def dictonarySynonums(self, word):
synJSON = vb.synonym(word)
if synJSON != False:
synonyms_lists = [
dictSyno["text"].encode('ascii', 'ignore')
for dictSyno in json.loads(vb.synonym(word))
]
return set(synonyms_lists)
else:
return set([])
def labels(self, query):
""" return parsed result """
res = []
meanings = vb.meaning(query, format="list")
if meanings:
meanings = [
e.replace("<i>",
"").replace("</i>",
"").replace("[i]",
"").replace("[/i]", "")
for e in meanings
]
for e in meanings:
res.append((query, "meaning", e))
synonyms = vb.synonym(query, format="list")
if synonyms:
for e in synonyms:
res.append((query, "synonym", e))
antonyms = vb.antonym(query, format="list")
if antonyms:
for e in antonyms:
res.append((query, "antonym", e))
#ps = vb.part_of_speech(query, format="list")
#if ps:
# for e in ps:
# res.append((query, "part_of_speech", e))
examples = vb.usage_example(query, format="list")
if examples:
for e in examples:
res.append((query, "usage_example", e))
return res
def test_synonynm_found(self, mock_api_call):
res = {
"tuc": [{
"phrase": {
"text": "get angry",
"language": "en"
}
}, {
"phrase": {
"text": "mad",
"language": "en"
},
}]
}
mock_api_call.return_value = mock.Mock()
mock_api_call.return_value.status_code = 200
mock_api_call.return_value.json.return_value = res
expected_result = '[{"text": "get angry", "seq": 0}, {"text": "mad", "seq": 1}]'
expected_result = json.dumps(json.loads(expected_result))
result = vb.synonym("angry")
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(expected_result, result)
else:
self.assertCountEqual(expected_result, result)
def query(self, query):
""" return raw result (dict) """
cons = {
"meaning": [],
"synonym": [],
"antonym": [],
"usage_example": [],
"part of speech": []
}
meanings = vb.meaning(query, format="list")
if meanings:
cons["meaning"] = [
e.replace("<i>",
"").replace("</i>",
"").replace("[i]",
"").replace("[/i]", "")
for e in meanings
]
synonyms = vb.synonym(query, format="list")
if synonyms:
cons["synonym"] = synonyms
antonyms = vb.antonym(query, format="list")
if antonyms:
cons["antonym"] = antonyms
ps = vb.part_of_speech(query, format="list")
if ps:
cons["part of speech"] = ps
examples = vb.usage_example(query, format="list")
if examples:
cons["usage_example"] = [
e.replace("[", "").replace("]", "") for e in examples
]
return cons
def test_synonynm_empty_list(self, mock_api_call):
res = {"result": "ok", "tuc": [], "phrase": "angry"}
mock_api_call.return_value = mock.Mock()
mock_api_call.return_value.status_code = 200
mock_api_call.return_value.json.return_value = res
self.assertFalse(vb.synonym("angry"))
def synonym(self, word):
res = 'SYNONYMS: '
synonyms = vb.synonym(word, format="list")
if not synonyms:
return False
res += synonyms[0]
res += self.more(synonyms[1:])
res += self.addLine()
return res
def get_syn(x):
word = x.split()
syns = []
for ii in range(0, len(word)):
s = vb.synonym(word[ii], format='list')
if s:
if s[0] not in syns:
syns.append(s[0])
return syns
def parse_keyword_synonym(self, tokens):
synonyms = []
for token in tokens:
try:
data = json.loads(vb.synonym(token))[0:self.synonym_limit]
for word_txt in data:
synonyms.append(word_txt["text"])
except:
continue
return synonyms
def synonym(text, lang='en-en'):
# do not support russian
src, dest = lang.split('-')
response = voc.synonym(text, src, dest)
if not response or response == '[]':
return ''
response = json.loads(response)
output = ""
for i, a in enumerate(response, start=1):
output += '\t' + str(i) + '. ' + a['text'] + '\n'
return output
def synonym(word):
syn = vb.synonym(word)
if (syn == False):
return "No Synonyms Founds"
syns = json.loads(syn)
ret = "Synonyms are "
i = 0
while (i < 4 and i < len(syns)):
ret += syns[i]['text'] + ", "
i += 1
ret = ret[:-2]
return ret
def generate_synonym_list(query):
"""
Creates a list of synonym suggestions for important words in the query
"""
synonym_list = []
for word in query.split():
syn = vb.synonym(word)
if syn:
syn = json.loads(syn)
for synonym in syn:
# Generate all the synonyms
synonym_list.append(
("Replace %s with %s" % (word, synonym['text']),
word + " " + synonym['text']))
