import pandas as pd
import streamlit as st
from utils import df_to_html, render_svg, combine_json_files, render_metadata

data = combine_json_files('./languages')


@st.cache_data
def render_home_table():
    """Renders home table."""
    # Compute number of unique domains/urls
    for key in data.keys():
        data[key]['Number of Sites'] = len(data[key].get('Sites', []))
        data[key]["Number of Links"] = sum(len(url_data["Links"]) for url_data in data[key].get('Sites', []))

    # Convert dict to df
    df_data = pd.DataFrame(data).transpose()
    df_data['ISO Code'] = df_data.index

    # Sort by ISO Code
    df_data = df_data.sort_values(by='ISO Code')

    df_data['Number of Sites'] = df_data['Number of Sites'].astype(str)  # Convert to string
    df_data['ISO Code'] = df_data['ISO Code'].astype(str)  # Convert to string
    df_data['Number of Sites'] = df_data.apply(lambda row: '<a href="/?isocode={}&site=True" target="_self">{}</a>'.format(row['ISO Code'], row['Number of Sites']), axis=1)
    df_data['Number of Links'] = df_data.apply(lambda row: '<a href="/?isocode={}&links=True" target="_self">{}</a>'.format(row['ISO Code'], row['Number of Links']), axis=1)


    # Display the table
    df_data = df_data[['ISO Code', 'Language Name', 'Family', 'Subgrouping', 'Number of Sites', 'Number of Links', 'Number of Speakers', 'Supported by allenai/MADLAD-400 or facebook/flores']]
    st.write(df_to_html(df_data), unsafe_allow_html=True)


@st.cache_data
def render_site_table(isocode):

    # back 
    back_text = '<a href="/?home=True" target="_self">[Back]</a>'
    st.markdown(back_text, unsafe_allow_html=True)

    st.write({'Language Name': data[isocode]['Language Name'], 'ISO Code': isocode})

    # site
    urls = data[isocode].get('Sites', [])
    df_urls = pd.DataFrame(urls)
    df_urls['Number of Links'] = df_urls['Links'].apply(len)
    df_urls = df_urls.sort_values(by='Number of Links', ascending=False)
    df_urls = df_urls.reset_index(drop=True)
    df_urls['Number of Links'] = df_urls.apply(lambda row: '<a href="/?isocode={}&siteurl={}" target="_self">{}</a>'.format(isocode, row['Site URL'], row['Number of Links']), axis=1)
    df_urls['Site URL'] = df_urls['Site URL'].apply(lambda url: f'<a href="{url}">{url}</a>')

    # Display the table
    df_urls = df_urls[['Site Name', 'Site URL', 'Category', 'Number of Links', 'Possible Parallel Languages']]
    st.write(df_to_html(df_urls), unsafe_allow_html=True)


@st.cache_data
def render_siteurl_table(isocode, url):

    # back
    back_text = '<a href="/?isocode={}&site=True" target="_self">[Back]</a>'.format(isocode)
    st.markdown(back_text, unsafe_allow_html=True)

    # Find selected domain
    urls = data[isocode].get('Sites', [])
    selected_domain = next((d for d in urls if 'Site URL' in d and d['Site URL'] == url), None)
    
    if selected_domain:
        st.write({'Language Name': data[isocode]['Language Name'], 'ISO Code': isocode, 'Site URL': url, 'Links': selected_domain['Links']})



@st.cache_data
def render_links_table(isocode):

    # back 
    back_text = '<a href="/?home=True" target="_self">[Back]</a>'
    st.markdown(back_text, unsafe_allow_html=True)

    # output
    urls = data[isocode].get('Sites', [])
    lang_name = data[isocode]['Language Name']
    all_urls = [{'Site URL': du['Site URL'], 'Links': du['Links']} for du in urls] 
        
    st.write({'Language Name': lang_name, 'ISO Code': isocode, 'URLs': all_urls})



# show logo
render_svg(open("assets/glotweb_logo.svg").read())

def main():
    params = st.query_params

    if 'isocode' in params:
        if 'siteurl' in params:
            render_siteurl_table(params['isocode'], params['siteurl'])
        if 'site' in params:
            render_site_table(params['isocode'])
        if 'links' in params:
            render_links_table(params['isocode'])

    else:
        # show home
        render_metadata()
        st.markdown("**GlotWeb** is an indexing service for low-resource languages. It indexes sites or links written in each language. This list can be used to create raw text or parallel corpora and to study low-resource languages on the web.\n")
        render_home_table()

main()