fix 儂->わし bug

README.md

@@ -1,32 +1,24 @@
-# Pseudo Chinese
+# Pseudo Chinese (with MeCab)
 Convert Japanese to pseudo-Chinese.
 
 ## Description
 This tool will automatically generate fake Chinese from Japanese sentences.
 
-## Demo
-私は本日定時退社します -> 我本日定時退社也
+Using MeCab to parse and word-tag Japanese sentences instead of COTOHA API.
 
-私はお酒を飲みたい -> 我飲酒希望
+## Demo
+私は本日定時退社します -> 我本日定時退社
+
+私はお酒を飲みたい -> 我御酒飲欲
 
 ## Requirement
 - Python 3.5.1
-- [COTOHA API](https://api.ce-cotoha.com/contents/index.html)
-
-You need to register for a COTOHA API account before you can run this tool.
-
-Once you have registered your COTOHA API account, you will set your Client ID and Client Secret to `env.json` .
-
-```json
-{
-	"client_id": "yourclinetid",
-	"client_secret": "yourclinetsecret"
-}
-```
+- mecab-python3
+- unidic-lite
 
 ## Usage
 ```
-$ python -u pseudo-chinese.py
+$ python -u pseudo-chinese.py [sentence]
 ```
 
 ## Contribution
@@ -42,4 +34,5 @@ MIT
 
 ## Author
 
-[Shoichiro Kono](https://github.com/k2font)
+- [Shoichiro Kono](https://github.com/k2font) (orig. creater)
+- [Tan Kian-ting](https://github.com/yoxem) (porting to MeCab, and modified it.)

env.json

@@ -1,4 +0,0 @@
-{
-	"client_id": "<Client ID>",
-	"client_secret": "<Client Secret>"
-}

pseudo-chinese.py

@@ -1,104 +1,135 @@
-import requests
-import json
+import functools
+import MeCab
 import sys
+import re
 
-BASE_URL = "https://api.ce-cotoha.com/api/dev/nlp/"
-
-# アクセストークンを取得する関数
-# Function to get the access token.
-# 获取访问令牌的函数
-def auth(client_id, client_secret):
-	token_url = "https://api.ce-cotoha.com/v1/oauth/accesstokens"
-	headers = {
-		"Content-Type": "application/json",
-		"charset": "UTF-8"
-	}
-
-	data = {
-		"grantType": "client_credentials",
-		"clientId": client_id,
-		"clientSecret": client_secret
-	}
-
-	r = requests.post(token_url,headers=headers,data=json.dumps(data))
-
-	return r.json()["access_token"]
 
 # 形態素解析する関数
 # Function for morphological analysis.
 # 形态学分析功能
-def parse(sentence, access_token):
-	base_url = BASE_URL
+def parse(sentence):
+	mecab_tagger = MeCab.Tagger()
+	raw_result = mecab_tagger.parse(sentence).split('\n')
+	result = []
+	for i in raw_result[:-2]:
+		j = i.split('\t')
+		item = dict()
+		item['form'] = j[0] # 食べ
+		#print(j)
+		if len(j) > 1:
+			item['lemma'] = j[3] # 食べる
+			item['pos'] = j[4] #　動詞-一般
+			item['features'] = j[6] # 連用形-一般
+		else:
+			item['lemma'] = j[0]
+			item["pos"] = ""
+			item["features"] = ""
 		
-	headers = {
-		"Content-Type": "application/json",
-		"charset": "UTF-8",
-		"Authorization": "Bearer {}".format(access_token)
-	}
+		result.append(item)
+	return result
 
-	data = {
-		"sentence": sentence,
-		"type": "default"
-	}
 
-	r = requests.post(base_url + "v1/parse",headers=headers,data=json.dumps(data))
-	return r.json()
+def is_hira(string):
+	if isinstance(string, str):
+		string = list(string)
+	if len(string) == 0:
+		return False
+	elif len(string) == 1:
+		return (("ぁ" <= string[0]) and (string[0] <= "ん"))
+	if len(string) > 1:
+		return functools.reduce((lambda x, y: (is_hira(x) and is_hira(y))) , string)
+
+def contain_kanji(str):
+	if len(str) == 0:
+		return False
+	elif len(str) == 1:
+		return re.match(r"[一-龯]", str)
+	if len(str) > 1:
+		return functools.reduce(lambda x, y: contain_kanji(x) or contain_kanji(y) , str)	
+
 