return synonym_list
def wordApplications(mess):
if mess.find("soph") == 0 and ("define" in mess or "definition" in mess
or "meaning"
in mess) or mess.find("define") == 0:
word = getWord(mess)
if word == None:
return
else:
result = str(d.meaning(word))
resultF = meaningParser(result)
return ("***" + str(word).upper() + "*** meaning(s)" +
str(resultF))
elif mess.find("soph") == 0 and ("antonym" in mess or "opposite" in mess
or "different" in mess and "word"
in mess) or mess.find("antonym") == 0:
word = getWord(mess)
if word == None:
return
else:
result = str(v.antonym(word))
resultF = parser(result)
if resultF == None or resultF == "1. ":
return ("Couldn't find any :(")
return ("**" + str(word) + "** antonym(s)\n" + str(resultF))
elif mess.find("soph") == 0 and ("synonym" in mess
or "similar to" in mess and "word"
in mess) or mess.find("synonym") == 0:
word = getWord(mess)
if word == None:
return
else:
result = str(v.synonym(word))
resultF = parser(result)
if resultF == None or resultF == "1. ":
return ("Couldn't find any :(")
return ("**" + str(word) + "** synonym(s)\n" + str(resultF))
mess = mess[:mess.find("d") + 1]
revmess = mess[::-1]
if revmess.find("drow") == 0 and mess.find(
"soph") == 0 and " is " in mess and " a " in mess:
word = mess[mess.find(" is ") + 4:mess.find(" a ")]
if d.meaning(word) == None:
return ("Well, it's not in my dictionary... :(")
else:
return ("Yeah, it is! :D")
def get_dictionary(subject):
cons = {"meaning": [], "synonym": [], "antonym": [], "example": [], "part of speech": []}
meanings = vb.meaning(subject, format="list")
if meanings:
cons["meaning"] = [e.replace("<i>", "").replace("</i>", "").replace("[i]", "") .replace("[/i]", "") for e in meanings]
synonyms = vb.synonym(subject, format="list")
if synonyms:
cons["synonym"] = synonyms
antonyms = vb.antonym(subject, format="list")
if antonyms:
cons["antonym"] = antonyms
ps = vb.part_of_speech(subject, format="list")
if ps:
cons["part of speech"] = ps
examples = vb.usage_example(subject, format="list")
if examples:
cons["example"] = [e.replace("[", "").replace("]", "") for e in examples]
return cons
def modify(text: str) -> Tuple[str, str]:
stopwords = get_stopwords()
re_word = re.compile(r'^\w+$')
word_counter = collections.Counter(
w for w in text.lower().split()
if w not in stopwords and re_word.match(w))
freq_words = word_counter.most_common(5)
modified_text = text
for word, _ in freq_words:
try:
syns = json.loads(Vocabulary.synonym(word))
except TypeError as e:
print(f'Cannot find synonym for "{word}" ({e})')
continue
repl = syns[0]['text']
modified_text = re.sub(word,
repl,
modified_text,
flags=re.I | re.M | re.S)
return modified_text
def details(word):
meaning = vb.meaning(word)
antonym = vb.antonym(word)
synonym = vb.synonym(word)
usage = vb.usage_example(word)
if meaning == False:
meaning = 'Not Found'
else:
meaning = json.loads(meaning)
# meaning = str(meaning[0]['text'])
meaning = [unescape(meaning[i]['text']) for i in range(len(meaning))]
if antonym == False:
antonym = 'Not Found'
else:
antonym = json.loads(antonym)
antonym = str(antonym[0]['text'])
if synonym == False:
synonym = 'Not Found'
else:
synonym = json.loads(synonym)
synonym = str(synonym[0]['text'])
if usage == False:
usage = 'Not Found'
else:
usage = json.loads(usage)
usage = str(usage[-1]['text'])
values = {
'meaning': meaning,
'antonym': antonym,
'synonym': synonym,
'usage': usage
}
return values
def getSynonyms(word):
syns = set()
result = vb.synonym(word)
if isinstance(result, list):
result = json.loads(result)
syns.update([synonym['text'] for synonym in result])
result = []
for syn in wordnet.synsets(word):
for l in syn.lemmas():
syns.add(l.name())
w = Word(word)
syns.update(w.synonyms())
if not syns:
apiKey = os.getenv('BIG_HUGE')
result = pyhugeconnector.thesaurus_entry(word=word,
api_key=apiKey,
pos_tag='n',
ngram=2,
relationship_type='syn')
if isinstance(result, list):
syns.update(result)
return syns
def simplify(self):
print(self.words)
simplified = []
simplified2 = []
simplified3 = []
for word in self.model:
print("Word: ", word)
shortest_syn = ""
