def check_profanity(user_message):
debug_log('in check_profanity, user_message: ' + user_message)
user_message_words = re.compile("\W+").split(user_message)
actual_words_in_user_message = [
word for word in user_message_words if len(word) > 0
]
if len(actual_words_in_user_message) < 1:
return False
else:
debug_log('in check_profanity, user_message_words: ' +
str(actual_words_in_user_message))
overall_profanity = sum(predict(actual_words_in_user_message))
if overall_profanity > 0:
return True
debug_log('in check_profanity, overall_profanity: ' +
str(overall_profanity))
overall_profanity_average = overall_profanity / len(
actual_words_in_user_message)
debug_log('in check_profanity, overall_profanity_average: ' +
str(overall_profanity_average))
debug_log('in check_profanity, profanity probability: ' +
str(predict_prob(actual_words_in_user_message)))
debug_log('in check_profanity, profanity probability: ' + str(
sum(predict_prob(actual_words_in_user_message)) /
len(actual_words_in_user_message)))
if overall_profanity_average > 0.5:
return True
return False
async def check(self, ctx, *, message):
"""Vérifie les insultes d'un message."""
from profanity_check import predict_prob
p = predict_prob([message])
judgments = [
"est gentil",
"est cool",
"est sympathique",
"est pas fou",
"est moyen",
"est pas cool",
"est insultant",
"est vulgaire",
"est violent",
"est innacceptable",
"mérite un ban",
]
i = min(int(p * len(judgments)), len(judgments) - 1)
judgment = judgments[i]
title = f"`{int(p*100)}% vulgaire`"
r = int(255 * p)
g = int(255 * (1 - p))
color = 256**2 * r + 256 * g
color = discord.Color(color)
description = f"Ce message {judgment}!"
embed = discord.Embed(title=title,
description=description,
color=color)
await ctx.send(embed=embed)
def sent(message: str):
binPred = predict([message])
probPred = predict_prob([message])
dct = {"bin": str(binPred[0]), "prob": str(probPred[0])}
jsonData = jsonable_encoder(dct)
return JSONResponse(jsonData)
def sentence_profanity_prob(string):
""" returns the probability that a string is profanity """
try:
result = predict_prob([ string ])
return result.item()
except:
return 0.
def profanity_score_min_max(*args):
profanity_score_list_ = []
for artist in set(artists_list(*args)):
for song in artist_s_cleaned_songs_list(artist, *args):
words_of_lyrics = []
raw_text = ""
f = open(dir_given(*args) + '/Cleaned_Songs/' + song, 'rb')
for line in f.readlines():
this_line_wordlist = line.decode('utf-8').split()
for word in this_line_wordlist:
words_of_lyrics.append(word)
for word_ in words_of_lyrics:
raw_text += word_ + " "
song_lyrics_for_profanity_check_ = [raw_text]
profanity_check = predict_prob(song_lyrics_for_profanity_check_)
profanity_score = 1 - float(' '.join(map(str, profanity_check)))
profanity_score_list_.append(profanity_score)
# print(profanity_score)
min_profanity_score_list_ = round(min(profanity_score_list_), 2)