 # ひらがなを削除する関数
 # Function to delete hiragana.
 # 删除平假名的功能
 def hira_to_blank(str):
-  return "".join(["" if ("ぁ" <= ch <= "ん") else ch for ch in str])
+	return "".join(["" if ("ぁ" <= ch <= "ん") else ch for ch in str])
 
 if __name__ == "__main__":
-	envjson = open('env.json', 'r')
-	json_load = json.load(envjson)
-	CLIENT_ID = json_load["client_id"]
-	CLIENT_SECRET = json_load["client_secret"]
+	
 
 	document = "私は明日、伊豆大島に行きたい"
 	args = sys.argv
 	if len(args) >= 2:
 		document = str(args[1])
 
-	access_token = auth(CLIENT_ID, CLIENT_SECRET)
-	parse_document = parse(document, access_token)
-	print(parse_document)
+	parse_document = parse(document)
+	#print(parse_document)
 	result_list = list()
-	for chunks in parse_document['result']:
-		for token in chunks["tokens"]:
-			# 形態素解析結果に置き換えルールを適用する
-			if (token["pos"] != "連用助詞" 
-			and token["pos"] != "引用助詞" 
-			and token["pos"] != "終助詞" 
-			and token["pos"] != "接続接尾辞" 
-			and token["pos"] != "動詞活用語尾"):
-				if token["pos"] == "動詞接尾辞" and '終止' in token["features"]:
-					if ("する" in token["lemma"]) or ("ます" in token["lemma"]):
-						prime = "也"
-					elif "たい" in token["lemma"]:
-						prime = "希望"
-					elif token['lemma'] != 'ない':
-						prime = "了"
-					else:
-						prime = "実行"
+	
+	for i, token in enumerate(parse_document):
+
+		# 形態素解析結果に置き換えルールを適用する
+		if (token["pos"] != "助詞-格助詞" 
+			and token["pos"] != "助詞-接続助詞" 
+			and token["pos"] != "助詞-終助詞" 
+			and token["pos"] != "助詞-接続助詞" ):
+			if '終止形-一般' in token["features"]:
+				if ("為る" in token["lemma"]) or ("ます" in token["lemma"]):
+					prime = "" # don't translate it. 
+				elif "たい" in token["lemma"]:
+					prime = "欲"
+				elif token["lemma"] in ["ない", "無い"]:
+					prime = "無"
+				elif token['lemma'] == 'た':
+					prime = "了"
 				else:
-					prime = token["form"]
+					print(is_hira(token['lemma']))
+					if is_hira(token['lemma']):
 						
-				if token['lemma'] == '私':
-					prime = '我'
+						prime = token["form"]
+					else:
+						prime = token["lemma"]
+			else:
 
-				if (token['lemma'] == '君' or token['lemma'] == 'あなた' or token['lemma'] == 'お前'):
-					prime = '你'
+				if is_hira(token["lemma"]) and contain_kanji(token["form"]):
+					prime=token["form"]
+				else:
+					prime = token["lemma"]
 
-				if len(token["features"]) != 0:
-					if "SURU" in token["features"][0] :
-						prime = "実行"
-					elif "連体" in token['features'][0]:
-						prime = "的"
-					elif "疑問符" in token["features"][0]:
-						prime = "如何?"
 
-				result_list.append(hira_to_blank(prime))
+			if (token['lemma'] == '君' or token['lemma'] == '貴方' or token['lemma'] == 'お前'):
+				prime = '你'
 
-	print(''.join(result_list))
+			if token['lemma'] == '為る' and parse_document[i-1]['pos'] == '名詞-普通名詞-サ変可能':
+				prime = ''
+
+
+			compound_matched = re.match("([^-]+)-([^-]+)", token['lemma'])
+			if compound_matched:
+				prime = compound_matched.group(1)
+
+			if token['lemma'] == '私-代名詞':
+				prime = '我'
+
+			if len(token["features"]) != 0:
+				if "連体形-一般" in token['features']:
+					if token['lemma'] == 'ない':
+						prime = "無之"
+					else:
+						prime = prime + "之"
+			
+			
+
+
+			result_list.append(hira_to_blank(prime))
+
+		
+
+		if token['lemma'] == 'の' and token['pos'] == "助詞-格助詞":
+			prime = "之"
+			result_list.append(hira_to_blank(prime))
+		if token["form"] == "か" and token['pos'] == '助詞-終助詞':
+			prime = "乎"
+			result_list.append(hira_to_blank(prime))
+
+print(''.join(result_list))