# synonyms = list(chain.from_iterable([word.lemma_names() for word in wordnet.synsets(word[0])]))
synonyms_json = vb.synonym(word[0]) or []
synonyms = [s["text"] for s in json.loads(synonyms_json)]
if synonyms == [] or word[0].lower() in ' '.join(self.common):
print("Either no synonym found or too common:", word)
simplified.append(word[0])
simplified2.append(word[0])
simplified3.append(word[0])
continue
print(word[0] + "-->" + word[1])
print(synonyms)
best_synonym = sorted(synonyms, key=textstat.gunning_fog)[0]
print(best_synonym)
if "V" in word[1]:
print("Cast: " + self.cast_base_verb(best_synonym, word[1]))
simplified2.append(self.cast_base_verb(best_synonym, word[1]))
else:
print("Not a verb")
simplified2.append(best_synonym)
for definition in [
e for e in synonyms if e in ' '.join(self.common)
]:
if len(definition) < len(shortest_syn) or shortest_syn == "":
shortest_syn = definition
print("Selected definition: " + str(shortest_syn))
simplified.append(shortest_syn)
simplest = ""
simplest_score = 100000000
for definition in [
e for e in synonyms if e in ' '.join(self.common)
]:
if textstat.gunning_fog(definition) < simplest_score:
simplest = definition
simplest_score = textstat.gunning_fog(definition)
print(simplest)
if "V" in word[1]:
print("Cast: " + self.cast_base_verb(simplest, word[1]))
simplified3.append(self.cast_base_verb(simplest, word[1]))
else:
print("Not a verb")
simplified3.append(simplest)
simp = ""
for s in simplified:
simp = simp + s
# if s in ",.!;:?":
simp = simp + " "
simp2 = ""
for s in simplified2:
simp2 = simp2 + s
# if s in ",.!;:?":
simp2 = simp2 + " "
simp3 = ""
for s in simplified3:
simp3 = simp3 + s
# if s in ",.!;:?":
simp3 = simp3 + " "
return [simp, simp2, simp3]
def test_synonynm_not_found(self, mock_api_call):
mock_api_call.return_value = mock.Mock()
mock_api_call.return_value.status_code = 404
self.assertFalse(vb.synonym("angry"))
def obtain_result(named_entity, query_properties):
"""
an array to store property codes for wikidata query,
as wikidata needs code not natural language for SPARQL query
"""
property_code = []
# if there are any query properties then only search for it's code
if len(query_properties) != 0:
# loading properties with it's code from property.json file which is property dump of wikidata
properties = open('property.json', 'r')
properties = json.load(properties)
print(type(properties))
# extracting the first query from the query properties list
noun = query_properties[0]
"""
finding the synonyms of the queried property using a library called vocabulary
so that it matches the property from wikidata
E.g. wikidata doesn't have a property wife, but it has a property spouse, so it'll
consider the property code corresponding to spouse property
"""
noun_synonyms = vb.synonym(noun, format="dict")
# checking if the query itself is a property in the property.json file
for p, prop in properties.items():
if prop == noun:
property_code.append(p)
break
# if the query is not found as a property in property.json file, then search in it's synonyms
if len(property_code) == 0:
# dict.items() extracts key and value from a dictionary
for p, prop in properties.items():
# vocabulary returns bool if there isn't any synonym for a particular word
if type(noun_synonyms) != bool:
# dict.itervalues basically iterate through the values of the dictionary
for synonym in noun_synonyms.itervalues():
if prop == synonym:
property_code.append(p)
break
print(property_code)
# if our NLP program fails to detect the named entity (subject) from the query sentence, then flag an error response
if len(named_entity) != 0:
"""
if there is any query for properties then use wikidata for that
E.g :- Who is the wife of Shahrukh Khan? Here, "wife" is the query property and "Shahrukh Khan" is the subject
"""
if len(property_code) != 0:
flag = 0
sparql = SPARQLWrapper("https://query.wikidata.org/sparql")
query = """SELECT ?label ?property ?thumbnail WHERE
{
?entity rdfs:label ?label .
?entity wdt:""" + property_code[0] + """ ?property_id .
?property_id rdfs:label ?property .
OPTIONAL { ?property_id wdt:P18 ?thumbnail .}
FILTER (STR(?label) = '""" + named_entity[0] + """') .