max_profanity_score_list_ = round(max(profanity_score_list_), 2)
return min_profanity_score_list_, max_profanity_score_list_
def main(argv):
min_prob = float(sys.argv[1])
ret = 0
with open(sys.argv[2], 'r') as f:
ignoreds = f.readlines()
ignoreds = [v.strip() for v in ignoreds]
print(ignoreds)
summary = ''
for fn in sys.argv[3:]:
with open(fn, 'r') as f:
lines = f.readlines()
ps = predict_prob(lines)
for i in range(len(lines)):
if ps[i] >= min_prob:
keysrc = "{}\n{}".format(fn, lines[i]).encode()
key = '{} {}'.format(
hashlib.sha256(keysrc).hexdigest(), fn)
summary += key + '\n'
if key not in ignoreds:
ret = ret + 1
print("\n❌ L{} of {}:\n{}".format(
i + 1, fn, lines[i][:-1]),
file=sys.stderr)
print("ℹ️ Add '{}' to {} to ignore this issue".format(
key, sys.argv[2]),
file=sys.stderr)
print('\n\n\nSummary to ignore everything using {}:\n{}'.format(
sys.argv[2], summary))
return ret
def main(argv):
min_prob = float(sys.argv[1])
ret = 0
with open(sys.argv[2], 'r') as f:
ignoreds = f.readlines()
ignoreds = [v.strip() for v in ignoreds]
print(ignoreds)
for fn in sys.argv[3:]:
with open(fn, 'r') as f:
lines = f.readlines()
ps = predict_prob(lines)
for i in range(len(lines)):
if ps[i] >= min_prob:
keysrc = "{}\n{}".format(fn, lines[i]).encode()
key = '{} {}'.format(
hashlib.sha256(keysrc).hexdigest(), fn)
if key not in ignoreds:
ret = ret + 1
print("\n\n\n❌ L{} of {}:\n{}".format(
i + 1, fn, lines[i]),
file=sys.stderr)
print("ℹ️ Add '{}' to {} to ignore this issue".format(
key, sys.argv[2]),
file=sys.stderr)
else:
print("\n\n\nℹ️ IGNORED: L{} of {}:\n{}".format(
i + 1, fn, lines[i]))
print(
"ℹ️ Remove '{}' from {} to stop ignoring this issue"
.format(key, sys.argv[2]))
return ret
def Make_Features( df ):
df['Freq'] = df['text'].map(df['text'].value_counts())
df['word_count'] = df['text'].swifter.apply(lambda x: len(str(x).split(" ") ) )
df['char_count'] = df['text'].str.len()
df['stpw_count'] = df['text'].swifter.apply( lambda x: len( [x for x in x.split() if x in stop] ) )
df['spchar_count'] = df['text'].swifter.apply( lambda x: len( [x for x in list(x) if x in special_char] ) )
#prepare for sentiment analysis:
# 1- remove the punctuations :
df['text_modif'] = df['text'].str.replace('[^\w\s]','')
# 2- remove stop words:
df['text_modif'] = df['text_modif'].swifter.apply(lambda x: " ".join(x for x in x.split() if x not in stop))
df['text_modif'] = df['text_modif'].swifter.apply(lambda x: " ".join([Word(word).lemmatize() for word in x.split()]))
df['sentiment'] = df['text_modif'].swifter.apply(lambda x: TextBlob(x).sentiment[0] )
# finally add profanity_check
df['profane_modf'] = predict_prob(df['text_modif'])
#print(df[['word_count','char_count','stpw_count','spchar_count', 'profane', 'sentiment']].head())
df['profane_pfilter'] = df['text'].swifter.apply(lambda x:nlp(x)._.is_profane )
