def strip_ml_tags(in_text):
	"""Description: Removes all HTML/XML-like tags from the input text."""
	# convert in_text to a mutable object (e.g. list)
	s_list = list(in_text)
	i,j = 0,0
	
	while i < len(s_list):
		# iterate until a left-angle bracket is found
		if s_list[i] == '<':
			while s_list[i] != '>':
				# pop everything from the the left-angle bracket until the right-angle bracket
				s_list.pop(i)
				
			# pops the right-angle bracket, too
			s_list.pop(i)
		else:
			i=i+1
			
	# convert the list back into text
	join_char=u''
	return join_char.join(s_list)

def addContent(country, topic, content):
	""" Get the data formated into a dict and insert it into SQL """

	# Remove the html tags
	retext = strip_ml_tags(content)

	# Remove the &; entities
	retext = re.sub("&.*?;", "", retext)

	# Remove newlines and spaces
	retext = re.sub('((\n)+|(\t)+)', "", retext)

	# Get our records with a regular expression
	
	"""More efficent REGEXP which needs converting into python
	   AFAIK these are experimental PCRE lookaheads => '<?>'
       (Title:(?>(?P<title>.*?)URL:)(?>(?P<url>.*?)Description:)(?>(?P<description>.*?)(?=Title:|$))	"""

	fields = re.finditer(r"(Title:(?P<title>.*?)URL:(?P<url>.*?)Description:(?P<description>.*?)(?=Title:|$))", retext)

	# Helper variables for the loop
	failure = False
	count = 0
	# Group each record into dicts and push to SQL
	for field in fields:
		# FIXME: if you know a better way to count the occurences of each
		# record in our fields iterator, please change this
		count += 1 

		# Each record into SQL
		data = field.groupdict()

		# Encode into UTF-8 for the utf table
		for string in data:
			string = string.encode('utf-8')

		try:
			cursor.execute("INSERT INTO python_links(country, topic, title, url, description) VALUES(%s, %s, %s, %s, %s);", (country, topic, data['title'].encode("utf-8"), data['url'], data['description'].encode("utf-8")))
			#print query
		except:
			failure = True
			traceback.print_exc()

	if not failure:
		print "Done (%d records)" % (count