FILTER (LANG(?property) = "en")
}"""
print query
else:
"""
if there is no query for any property then use dbpedia for extracting the abstract of that entity
E.g : Who is Shahrukh Khan? Note: There is no query property, but there is a subject
"""
flag = 1
sparql = SPARQLWrapper("https://dbpedia.org/sparql")
query = """SELECT ?label ?description ?thumbnail WHERE
{
?entity rdfs:label ?label .
?entity dbo:abstract ?description .
?entity dbo:thumbnail ?thumbnail .
FILTER (STR(?label) = '""" + named_entity[0] + """' && LANG(?description) = "en") .
}
LIMIT 1"""
print query
sparql.setQuery(query)
sparql.setReturnFormat(JSON)
global result
result = list()
temp = str()
try:
results = sparql.query().convert()
print(results)
# parsing the result obtained from SPARQLquery
if flag == 0:
# parsing for wikidata
for data in results["results"]["bindings"]:
result.append(data["property"]["value"])
# if the thumbnail do not exist for an entity
if "thumbnail" in data and data["thumbnail"]["value"] != "":
temp = data["thumbnail"]["value"]
else:
# parsing for dbpedia
for data in results["results"]["bindings"]:
result.append(data["description"]["value"])
# if the thumbnail do not exist for an entity
if "thumbnail" in data and data["thumbnail"]["value"] != "":
temp = data["thumbnail"]["value"]
# removing redundant data from the result set, by using set function and then converting it to a list
result = list(set(result))
response = []
for i in result:
print(i)
# encoding required to remove the 'u' character from the list elements, by deafult there in Python list
response.append(u''.join(i).encode('utf-8'))
print(response)
# Response sent as result status, the data and the thumbnail for that data
if flag == 0:
data = {"status": "200", "data": response, "thumbnail": temp}
else:
data = {"status": "200", "data": response, "thumbnail": temp}
result = json.dumps(data)
print(results)
except:
response = ["Unable to retrieve data"]
data = {"status": "500", "data": response}
result = json.dumps(data)
raise
else:
response = ["Unable to retrieve data"]
data = {"status": "500", "data": response}
result = json.dumps(data)
print(result)
return result
class HQTrivia():
#initialization
def __init__(self):
# QuickTime - MacOS has record feature for phone (best)
self.use_quicktime = False
self.use_input = False
# the filename of the image (no extension = capturing image)
self.picture = 'source'
# location of where to work on self.picture
self.location = os.getcwd()
# Replace with your own auth file name
self.google_auth_json = 'HQproject-a1a4e25e4b45.json'
# wikipedia setting (english)
self.wiki = wikipediaapi.Wikipedia('en')
self.vb = Vocabulary()
# The OCR text (directly converted from image)
self.raw = ''
# processed texts
self.question = ''
self.question_nouns = ''
self.answers = {}
self.lookup_info = {}
# For debugging
self.times = {}
self.verbose = False
def debug(self, msg):
# in multiprocessing environments, following line helps
sys.stdout.flush()
print("hqtrivia-automation.py: " + str(msg))
def capture(self, ftype='tiff'):
# function to selection function to capture picture
if self.verbose:
pre = "[DEBUG] in capture() | "
self.debug(pre + "choosing how to capture...")
