Update textify_text.py

textify_text.py

@@ -1,15 +1,156 @@
 from transformers import AutoTokenizer
 from transformers import Tool
 
+
+#####
+## https://github.com/Jcharis/textify/tree/master/textify
+## pip install textify
+####
+# Patterns
+EMAIL_REGEX = re.compile(r"[\w\.-]+@[\w\.-]+")
+PHONE_REGEX = re.compile(r"[\+\(]?[1-9][0-9 .\-\(\)]{8,}[0-9]")
+NUMBERS_REGEX = re.compile(r"\d+")
+SPECIAL_CHARACTERS_REGEX = re.compile(r"[^A-Za-z0-9 ]+")
+EMOJI_REGEX = re.compile("["
+                       u"\U0001F600-\U0001F64F"  # emoticons
+                       u"\U0001F300-\U0001F5FF"  # symbols & pictographs
+                       u"\U0001F680-\U0001F6FF"  # transport & map symbols
+                       u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
+                       u"\U00002702-\U000027B0"
+                       u"\U000024C2-\U0001F251"
+                       "]+", flags=re.UNICODE)
+
+CURRENCIES = {
+	"$": "USD",
+	"zł": "PLN",
+	"£": "GBP",
+	"¥": "JPY",
+	"฿": "THB",
+	"₡": "CRC",
+	"₦": "NGN",
+	"₩": "KRW",
+	"₪": "ILS",
+	"₫": "VND",
+	"€": "EUR",
+	"₱": "PHP",
+	"₲": "PYG",
+	"₴": "UAH",
+	"₹": "INR",
+}
+CURRENCY_REGEX = re.compile(
+	"({})+".format("|".join(re.escape(c) for c in CURRENCIES.keys()))
+)
+
+
+
+class TextCleaner(object):
+	"""TextCleaner: Class For Text Cleaning
+	usage
+	docx = TextCleaner()
+	docx.text = "this is example@gmail.com and you can reach me at +380994777888 at 5pm#"
+
+	"""
+	def __init__(self, text=None):
+		super(TextCleaner, self).__init__()
+		self.text = text
+		
+	def __repr__(self):
+		return "TextCleaner(text={})".format(self.text)
+
+	def remove_emails(self):
+		result = re.sub(EMAIL_REGEX,"",self.text)
+		return result
+	
+	def remove_phone_numbers(self):
+		result = re.sub(PHONE_REGEX,"",self.text)
+		return result
+
+	def remove_numbers(self):
+		result = re.sub(NUMBERS_REGEX,"",self.text)
+		return result
+
+	def remove_special_characters(self):
+		result = re.sub(SPECIAL_CHARACTERS_REGEX,"",self.text)
+		return result
+
+	def remove_emojis(self):
+	   result = re.sub(EMOJI_REGEX,"",self.text)
+	   return result
+
+	def replace_emails(self,replace_with="<EMAIL>"):
+		result = re.sub(EMAIL_REGEX,replace_with,self.text)
+		return result
+	
+	def replace_phone_numbers(self,replace_with="<PHONENUMBER>"):
+		result = re.sub(PHONE_REGEX,replace_with,self.text)
+		return result
+
+	def replace_numbers(self,replace_with="<NUMBER>"):
+		result = re.sub(NUMBERS_REGEX,replace_with,self.text)
+		return result
+
+	def replace_special_characters(self,replace_with="<SPECIAL_CHAR>"):
+		result = re.sub(SPECIAL_CHARACTERS_REGEX,replace_with,self.text)
+		return result
+
+
+	def clean_text(self,preserve=False):
+		if preserve == False:
+			email_result = re.sub(EMAIL_REGEX,"",self.text)
+			phone_result = re.sub(PHONE_REGEX,"",email_result)
+			number_result = re.sub(NUMBERS_REGEX,"",phone_result)
+			emoji_result = re.sub(EMOJI_REGEX,"",number_result)
+			special_char_result = re.sub(SPECIAL_CHARACTERS_REGEX,"",emoji_result)
+			final_result = special_char_result.lower()
+			
+		else:
+			special_char_result = re.sub(r'[^A-Za-z0-9@ ]+',"",self.text)
+			email_result = re.sub(EMAIL_REGEX,"<EMAIL>",special_char_result)
+			phone_result = re.sub(PHONE_REGEX,"<PHONENUMBER>",email_result)
+			number_result = re.sub(NUMBERS_REGEX,"<NUMBERS>",phone_result)
+			final_result = number_result.lower()
+			
+		return final_result
+
+
+class TextExtractor(TextCleaner):
+	"""TextExtractor - Extract emails,numbers and phone numbers from text"""
+	def __init__(self, text=None):
+		super(TextExtractor, self).__init__()
+		self.text = text
+
+	def __repr__(self):
+		return "TextExtractor(text={})".format(self.text)
+
+	def extract_emails(self):
+		match = re.findall(EMAIL_REGEX,self.text)
+		return match
+	
+	def extract_phone_numbers(self):
+		match = re.findall(PHONE_REGEX,self.text)
+		return match
+
+	def extract_numbers(self):
+		match = re.findall(NUMBERS_REGEX,self.text)
+		return match
+
+	def extract_emojis(self):
+	   match = re.findall(EMOJI_REGEX,self.text)
+	   return match
+
+        
 class TextifyTextTool(Tool):
     name = "token_counter"
-    description = "This is a tool for counting token used by a prompt. It takes a prompt as input and returns the generated text."
+    description = "This is a tool for cleaning text. It removes bad, unused characters."
     inputs = ["text"]
     outputs = ["text"]
 
     def __call__(self, prompt: str):
+        docx = TextCleaner()
+        docx.text = "your text goes here"
+        docx.clean_text()
        # token = os.environ['hf']
         tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2-dolly")
         tokens = tokenizer(prompt)["input_ids"]
-        return f"{len(tokens)}"
+        return f"{docx.text}"