db = MySQLdb.connect(mySQLUrl, userName, passwd, DBName, charset='utf8', use_unicode=True)

resultTuple = generateCorpus()

# dictionary = resultTuple['dictionary']

# corpus = resultTuple['corpus']

# socTitleDict = resultTuple['socTitleDict']

#

# num_topics = 200

# lsi = models.LsiModel(corpus, id2word=dictionary, num_topics=num_topics)

# gensimIndex = Similarity('/tmp/tst', lsi[corpus], num_features=num_topics)

# gensimIndex.num_best = 3

g.gensimIndex = gensimIndex

g.dictionary = dictionary

g.lsi = lsi

g.socTitleDict = socTitleDict

jobTitle = request.args.get('jobTitle')

jobDescription = request.args.get('jobDescription')

vec_bow = g.dictionary.doc2bow(jobDescription.lower().split())

vec_lsi = g.lsi[vec_bow]

wordsId = [word[0] for word in vec_bow]

wordsIdMap = zip(wordsId, vec_lsi)

features = map(lambda x:dictionary.get(x[0]), sorted(wordsIdMap, key=lambda x:-x[1][1]))

sims = g.gensimIndex[vec_lsi]

resultDict = {}

resultDict['mathResults'] = []

cursor = g.db.cursor()

for item in sims:

dictForOneResult = {}

socCode = g.socTitleDict[item[0]]

sql = '''

select jobtitle from SOC_JOBTITLE

where jobtitle_id = %s

''' %(socCode)

cursor.execute(sql)

row = cursor.fetchone()

dictForOneResult['socTitle'] = row[0]

dictForOneResult['score'] = item[1]

dictForOneResult['featuresList'] = " ".join(features[0:10]).replace('\'', '')

sortedVecList = sorted(vec_lsi, key=lambda x:-x[1])

top10feature = [str(round(vec[1], 5)) for vec in sortedVecList[0:10]]

dictForOneResult['featuresListScore'] = " ".join(top10feature)

dictForOneResult['socID'] = socCode

resultDict['mathResults'].append(dictForOneResult);