if self.use_input:
if self.verbose:
self.debug(pre + "using user input")
return
# add extension name as 'tiff'
self.picture += '.' + ftype
if self.use_quicktime:
if self.verbose:
self.debug(pre + "quicktime")
#call scan_quicktime function (take screenshot)
self.scan_quicktime(ftype)
def scan_quicktime(self, ftype='tiff'):
# function to take screenshot via AppleScript (wire connection to computer)
# To do: 1. open QuickTime player and do a movie recording
# 2. Select drop down arrow next to record button, select device
# Steps: 1. Get Window ID of QuickTime Player
# 2. Run shell script to screen-capture the window ID
if self.verbose:
self.debug("[DEBUG] Starting QuickTime")
start = time.time()
full_path = os.path.join(self.location, self.picture)
script = """tell application "QuickTime Player"
set winID to id of window 1
end tell
do shell script "screencapture -x -t tiff -l " & winID &"""
script += ' " ' + full_path + '"'
# replace 'tiff' with ftype
script = script.replace('tiff', ftype)
# Take screenshot
s = NSAppleScript.alloc().initWithSource_(script)
s.executeAndReturnError_(None)
if self.verbose:
diff = time.time() - start
self.debug("[DEBUG] Quicktime - elapsed {!s}".format(diff))
self.times['scan_quicktime'] = diff
def ocr_vision(self, queue):
# Use Google Cloud Vision API to process OCR
if self.verbose:
pre = "[DEBUG] In ocr_vision() | "
start = time.time()
self.debug(pre + "starting")
# Authenticate
try:
file_path = os.path.join(self.location, self.google_auth_json)
if not os.path.isfile(file_path):
if self.verbose:
self.debug(pre + "no auth file")
queue.put("END")
return
except:
if self.verbose:
self.debug(pre + "no auth file")
queue.put("END")
return
# Google Vision API credential
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = file_path
# Instantiates a client
client = vision.ImageAnnotatorClient()
# get the image file (full path)
if not os.path.isfile(self.picture):
full_path = os.path.join(self.location, self.picture)
else:
full_path = self.picture
# loads the image into memory
with io.open(full_path, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# text detection on the image
response = client.text_detection(image=image)
text = response.text_annotations
for t in text:
self.raw = t.description
break
# cleaning up the text
self.raw = self.raw.split('\n')
# print out raw message
self.debug(pre + "raw - " + str(self.raw))
i = 0
while i < len(self.raw):
value = self.raw[i].lower()
if i < 2:
self.raw.pop(i)
self.debug("method - ocr | delete [" + value + "]")
i += 1
else:
i += 1
# swipe left comment
self.raw.pop(-1)
# Return data to parent process
queue.put(self.raw)
if self.verbose:
self.debug(pre + "raw - cleaned" + str(self.raw))
diff = time.time() - start
self.debug(pre + "elapsed {!s}".format(diff))
queue.put("END")
def parse(self):
# Parse the raw OCR text to find Q&A
if self.verbose:
pre = "[DEBUG] parsing texts | "
self.debug(pre + "starting")
start = time.time()
# Save it to question and answer variable
check_q = True
count_answer = 1
for line in self.raw:
#print(len(line), end=' ['+line+']\n')
# check for question mark in the question
if check_q:
if len(line) > 2:
if '?' not in line:
self.question += line + ' '
else:
self.question += line
check_q = False
else:
if 'Swipe left' not in line:
if len(line) > 0 and line != '-':
ans = line
self.answers[ans] = {
"answer": ans,
"keywords": [],
"score": 0,
"index": str(count_answer)
}
self.lookup_info[ans] = []
count_answer += 1
else:
break
# checking parsed results
if '?' not in self.question:
self.debug(pre + "Could not find question!")
raise
if len(self.answers) < 1:
self.debug(pre + "Could not find answers!")
raise
elif len(self.answers) > 3:
self.debug(pre + "Found more than three answers!")
raise
# Use local dictionary (nltk) to find nouns
for q in nltk.pos_tag(nltk.word_tokenize(self.question)):
if q[1] == 'NN' or q[1] == 'NNP':
self.question_nouns += " " + q[0]
self.question_nouns = self.question_nouns.strip().split(' ')
if self.verbose:
self.debug(pre + "question = " + str(self.question))
self.debug(pre +
"nouns in question - {!s}".format(self.question_nouns))
self.debug(pre + "answer = " + str(self.answers))
diff = time.time() - start
self.debug(pre + "elapsed {!s}".format(diff))
self.times["parse"] = diff
def keywords(self, words):
# Function to find words in a string that are also in question
# and return keywords found
keywords = []
for w in words:
if len(w) > 2:
if w in self.question_nouns:
if w not in keywords:
keywords.append(w)
return keywords
def lookup_wiki(self, queue):
# Get wiki info about answer
# Needs to return results to parent (for multi-processing)
if self.verbose:
pre = "[DEBUG] lookup_wiki() | "
self.debug(pre + "starting")
start = time.time()
# search in wikipedia for each answer
for index, ans in self.answers.items():
l_info = self.lookup_info[ans['answer']]
try:
page = self.wiki.page(ans['answer'])
if page.exists():
try:
words = []
for i in page.sections:
words += i.text.split(' ')
except:
self.debug(pre +
"issue with wikipedia for {!s}".format(
ans['answer']))
else:
l_info.append("[Wikipedia]: " + page.summary)
queue.put(
[ans['answer'],
self.keywords(words), l_info])
else:
a = ans['answer'].split(' ')
if len(a) < 2:
# Could not find page, so throw exception and move on
self.debug(pre +
"no results for {!s} in wikipedia... ".
format(ans['asnwer']))
raise
else:
# Try searching each word in answer as last resort
for w in a:
if len(w) > 3:
page = self.wiki.page(w)
if page.exists():
try:
words = []
for i in page.sections:
words += i.text.split(' ')
except:
self.debug(
pre +
"issue with wikipedia for {!s}".
