from urlextract import URLExtract
from wordcloud import WordCloud
from collections import Counter
import pandas as pd
import emoji

extractor = URLExtract()


def fetch_states(selected_user, df):

    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    # 1. Number of messages
    num_messages = df.shape[0]

    # 2. Number of words
    words = []
    for message in df['message']:
        words.extend(message.split())

    # 3. Number of media
    num_media_messages = df[df['message'] == '<Media omitted>\n'].shape[0]

    # 4. Number of Links
    links = []
    for message in df['message']:
        links.extend(extractor.find_urls(message))

    return num_messages, len(words), num_media_messages, len(links)


def monthly_timeline(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    df['month_num'] = df['date'].dt.month
    timeline = df.groupby(['year', 'month_num', 'month']).count()[
        'message'].reset_index()

    time = []
    for i in range(timeline.shape[0]):
        time.append(timeline['month'][i] + '-' + str(timeline['year'][i]))
    timeline['time'] = time

    return timeline.rename(columns={'message': 'Message', 'time': 'Timeline'})


def daily_timeline(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    daily_timeline = df.groupby('only_date').count()['message'].reset_index()
    daily_timeline = daily_timeline.rename(
        columns={'only_date': 'Date', 'message': 'Message'})

    return daily_timeline


def week_activity_map(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    week_activity = df['day_name'].value_counts().reset_index()
    week_activity = week_activity.rename(
        columns={'day_name': 'Day', 'count': "Message"})

    return week_activity


def month_activity_map(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    month_activity = df['month'].value_counts().reset_index()
    month_activity = month_activity.rename(
        columns={'month': 'Month', 'count': "Message"})

    return month_activity


def activity_heatmap(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    user_heatmap = df.pivot_table(
        index='day_name', columns='period', values='message', aggfunc='count').fillna(0)

    user_heatmap = user_heatmap.rename_axis('Day', axis='index')
    user_heatmap = user_heatmap.rename_axis('Time Period', axis='columns')
    # user_heatmap = user_heatmap.rename_axis('Message', axis='color')

    return user_heatmap


def most_active_user(df):

    temp = df[df['user'] != 'group_notification']

    x = (temp['user'].value_counts().head()).reset_index().rename(
        columns={'user': 'User', 'count': 'Count'})

    per = round(((temp['user'].value_counts() / temp.shape[0]) * 100),
                2).reset_index().rename(columns={'user': 'User', 'count': 'Percent(%)'})
    return x, per


def create_wordcloud(selected_user, df):
    f = open('stop_ben-hin-eng.txt', 'r')
    stop_words = f.read()

    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    temp = df[df['user'] != 'group_notification']
    temp = temp[temp['message'] != '<Media omitted>\n']

    def remove_stop_words(message):
        y = []
        for word in message.lower().split():
            if word not in stop_words:
                y.append(word)
        return " ".join(y)

    wc = WordCloud(width=500, height=500, min_font_size=10,
                   background_color='white')
    temp['message'] = temp['message'].apply(remove_stop_words)
    df_wc = wc.generate(temp['message'].str.cat(sep=" "))

    return df_wc


def most_common_words(selected_user, df):
    f = open('stop_ben-hin-eng.txt', 'r')
    stop_words = f.read()

    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    temp = df[df['user'] != 'group_notification']
    temp = temp[temp['message'] != '<Media omitted>\n']

    words = []
    for message in temp['message']:
        for word in message.lower().split():
            if word not in stop_words:
                words.append(word)

    most_common_df = pd.DataFrame(Counter(words).most_common(
        20)).iloc[::-1].rename(columns={0: 'Message', 1: 'Count'})

    return most_common_df


def emoji_data(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    emojis = []
    for message in df['message']:
        emojis.extend([c for c in message if c in emoji.EMOJI_DATA])

    emoji_df = pd.DataFrame(Counter(emojis).most_common(
        len(Counter(emojis))))

    if emojis:
        emoji_df = emoji_df.rename(columns={0: 'Emoji', 1: 'Count'})

    return emoji_df


def data_timeframe(df):

    df_first = df.iloc[0]
    df_last = df.iloc[-1]

    timeframe = str(df_first['day']) + " " + str(df_first['month']) + " " + str(df_first['year']) + \
        " to " + str(df_last['day']) + " " + \
        str(df_last['month']) + " " + str(df_last['year'])

    return timeframe