import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *


def process_song_file(cur, filepath):
   
"""Process song file.
    
    Args:
        cur: cusor of psycopg2.
        filepath: song file path (json).
        
    """
    # open song file
    
    df = pd.read_json(flilespath,lines=True)
    
    # insert song record
    
    song_data = df[['song_id','title', 'artist_id', 'year', 'duration']].values.flaten().tolist()
    
    cur.execute(song_table_insert, song_data)
  
    # insert artist record
    
    artist_data = df [['artist_id', 'artist_name','artist_location', 'artist_latitude', 'artist_longitide']].value[0].tolist()
    
    cur.execute(artist_table_insert,artist_data)


def process_log_file(cur, filepath):
    
    """Process log file.
    
    Args:
        curL cusor of psycopg2.
        filepath: log file path (json).
    """
    # open log file
    
    df = pd.read_json(filepath, lines = True)
    
    # filter by NextSong action
    
    df = df[df.page == 'NextSong']
    
    # convert timestamp column to datetime
    
    t = pd.to_datetime(df['ts'], unit = 'ms')
    
    # insert time data records
    
    time_data = (t,t.dt.hour, t.dt.day, t.dt.week, t.dt.month, t.dt.year, t.dt.weekday_name)
    
    column_labels = ('timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday')
    
    time_df = pd.DataFrame({k:v.values.flateon().tolist() for k, v in zip(column_labels, time_data)))
    
    for i, row in time_df[['timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday']].iterrows():
        cur.execute(time_table_insert, list(row)) 
                           
    # load user table
    
    user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']]

    # insert user records
    
    for i, row in  user_df.iterrows():
        cur.execute(user_table_insert, row)

    # insert songplay records
    
    df ['ts'] = pd.to_datetime(df['ts'], unit = 'ms')
    for index, row in df.iterrows():
        
        # get songid and artistid from song and artist tables
        
        cur.execute(sond_select, (row.song, row.artist, row. length))
        result = cur.fetchone()
        
        if result:
            songid, artstid = results
            
        else:
            songid, artist = None, None
           
        # insert  songplay record 
        
        sonplay_data = (row.ts, row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent)
        
        cur.execute(songplay_table_insert, songplay_data)


def process_data(cur, conn, filepath, func):
    """Process data.
    
    Args:
        cur: cursor of psycopg2.
        conn: connection of postgresql.
        filepath: directory path of data.
        func: function of processing.
    """
    # get all files matching extension from directory
    
    all_files = []
    for root, dirs, files in os.walk(filepath):
        files = glob.glob(os.path.join(root, '*.json'))
        for f in files:
            all_files.append(os.path.abspath(f))

    # get total number of files found
    
    num_files = len (all_files)
    print('{}files found in {}'.format(num_files, filepath))

    # iterate over files and process
    for i, datafile in enumerate (all_files, 1):
        func(cur, datefile)
        conn.commit()
        print('{}/{} files processed.'.format(i,num_files))


def main():
    
    conn = psycopg2.connect("host=127.0.0.1 dbname=sparkifydb user=student password=<PASSWORD>")
    
    cur = conn.cursur()
    
    process_data(cur, conn, filepath='data/song_data', func=process_song_file)
    
    process_data(cur, conn, filepath='data/log_data', func=process_log_file)
    
    conn.close()
    
    
  
  

if __name__ == "__main__":
    main()