# spacy to identify people organizations etc ...
df['org'] = df['text'].swifter.apply(lambda x: len( [y for y in nlp(x).ents if str(y.label_) =='ORG']))
df['Money'] = df['text'].swifter.apply(lambda x: len( [y for y in nlp(x).ents if str(y.label_) =='MONEY']))
df['tDate'] = df['text'].swifter.apply(lambda x: len( [y for y in nlp(x).ents if str(y.label_) =='DATE' or str(y.label_) =='TIME']))
df['Pers'] = df['text'].swifter.apply(lambda x: len( [y for y in nlp(x).ents if str(y.label_) =='PERSON']))
df['GPE'] = df['text'].swifter.apply(lambda x: len( [y for y in nlp(x).ents if str(y.label_) =='GPE']))
print(df.describe() )
return df
def profanity_score(song_to_be_scored, profanity_score_min,
profanity_score_max, *args):
words_of_lyrics_of_song_to_be_scored = []
raw_text = ""
f = open(dir_given(*args) + '/Cleaned_Songs/' + song_to_be_scored, 'rb')
for line in f.readlines():
this_line_wordlist = line.decode('utf-8').split()
for word in this_line_wordlist:
words_of_lyrics_of_song_to_be_scored.append(word)
for word_ in words_of_lyrics_of_song_to_be_scored:
raw_text += word_ + " "
song_lyrics_for_profanity_check_of_song_to_be_scored = [raw_text]
profanity_check = predict_prob(
song_lyrics_for_profanity_check_of_song_to_be_scored)
profanity_score_of_song_to_be_scored = 1 - \
float(' '.join(map(str, profanity_check)))
regularization_step = (profanity_score_of_song_to_be_scored -
profanity_score_min) / (profanity_score_max -
profanity_score_min)
profanity_score_of_song_to_be_scored_regularized = 1 * \
regularization_step + 0*(1-regularization_step)
return round(profanity_score_of_song_to_be_scored_regularized, 2)
def _check_for_profanity(self, message):
"""
Checks message content to see if it contains a blacklisted word
:param message: content to be checked against blacklist
:return True if content is flagged, False otherwise
"""
return predict_prob(message)[0] > THRESHOLD
def censor(word):
new_words_list=''
for i,item in enumerate(word.split()):
if predict_prob([item])>=0.3 or item in chkchk(word):#(e for e in badwords['word'] if e == item ):#any(badword in item for badword in badwords_lst):
item = item[0]+'*'*len(item[1:])
new_words_list += item+" "
return new_words_list
def get_prob(message):
'''
Parameter (Discord Message): the discord message the bot has recieved
Returns (float): a float value represent the probability profanity is the message
'''
#use .content to get the string in the Discord message and the wrap it in a list
#get the probability from the first value in the numpy array then convert it into a native python float using .item()
return predict_prob([message.content])[0].item()
def profanityCheck(text):
"""
Initial profanity check using profanity_check
:param: text The text to analyse
:param: keyNum The key number to use
:param: lang The language of the text
:return: The probability of the text containing profanity
"""
return predict_prob([text])[0]
async def process_message(self, message):
"""Checks a message for profanity. Currently does nothing."""
if message.author != self.bot.user:
prof_percent = predict_prob([message.clean_content])
if prof_percent[0] >= 0.75:
await message.add_reaction('😡'
) # Pretty strong mute candidate
elif prof_percent[0] >= 0.5:
await message.add_reaction('😠')
def doc_calc(self, article):
"""Helper code to compute average word length of a name"""
flesch_ease = textstat.flesch_reading_ease(article)
flesch_grade = textstat.flesch_kincaid_grade(article)
gunning = textstat.gunning_fog(article)
profanity = predict_prob([article])[0]
polarity = TextBlob(article).sentiment.polarity
return pd.Series(
[flesch_ease, flesch_grade, gunning, profanity, polarity])
async def read_root(query: str):
prediction = predict_prob([query])[0]
leetSp33k = profanity.contains_profanity(query)
if prediction > .5:
return True
else:
if leetSp33k:
return True
else:
return False
async def rating(request):
key = request.match_info.get("key", "")
if key not in ALLOWED_API_KEYS:
return web.Response(status=401)
data = request.match_info.get("b64", "")
data = b64decode(data)
return web.Response(body=str(predict_prob([data])[0]).encode("utf-8"))
async def on_message(self, message):
if message.channel.id != channels["system"]:
swearing = any(predict([message.content]))
if swearing:
probability = round(predict_prob([message.content])[0],
2) * 100
if probability > 85:
await message.guild.get_channel(channels["system"]).send(
f'{message.author.mention} swore in {message.channel.mention}: "{message.content}" ({probability}%)'
)
def offensiveness(sentence):
"""
Compute and return the probability that the given sentence is offensive.
Args:
sentence: The sentence to check
Returns: The probability that the given sentence is offensive as a float p (1 = offensive, 0 = nice, 0 <= p <= 1)
"""
profane_prob = predict_prob([sentence])
return profane_prob[0]
async def action(self, channel, sender, message):
quick_chats = [
"OMG!", "Wow!", "Okay.", "Savage!", "Thanks!", "Holy cow!"
