# -*- coding: utf-8 -*-
#!/usr/bin/python2.7 -S
# python -m spacy.en.download python -m spacy.de.download
# https://spacy.io/docs/#tutorials
# CSS: http://codepen.io/explosion/pen/xEpgKz
# CSS 2: https://explosion.ai/blog/displacy-ent-named-entity-visualizer
import time
start_time = time.time()

'''
>>> 'abc'.decode('utf-8')  # str to unicode
u'abc'
>>> u'abc'.encode('utf-8') # unicode to str
'abc' 
'''

# pip install --upgrade thinc
# pip3 install suds-jurko
import spacy
#import base64
import json
#import site
import codecs
import locale
import shelve
import gensim, logging
import pprint
import os.path
#import sphinxapi as SP
from sphinxapi import *
# pip install --upgrade mysql-python
import MySQLdb
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)


# pip3 install --upgrade spacy
# apt-get install python3-pip
# pip install --upgrade pip
# pip3 install --upgrade pip3
# pip install -U textblob
# python -m textblob.download_corpora
# pip install --upgrade langdetect
# pip install -U textblob-de
# python -m textblob.download_corpora
#from textblob_de import TextBlobDE as TextBlob
from langdetect import detect
#from textblob_de import TextBlobDE
#from textblob import TextBlob
from libleipzig import * # pip install --upgrade libleipzig
# pip install --upgrade libleipzig
# pip3 install --upgrade libleipzig
# pip install --upgrade pattern
import sys, time  # import sys package, if not already imported
reload(sys)
sys.setdefaultencoding('utf-8')
#sys.stdout = codecs.getwriter(locale.getpreferredencoding())(sys.stdout)

###SqlQuery = "SELECT * FROM `synonym_unique` WHERE `word` LIKE 'Zusammenhalt'"
SqlQuery = "SELECT word, synonyms, uid FROM synonym_unique WHERE 1 ORDER BY uid ASC"
#pprint.pprint(SqlQuery)

# Open database connection
# https://www.tutorialspoint.com/python/python_database_access.htm


def do_training(sentences, word):

	path 	= "/home/100biere/pretrained"+"/"+word.lower()+".w2v.bin"
	print("Working on: "+word)
	
	model 	= gensim.models.Word2Vec(sentences, hs=1, iter=400, size=400, workers=1, sorted_vocab=1, alpha=0.325, min_count=1)
	model.init_sims(replace=False) 	# can read,write from, and also training -> more memory
	#model.init_sims(replace=True)	# can only read from, but no more training -> less memory
	model.save(path)

	return 1

try:
	dbs = MySQLdb.connect(user="root",
						passwd="###########99",
						host="127.0.0.1",
						unix_socket="/var/run/mysqld/mysqld.sock",
						port=3306,
						db="onetopp")

	# Execute the SQL command
	cursor = dbs.cursor()
	cursor.execute(SqlQuery)
	dbs.commit()

	# Fetch all the rows in a list of lists.
	results = cursor.fetchall()

	# pprint.pprint(results)
	#sys.exit(0)

	res = []
	for row in results:
		word 		= row[0]
		synonyms 	= row[1]
		uid 		= row[2]
		# Now print fetched result
		#print "HITS: word=%s,synonyms=%s" % (word, synonyms )
		w 	= synonyms.split(";")
		
		my_w = word.encode("latin-1")
		do_training(synonyms, my_w)
		
		file = codecs.open("/home/100biere/pretrained/trained.log", "a", "utf-8")
		file.write('{:08d}\n'.format(uid))
		file.write(" -> word: "+my_w+"\n"),
		file.close()
		
		
except e:
	#print "Error %d: %s" % (e.args[0],e.args[1])
	print(cursor._last_executed)
	print ("Error: unable to fetch data")
	#for row in cursor:
	#	print(row)        

# disconnect from server
cursor.close()
dbs.close()

print("Script Runtime: --- %s seconds ---" % (time.time() - start_time))
sys.exit(0)