format(ans['answer']))
else:
l_info.append(
"[Wikipedia {!s}]: ".format(w) +
page.summary)
queue.put([
ans['answer'],
self.keywords(words), l_info
])
except:
self.debug(
pre +
"issue with wikipedia for {!s}... ".format(ans['answer']))
self.debug(sys.exc_info())
queue.put("END")
if self.verbose:
self.debug(pre + "elapsed " + str(time.time() - start))
def lookup_dict_and_syn(self, queue):
# Use nltk to look up word info(synonym). Use online dictionary if fails.
if self.verbose:
pre = "[DEBUG] lookup_dict_and_syn() | "
self.debug(pre + "starting")
start = time.time()
# Get dictionary/synonyms
for index, ans in self.answers.items():
l_info = self.lookup_info[ans['answer']]
a = ans['answer'].split(' ') # incase of multi word answers
for w in a:
# don't waste time on looking for smaller words
if len(w) > 3:
# definition
define = nltk.corpus.wordnet.synsets(w)
synset_found = False
if len(define) < 1:
# local dictionary didn't find anything so search online
if self.verbose:
self.debug(pre +
"nltk none for {!s}, using vocabulary".
format(w))
try:
define = self.vb.meaning(w, format='list')
if define != False:
# Multiple definitions possible
for d in define:
l_info.append(
"[Meaning {!s}]: ".format(w) + d)
queue.put([
ans['answer'],
self.keywords(d), l_info
])
except:
self.debug(
pre +
"issue with vocabulary for {!s}... ".format(w))
self.debug(sys.exc_info())
else:
synset_found = True
l_info.append("[Meaning {!s}]: ".format(w) +
define[0].definition())
queue.put([
ans['answer'],
self.keywords(define[0].definition()), l_info
])
# Synonyms
if synset_found:
synonyms = [
l.name() for s in define for l in s.lemmas()
]
# Remove duplicates nltk adds
s = []
i = 0
while i < len(synonyms):
if synonyms[i] in s:
synonyms.pop(i)
else:
s.append(synonyms[i])
i += 1
syn = ', '.join(s)
l_info.append("[Synonyms {!s}]: ".format(w) + syn)
queue.put([ans['answer'], self.keywords(syn), l_info])
else:
# Local dictionary didn't find anything so search online
self.debug(
pre +
"nltk has nothing for {!s}, using vocabulary".
format(w))
try:
synonyms = self.vb.synonym(w, format='list')
if synonyms != False:
l_info.append("[Synonyms {!s}]: ".format(w) +
str(synonyms))
queue.put([
ans['answer'],
self.keywords(str(synonyms)), l_info
])
except:
self.debug(
pre +
"issue with vocabulary for {!s}... ".format(w))
self.debug(sys.exc_info())
queue.put("END")
if self.verbose:
self.debug(pre + "elapsed " + str(time.time() - start))
def lookup_google_search(self, queue):
# Do google search for each answer
# Find if words in results are found in the question
if self.verbose:
pre = "[DEBUG] lookup_google_search() | "
self.debug(pre + "starting")
start = time.time()
# Google Search
for index, ans in self.answers.items():
l_info = self.lookup_info[ans['answer']]
try:
#parse replace space by plus sgin
text = urllib.parse.quote_plus(ans['answer'])
url = 'https://google.com/search?q=' + text
#request google search
response = requests.get(url, timeout=2)
#pulling data out of html. lxml is a python paraser
soup = BeautifulSoup(response.text, 'lxml')
results = ''
#find_all() - mnethod to look through a tag's descendent (class in CSS)
for g in soup.find_all(class_='st'):
results += " " + g.text
#remove new line
cleaned_results = results.strip().replace('\n', '')
l_info.append("[Google]: " + cleaned_results)
queue.put(
[ans['answer'],
self.keywords(cleaned_results), l_info])
except:
self.debug(pre +
"issue with google search for {!s}... ".format(
ans['answer']))
self.debug(sys.exc_info())
if self.verbose:
self.debug(pre + "google search elapsed " +
str(time.time() - start))
def display(self):
# Clear the screen
os.system('cls' if os.name == 'nt' else 'clear')
# Text to output to screen
output = []
# Question
output.append('\n\nQuestion - ' + self.question + '\n')
# Answers & Lookup Info
# choice to track answer with the highest score
choice = {'index': [], 'score': 0, 'l_info': []}
# a is the key and ans is the value; items() is for dict datastructure
for a, ans in self.answers.items():
if ans['score'] == choice['score']:
choice['index'].append(a)
if 'NOT' in self.question:
if ans['score'] < choice['score']:
choice['index'] = [a]
choice['score'] = ans['score']
else:
if ans['score'] > choice['score']:
choice['index'] = [a]
choice['score'] = ans['score']