]
profanity = predict_prob([message])
if profanity[0] > self.profanity_threshold and random.random(
) < self.random_response_chance:
say = Message().set_target(channel)
say.add_field(name="", value=random.choice(quick_chats))
await self.connector.send_message(say)
async def on_message(message):
if message.author == dscClient.user:
return
print(str(predict_prob([message.content])[0]) + " " + str(message.guild))
if predict_prob([message.content
])[0] > .79 or messageContainsTriggerWord(message):
await deleteBlacklistedMessage(message)
if message.channel.name == "catras-diary":
archiveChannel = dscClient.get_channel(738415449582075924)
await archiveChannel.send(message.author.name + ": " + message.content)
time.sleep(calculateDelayTime(message.content))
try:
await message.delete()
except discord.errors.NotFound:
await log(
"Someone deleted a message before me. Sneaky. System may reboot, this is normal behavior."
)
elif message.content.lower().startswith(
"lighthope") or message.content.lower().endswith("lighthope"):
response = witClient.message(msg=message.content)
await handle_message(response, message.channel)
def profanityAnalysis(text_content):
start_index = 0
text_content_arr = text_content.split()
end_index = len(text_content_arr)
profanity_baseline = predict_prob([text_content
])[0] / (end_index - start_index)
profanity_split_1 = predict_prob([
' '.join(text_content_arr[start_index:min(end_index // 2 +
1, end_index - 1)])
])[0] / (min(end_index // 2 + 1, end_index - 1) - start_index)
profanity_split_2 = predict_prob([
' '.join(text_content_arr[max(1, end_index // 2 - 1):end_index])
])[0] / (end_index - max(1, end_index // 2 - 1))
while ((end_index - start_index) > 1
and (profanity_baseline <= profanity_split_1
or profanity_baseline <= profanity_split_2)):
if profanity_split_1 >= profanity_split_2:
profanity_baseline = profanity_split_1
start_index = start_index
end_index = min((start_index + end_index) // 2, end_index - 1)
else:
profanity_baseline = profanity_split_1
start_index = max(1, (start_index + end_index) // 2)
end_index = end_index
profanity_split_1 = predict_prob([
' '.join(text_content_arr[start_index:min(
(start_index + end_index) // 2, end_index - 1)])
])[0] / max(1, (min(
(start_index + end_index) // 2, end_index - 1) - start_index))
profanity_split_2 = predict_prob([
' '.join(text_content_arr[max(1, (start_index + end_index) //
2):end_index])
])[0] / max(1, (end_index - max(1, (start_index + end_index) // 2)))
return (start_index, end_index)
def feature_profanity(dataset_filename_pkl):
feature_filename = 'features/' + os.path.basename(
dataset_filename_pkl)[:-4] + '_feature_profanity.pkl'
if not os.path.isfile(feature_filename):
author_data = utils.load_feature(dataset_filename_pkl)
author_profanity = {}
for author in tqdm(author_data):
single_text = ''.join(author_data[author])
profanity_rate = predict_prob([single_text])
author_profanity[author] = profanity_rate[0]
profanity_file = open(feature_filename, 'wb')
pickle.dump(author_profanity, profanity_file)
profanity_file.close()
return feature_filename
def censoring(input_list):
'''
INPUT:
input_list : (list of sentences) list of sentences want to check
OUTPUT:
predictions : (list of booleans) list consists of true/false if ther sentence includes offensiveness or profanity
'''
threshold = 0.15
predictions = []
predictions = [prob>threshold for prob in predict_prob(input_list)]
return predictions
def censor_profanity(comment_text, profanity_threshold=0.9):
"""
Replaces profanity using a (probably) inefficient use of the alt-profanity-filter.
