# linux, run the script:
    pip install deepnlp

    # linux, run the script:
    tar zxvf deepnlp-0.1.6.tar.gz
    cd deepnlp-0.1.6
    python setup.py install

    cd test
    python test_pos_en.py
    python test_segmenter.py
    python test_pos_zh.py

#coding=utf-8
from __future__ import unicode_literals # compatible with python3 unicode

#coding=utf-8
from __future__ import unicode_literals

from deepnlp import segmenter

text = "我爱吃北京烤鸭"
segwords = segmenter.seg(text)
text_seg = " ".join(segwords)

print (text.encode('utf-8'))
print (text_seg.encode('utf-8'))

#coding:utf-8
from __future__ import unicode_literals

## English Model
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(lang = 'en')  # Loading English model, lang code 'en', English Model Brown Corpus

#Segmentation
text = "I will see a funny movie"
words = text.split(" ")     # unicode
print (" ".join(words).encode('utf-8'))

#POS Tagging
tagging = tagger.predict(words)
for (w,t) in tagging:
    str = w + "/" + t
    print (str.encode('utf-8'))

## Chinese Model
from deepnlp import segmenter
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(lang = 'zh') # Loading Chinese model, lang code 'zh', China Daily Corpus

#Segmentation
text = "我爱吃北京烤鸭"
words = segmenter.seg(text) # words in unicode coding
print (" ".join(words).encode('utf-8'))

#POS Tagging
tagging = tagger.predict(words)  # input: unicode coding
for (w,t) in tagging:
    str = w + "/" + t
    print (str.encode('utf-8'))

#Results
#我/r 爱/v 吃/v 北京/ns 烤鸭/n

#coding:utf-8
from __future__ import unicode_literals

from deepnlp import segmenter
from deepnlp import ner_tagger
tagger = ner_tagger.load_model(lang = 'zh') # Loading Chinese NER model

#Segmentation
text = "我爱吃北京烤鸭"
words = segmenter.seg(text)
print (" ".join(words).encode('utf-8'))

#NER tagging
tagging = tagger.predict(words)
for (w,t) in tagging:
    str = w + "/" + t
    print (str.encode('utf-8'))

#Results
#我/nt 爱/nt 吃/nt 北京/p 烤鸭/nt

#coding:utf-8
from __future__ import unicode_literals

from deepnlp import pipeline
p = pipeline.load_model('zh')

#Segmentation
text = "我爱吃北京烤鸭"
res = p.analyze(text)

print (res[0].encode('utf-8'))
print (res[1].encode('utf-8'))
print (res[2].encode('utf-8'))

words = p.segment(text)
pos_tagging = p.tag_pos(words)
ner_tagging = p.tag_ner(words)

print (pos_tagging.encode('utf-8'))
print (ner_tagging.encode('utf-8'))

#coding:utf-8
from __future__ import unicode_literals

import deepnlp
from deepnlp import api_service
login = api_service.init()  # registration, if failed, load default login with limited access
username = login['username']
password = login['password']

import json
import requests
import sys, os
if (sys.version_info>(3,0)): from urllib.parse import quote 
else : from urllib import quote

# use your personal login
# login = {'username': 'your_user_name' , 'password': 'your_password'}
login = {} # load default login for pypi with limited access
conn = api_service.connect(login)   # save the connection with login cookies

# API Setting
base_url = 'http://www.deepnlp.org'
lang = 'zh'

#
text = ("我爱吃北京烤鸭").encode("utf-8")  # convert text from unicode to utf-8 bytes

# Pipeline
annotators = "segment,pos,ner"
url_pipeline = base_url + "/api/v1.0/pipeline/?" + "lang=" + quote(lang) + "&text=" + quote(text) + "&annotators=" + quote(annotators)
web = requests.get(url_pipeline, cookies = conn)
tuples = json.loads(web.text)
segment_str = tuples['segment_str']  # segment module
pos_str = tuples['pos_str']   # pos module
ner_str = tuples['ner_str']   # ner module
ner_json = tuples['ner_json'] # ner result in json
print ("Pipeline API")
print (segment_str)
print (pos_str)
print (ner_str)
print (ner_json)

    pip install deepnlp

    # linux, run the script:
    tar zxvf deepnlp-0.1.6.tar.gz
    cd deepnlp-0.1.6
    python setup.py install