output.append("Choice - " + ans['answer'] + ' - Score ' +
str(ans['score']))
for l_info in self.lookup_info[ans['answer']]:
for l in l_info:
l_index = l.split(':')[0]
if l_index not in choice['l_info']:
choice['l_info'].append(l_index)
if len(l) > 140:
output.append(l[0:140])
else:
output.append(l)
output.append("[Keywords]: " + str(ans['keywords']))
output.append("")
# Highest scoring answer
if len(choice['index']) > 0:
choose = []
for i in choice['index']:
choose.append(self.answers[i]['answer'])
msg = "Answer - " + ', '.join(choose)
# If negative word, choose the lowest score
if 'NOT' in self.question:
msg += (" - NOT keyword so lowest score is " +
str(choice['score']))
else:
msg += (" - highest score is " + str(choice['score']))
output.append(msg)
else:
output.append("Answer - Unknown")
output.append("")
output.insert(1, msg + '\n')
# print it all
for line in output:
print(line)
def getsimilar(word):
return vb.synonym(word, format="list")
def synonym_request(word):
response = vb.synonym(word)
print(response)
async def on_message(message):
# we do not want the bot to reply to itself
if message.author == client.user:
return
#check if bot is working
if message.content.startswith('!heck'):
msg = 'Heck Off {0.author.mention}'.format(message)
await client.send_message(message.channel, msg)
#text response
if message.content.startswith('!venom'):
msg = 'is cute uwu'.format(message)
await client.send_message(message.channel, msg)
#text response
if message.content[0:4] == "ayy":
await client.send_message(message.channel, "lmao".format(message))
#text response
if message.content[0:5] == "lmao":
await client.send_message(message.channel, "ayy".format(message))
#using giphy api post a random happy birthday gif
if message.content.startswith("!hbd"):
msg = "HAPPPY BARTHDAYYYYY "
if len(message.mentions) > 0:
msg += message.mentions[0].mention
hbds = [x for x in g.search("happy birthday")]
hbd = hbds[random.randint(0, len(hbds))]
msg += " " + hbd.media_url
await client.send_message(message.channel, msg)
#tag spam a user(not recommended)
if message.content.startswith("!tagspam"):
msg = ""
if len(message.mentions) > 0:
for i in message.mentions:
if i.mention == "<@199515135142920192>": #hardcoded to not work against me xd
await client.send_message(message.channel, "Nope")
return
msg += i.mention + "\t"
else:
msg = "Mention someone."
await client.send_message(message.channel, msg)
return
if len(message.content.split(" ")) > 2:
try:
r = int(message.content.split(" ")[2])
if r > 50:
r = 50
except:
r = 5
else:
r = 5
for x in range(r):
await client.send_message(message.channel, msg)
#synonym using vocabulary api
if message.content[0:3] == "!s ": #match first 3 charachters
query = message.content.split(" ")[
1] #seperate the content from the identifier
result = vb.synonym(query)
msg = ""
if result == False: #if no reply from api
msg = "Not found"
else:
result = json.loads(result) #parse json string
for i in result:
msg += i["text"] + "\n" #add all results
await client.send_message(message.channel, msg)
#antonym using vocabulary api
if message.content[0:3] == "!a ":
query = message.content.split(" ")[1]
result = vb.antonym(query)
msg = ""
if result == False:
msg = "Not found"
else:
result = json.loads(result)
for i in result:
msg += i["text"] + "\n"
await client.send_message(message.channel, msg)
#usage
if message.content[0:3] == "!u ":
query = message.content.split(" ")[1:]
query = ' '.join(query)
result = vb.usage_example(query)
msg = ""
if result == False:
msg = "Not found"
else:
result = json.loads(result)
for i in result:
msg += i["text"] + "\n"
await client.send_message(message.channel, msg)
#meaning
if message.content[0:3] == "!m ":
query = message.content.split(" ")[1]
result = vb.meaning(query)
msg = ""
if result == False:
msg = "Not found"
else:
result = json.loads(result)
for i in result:
msg += i["text"] + "\n"
await client.send_message(message.channel, msg)
#despacito
if message.content.startswith("!despacito"):
with open("despacito.txt") as file:
content = file.readlines()
j = 0
while j < len(content):
msg = ""
i = 0
while i < 10 and j < len(
content
): #10 lines at a time to prevent getting rate limited by discord
msg += content[j]
i += 1
j += 1
await client.send_message(message.channel, msg)
