-text-sport.png"
)

# Creation of basic skeleton of the web app
user_menu = st.sidebar.radio(
    "Select an Option",
    (
        "Medal Tally",
        "Overall Analysis",
        "Country-wise Analysis",
        "Athlete wise Analysis",
    ),
)

# Displaying the returned dataframe from preprocess() fn.
# st.dataframe(df)

if user_menu == "Medal Tally":
    st.sidebar.header("Medal Tally")
    years, country = helper.country_year_list(df)
    selected_year = st.sidebar.selectbox("Select Year", years)
    selected_country = st.sidebar.selectbox("Select Country", country)

    # medal_tally = helper.medal_tally(df)
    medal_tally = helper.fetch_medal_tally(df, selected_year, selected_country)
    if selected_year == "Overall" and selected_country == "Overall":
        st.title("Overall Tally")
    if selected_year != "Overall" and selected_country == "Overall":
        st.title(f"Medal Tally in {selected_year} Olympics")
    if selected_year == "Overall" and selected_country != "Overall":
        st.title(f"{selected_country} overall performance")
    if selected_year != "Overall" and selected_country != "Overall":
        st.title(f"{selected_country} performance in {selected_year} Olympics")

    # st.dataframe(medal_tally)
    st.table(medal_tally)

if user_menu == "Overall Analysis":
    # Some stats to be shown beforehand
    editions = df["Year"].unique().shape[0] - 1
    cities = df["City"].unique().shape[0]
    sports = df["Sport"].unique().shape[0]
    events = df["Event"].unique().shape[0]
    athletes = df["Name"].unique().shape[0]
    nations = df["region"].unique().shape[0]

    st.title("Top Statistics")
    col1, col2, col3 = st.columns(3)
    with col1:
        st.header("Editions")
        st.title(editions)
    with col2:
        st.header("Hosts")
        st.title(cities)
    with col3:
        st.header("Sports")
        st.title(sports)

    col1, col2, col3 = st.columns(3)
    with col1:
        st.header("Events")
        st.title(events)
    with col2:
        st.header("Nations")
        st.title(nations)
    with col3:
        st.header("Athletes")
        st.title(athletes)

    # nations_over_time = helper.participating_nations_over_time(df)
    nations_over_time = helper.data_over_time(df, "region")
    # Line plot of nations_over_time
    fig = px.line(nations_over_time, x="Year/Edition", y="region")
    st.title("Participating Nations over the years")
    st.plotly_chart(fig)

    events_over_time = helper.data_over_time(df, "Event")
    # Line plot of events_over_time
    fig = px.line(events_over_time, x="Year/Edition", y="Event")
    st.title("Events over the years")
    st.plotly_chart(fig)

    athlete_over_time = helper.data_over_time(df, "Name")
    # Line plot of events_over_time
    fig = px.line(athlete_over_time, x="Year/Edition", y="Name")
    st.title("Athletes over the years")
    st.plotly_chart(fig)

    st.title("No. of Events over time(Every Sport)")
    fig, ax = plt.subplots(figsize=(20, 20))
    x = df.drop_duplicates(["Year", "Sport", "Event"])
    hm = (
        x.pivot_table(index="Sport", columns="Year", values="Event", aggfunc="count")
        .fillna(0)
        .astype("int")
    )
    ax = sns.heatmap(hm, annot=True)
    st.pyplot(fig)

    st.title("Most successful Athletes")
    # For dropdown on the app to choose the particular sport
    sport_list = df["Sport"].unique().tolist()
    sport_list.sort()
    sport_list.insert(0, "Overall")

    selected_sport = st.selectbox("Select a Sport", sport_list)
    x = helper.most_successful(df, selected_sport)
    st.table(x)

if user_menu == "Country-wise Analysis":

    st.sidebar.title("Country-wise Analysis")