Input: comment_text (str)
Output: comment text with profane words censored
"""
repl_text = [
(x, f"{x[0]}{''.join(['*' for x in range(len(x)-1)])}")
for x in comment_text.split(' ')
if predict_prob([x])[0] > profanity_threshold
]
comment_text_censored = comment_text
for text in repl_text:
comment_text_censored = comment_text_censored.replace(text[0], text[1])
return comment_text_censored
def get_profanity(txt, prob=False):
if not isinstance(txt, str):
raise Exception('txt has to be a string')
if len(txt.strip()) == 0:
return False
blob = TextBlob(txt)
lang = blob.detect_language()
result = None
# if blob.detect_language() != 'en':
# raise Exception('Only english text can be verified for profanity!')
if lang == 'en':
if not prob:
result = bool(predict([txt])[0] == 1)
else:
result = predict_prob([txt])[0]
# aparent egalitatea urmatoarea nu intoarce python bool si trebuie sa convertim la python bool
return {'result': result, 'lang': lang}
def test_accuracy():
texts = [
'Hello there, how are you',
'Lorem Ipsum is simply dummy text of the printing and typesetting industry.',
'!!!! Click this now!!! -> https://example.com',
'f**k you',
'f**K u',
'GO TO hElL, you dirty scum',
]
assert list(predict(texts)) == [0, 0, 0, 1, 1, 1]
probs = predict_prob(texts)
for i in range(len(probs)):
if i < 3:
assert probs[i] <= 0.5
else:
assert probs[i] >= 0.5
def add_tweet():
user = request.json['user']
description = request.json['description']
private = request.json['private']
pic = request.json['pic']
number = predict_prob([description])
if number <= 0.5:
tweet = dict(user=user,
description=description,
private=private,
upvote=0,
date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
pic=pic,
_id=str(ObjectId()))
tweets[tweet['_id']] = tweet
if push_to_redis:
rjjsonsetwrapper('ttwi-' + ssm() + '-' + tweet['_id'],
Path.rootPath(), tweet)
return jsonify(tweet)
async def on_message(message):
# SAO Easter Egg
punctuations = '!()-[]{};:\'"\\,<>./?@#$%^&*_~'
# remove punctuation from the string
msg = ""
for char in message.content.lower():
if char not in punctuations:
msg = msg + char
# profanity check
prob = predict_prob([msg])
if prob >= 0.8:
em = discord.Embed(title=f"AI Analysis Results", color=0xC54B4F)
em.add_field(name='PROFANITY DETECTED! ', value=str(prob[0]))
await message.channel.send(embed=em)
if msg.startswith("system call "):
content = msg[12:].split(" ")
if content[0].lower() == "inspect":
if content[1].lower() == "entire":
if content[2].lower() == "command":
if content[3].lower() == "list":
em = discord.Embed(title=f"🍢 SAO Command List", color=0x7400FF)
em.set_thumbnail(
url="https://cdn.discordapp.com/attachments/668816286784159763/674285661510959105/Kirito-Sao-Logo-1506655414__76221.1550241566.png")
em.add_field(name='Commands',
value="generate xx element\ngenerate xx element xx shape\ninspect entire command list")
em.set_footer(text=f"{teapot.copyright()} | Code licensed under the MIT License")
await message.channel.send(embed=em)
elif content[0].lower() == "generate":
if content[-1].lower() == "element":
em = discord.Embed(title=f"✏ Generated {content[1].lower()} element!",
color=0xFF0000)
await message.channel.send(embed=em)
if content[-1].lower() == "shape":
if content[2].lower() == "element":
em = discord.Embed(
title=f"✏ Generated {content[-2].lower()} shaped {content[1].lower()} element!",
color=0xFF0000)
await message.channel.send(embed=em)
await bot.process_commands(message)
def api_offensive():
filepath = 'C:/Users/dipta/OneDrive/Documents/MCS/CS 410/CourseProject/file-read-api/search.txt'
response = []
with open(filepath, errors='ignore') as f:
for line in f:
profanity = []
profanity.append(line);
det_val = predict(profanity)
prob_val = predict_prob(profanity)
val_list = list(det_val)
prob_list = list(prob_val)
prediction = int(val_list[0])
prediction_prob = float(prob_list[0])
data = {}
data['tweet'] = line
data['prediction'] = prediction
data['prob'] = prediction_prob
response.append(data)
return json.dumps(response)
