import math

import pandas as pd
import streamlit as st


@st.cache_data
def load_data(file, skiprows=0, encoding="ISO-8859-1"):
    df = pd.read_csv(file, sep="\t", skiprows=skiprows, encoding=encoding)
    return df


@st.cache_data
def convert_df(df):
    return df.to_csv(sep="\t", index=False).encode('utf-8')


@st.cache_data    
def extract_surname(name):
    """Add first name and last name columns"""
    non_names = ['1992 Hoops set', '2016 Topps Now Highlights', 'Fox-Aparicio', 
                 '1960 World Series', 'Mantle/Berra', 'Ashburn-Mays', 'Ruth/Aaron/Mays',
                'Mays/Snider', 'New York Yankees', 'Checklist']
    if not isinstance(name, str):
        return None, None
    if name == "G Hill Tribute":
        return "G", "Hill"
    if name == 'Ken Griffey, Jr.' or name=='Ken Griffey Jr.':
        return "Ken", "Griffey Jr."
    elif 'Vladimir Guerrero Jr.' in name:
        return "Vladimir", 'Guerrero Jr.'
    elif any(item in name for item in non_names):
        return 'multiple', 'multiple'
    elif "," in name:
        return "multiple", 'multiple'
    else:
        if "-" in name or "/" in name:
            print(name)
            raise Exception("Found suspected multiple-name card!")
        return " ".join(name.split()[:-1]), name.split()[-1]


@st.cache_data     
def add_grading_status(grader):
    """Add graded column (yes/no)"""
    if isinstance(grader, str):
        return "Yes"
    elif math.isnan(grader):
        return "No"
    else:
        print(grader)
        raise Exception("Found unexpected item in Grader column!")


@st.cache_data
def get_default_sort_order(df):
    default_cols = [c for c in ["Type", "Graded", "Sport", "Last Name", "Year"] if \
                    c in df.columns.values]
    default_sort_order = [{"column" : item, "order": i }for i, item in \
                          enumerate(default_cols)]
    for col_name in df.columns.values:
        if col_name not in default_cols:
            default_sort_order.append({"column" : col_name, "order": None})
    return pd.DataFrame(default_sort_order)


@st.cache_data
def get_sort_order(edited_sort_order):
    sort_columns = edited_sort_order.dropna(subset="order")
    cols = sort_columns.column.tolist()
    orders = sort_columns.order.tolist()
    return [col for (col, _) in sorted(zip(cols, orders), key=lambda x: x[1])]


@st.cache_data
def add_graded_column(df):
    if "Grader" in df:
        df["Graded"] = df['Grader'].apply(lambda x: add_grading_status(x))
        df["Graded"] = pd.Categorical(df["Graded"], categories = ["Yes", "No"])  # sets sort order
        df = df.drop(columns=['Grader'])
    else:
        st.warning('Input data must have a "Grader" column'
                 ' in order to create a "Graded" column', icon="⚠️")
    return df


@st.cache_data
def add_multiple_column(df):
    if "Quantity" in df:
        df['Multiple'] = df['Quantity'].apply(lambda x: "Yes" if x>1 else "No")
        df["Multiple"] = pd.Categorical(df["Multiple"], categories = ["Yes", "No"])
        df = df.drop(columns=['Quantity'])
    else:
        st.warning('Input data must have a "Quantity" column'
                 ' in order to do this', icon="⚠️")
    return df


@st.cache_data
def add_first_and_last_name_columns(df):
    if "Player Name" in df:
        df['First Name'] = df['Player Name'].apply(lambda x: extract_surname(x)[0])
        df['Last Name'] = df['Player Name'].apply(lambda x: extract_surname(x)[-1])
    else:
        st.warning('Input data must have a "Player Name" column'
                 ' in order to extract first and last names', icon="⚠️")
    return df


if __name__ == "__main__":
    st.markdown("# Baseball card data wrangling")
    st.write("Upload a tab-separated spreadsheet. The first row should contain column"
             " names.")
    input_file = st.file_uploader("Choose a file", type=['txt', 'csv', 'tsv',  'xlsx'])

    if st.checkbox("Use sample data 1 (baseball cards)"):
        input_file = "sample_data/sample_data_1.txt"
    elif st.checkbox("Use sample data 2 (big cats)"):
        input_file = "sample_data/sample_data_2.txt"
        
    if input_file is not None:    
        df = load_data(input_file)
        st.subheader('Input data')
        st.write(df)

        if st.checkbox('Create first name and last name columns'):
            df = add_first_and_last_name_columns(df)

        if st.checkbox('Add "Graded" column and remove "Grader" column'):
            df = add_graded_column(df)

        if st.checkbox('Create "Multiple" column and remove "Quantity" column'):
            df = add_multiple_column(df)
        
        if st.checkbox("Change sort order"):
            st.subheader("Column sort order")
            st.write("Edit the sort priority by changing the numbers in the table below."
                     " Click the sort button below when you're done.")
            default_sort_order = get_default_sort_order(df)
            edited_sort_order = st.data_editor(default_sort_order)
    
            do_sort = st.button("Sort")
        
            if do_sort:
                col_order = get_sort_order(edited_sort_order)
                st.subheader('Sorted output data')
                df = df.sort_values(by=col_order).reset_index(drop=True)


        st.subheader('Output data')
        st.write(df)

        output_file = st.text_input("Enter name for the file to be downloaded", value="cards_output.tsv")
        if output_file is not None:
            csv = convert_df(df)
            st.download_button("Download data as tab-separated values", csv, file_name=output_file)