    # For a Dropdown for selecting country
    country_list = df["region"].dropna().unique().tolist()
    country_list.sort()

    selected_country = st.sidebar.selectbox("Select a Country", country_list)

    country_df = helper.yearwise_medal_tally(df, selected_country)
    fig = px.line(country_df, x="Year", y="Medal")
    st.title(f"{selected_country} Medal Tally over the years")
    st.plotly_chart(fig)

    st.title(f"{selected_country} excels in the following sports:")
    pt = helper.country_event_heatmap(df, selected_country)
    fig, ax = plt.subplots(figsize=(20, 20))
    ax = sns.heatmap(pt, annot=True)
    st.pyplot(fig)

    st.title(f"Top 10 athletes of {selected_country}")
    top10_df = helper.most_successful_countrywise(df, selected_country)
    st.table(top10_df)

if user_menu == "Athlete wise Analysis":
    # Dropping duplicates in Name and region since the same athlete may have played in many olympics
    athlete_df = df.drop_duplicates(subset=["Name", "region"])
    # Plotting curve for specific medal winning athletes
    x1 = athlete_df["Age"].dropna()
    x2 = athlete_df[athlete_df["Medal"] == "Gold"]["Age"].dropna()
    x3 = athlete_df[athlete_df["Medal"] == "Silver"]["Age"].dropna()
    x4 = athlete_df[athlete_df["Medal"] == "Bronze"]["Age"].dropna()
    # Distribution plot over the Age column of diff medal-winning athletes
    fig = ff.create_distplot(
        [x1, x2, x3, x4],
        ["Overall Age", "Gold Medalist", "Silver Medalist", "Bronze Medalist"],
        show_hist=False,
        show_rug=False,
    )
    fig.update_layout(autosize=False, width=1000, height=600)
    st.title("Distribution of Age")
    st.plotly_chart(fig)

    # Plotting distribution of Age w.r.t. each sport
    famous_sports = [
        "Basketball",
        "Judo",
        "Football",
        "Tug-Of-War",
        "Athletics",
        "Swimming",
        "Badminton",
        "Sailing",
        "Gymnastics",
        "Art Competitions",
        "Handball",
        "Weightlifting",
        "Wrestling",
        "Water Polo",
        "Hockey",
        "Rowing",
        "Fencing",
        "Shooting",
        "Boxing",
        "Taekwondo",
        "Cycling",
        "Diving",
        "Canoeing",
        "Tennis",
        "Golf",
        "Softball",
        "Archery",
        "Volleyball",
        "Synchronized Swimming",
        "Table Tennis",
        "Baseball",
        "Rhythmic Gymnastics",
        "Rugby Sevens",
        "Beach Volleyball",
        "Triathlon",
        "Rugby",
        "Polo",
        "Ice Hockey",
    ]

    x = []
    name = []
    for sport in famous_sports:
        temp_df = athlete_df[athlete_df["Sport"] == sport]
        # Especially for Gold Medalists
        x.append(temp_df[temp_df["Medal"] == "Gold"]["Age"].dropna())
        name.append(sport)

    fig = ff.create_distplot(x, name, show_hist=False, show_rug=False)
    fig.update_layout(autosize=False, width=1000, height=600)
    st.title("Distribution of Age w.r.t. Sports(Gold Medalist)")
    st.plotly_chart(fig)

    # For dropdown on the app to choose the particular sport
    sport_list = famous_sports
    sport_list.sort()
    sport_list.insert(0, "Overall")

    st.title("Height Vs Weight")
    selected_sport = st.selectbox("Select a Sport", sport_list)
    temp_df = helper.weight_v_height(df, selected_sport)

    fig, ax = plt.subplots()
    ax = sns.scatterplot(
        x=temp_df["Weight"],
        y=temp_df["Height"],
        hue=temp_df["Medal"],
        style=temp_df["Sex"],
        s=60,
    )
    st.pyplot(fig)

    st.title("Men Vs Women Participation Over the Years")
    final = helper.men_vs_women(df)
    fig = px.line(final, x="Year", y=["Male", "Female"])
    fig.update_layout(autosize=False, width=1000, height=600)
    st.plotly_chart(fig)