def __cmp__(self, other):
if self.confidence > other.confidence:
return -1
elif self.confidence == other.confidence:
return 0
elif self.confidence < other.confidence:
return 1
def __repr__(self):
return str(self.confidence)+' '+str(self.words)
if __name__=="__main__":
from vocabulary.vocabulary import Vocabulary as vb
import json
try:
car_synonyms = json.loads(vb.synonym("qertadf"))
except Exception as e:
car_synonyms = []
print(car_synonyms)
# print(vb.synonym("car"))
# corrector = Spelling(["corpora/words_extend","corpora/words_domain"])
# corrector.load()
# corrector.train()
# print corrector.correct("tape")
# print corrector.correct("chinaa")
# print corrector.correct("speling'")
# print corrector.correct("bycycle")
# print corrector.correct("inconvient")
# print corrector.correct("arrainged")
# print corrector.correct("peotry")
# print corrector.correct("peotryy")
from vocabulary.vocabulary import Vocabulary as vb
name = raw_input()
print(vb.synonym("% s" % name))
def add_synonyms(query):
try:
synonyms = json.loads(vb.synonym(query))
except Exception as e:
synonyms = []
return query + ' ' + ' '.join([item['text'] for item in synonyms])
class HQTrivia():
"""
A simple test to see if one can automate determining the best answer
"""
def __init__(self):
# This determines source location on where to caputer picture
# QuickTime - MacOS has record feature for phone (best)
# WebCam - Use OpenCV to capture photo (untested)
self.use_quicktime = False
self.use_webcam = False
self.use_input = False
# The filename of picture (no extension means we're capturing image)
self.picture = 'source'
# Default location of where to work on self.picture
self.location = os.getcwd()
# Replace with your own auth file name
self.google_auth_json = 'blissend.json'
# Default the language for wikipedia searches
self.wiki = wikipediaapi.Wikipedia('en')
self.vb = Vocabulary()
# The OCR text
self.raw = ''
# The information we ultimately wanted to be analyzed
self.question = ''
self.answers = {}
self.definitions = {}
# For debugging
self.verbose = False
def debug(self, msg):
# In multiprocessing environments, the below statement helps
sys.stdout.flush()
print("hqtrivia-automation.py: " + str(msg))
def capture(self, ftype='tiff'):
"""
Simple function to select function to capture picture
"""
if self.verbose:
pre = "method - capture | "
self.debug(pre + "choosing how to capture...")
if self.use_input:
if self.verbose:
self.debug(pre + "input provided, don't capture")
return
# Set file type
self.picture += '.' + ftype
if self.use_quicktime:
if self.verbose:
self.debug(pre + "quicktime")
self.quicktime(ftype)
elif self.use_webcam:
if self.verbose:
self.debug(pre + "webcam")
self.webcam()
def quicktime(self, ftype='tiff'):
"""
Takes screenshot of phone screen via AppleScript
To use this open QuickTime player and do a movie recording. Select the
drop down arrow next to record button and select your iPhone. This
requires a wire connection to your computer using QuickTime. Remember,
don't record anything. Having it show on screen is enough for a
screencapture!
1. Get window ID of QuickTime Player
2. Tell script to run shell script command screencapture the window ID
"""
if self.verbose:
self.debug("method - quicktime | starting")
start = time.time()
full_path = os.path.join(self.location, self.picture)
script = """tell application "QuickTime Player"
set winID to id of window 1
end tell
do shell script "screencapture -x -t tiff -l " & winID &"""
script += ' " ' + full_path + '"'
script = script.replace('tiff', ftype)
s = NSAppleScript.alloc().initWithSource_(script)
s.executeAndReturnError_(None)
if self.verbose:
diff = time.time() - start
self.debug("method - quicktime | elapsed {!s}".format(diff))
def webcam(self):
"""
Takes screenshot using webcam.
This is untested but here just in case it's needed. You need to figure
out which camera to capture which unfortnately appears to be a discovery
process of entering in numbers from 0 to higher until found. Also note,
not all cameras have good controls and autofocus sucks for this.
"""
if self.verbose:
self.debug("method - webcam | starting")
video = cv2.VideoCapture(1) # cam id (try from 0 and higher til found)
video.set(cv2.CAP_PROP_AUTOFOCUS, 0) # turn the autofocus off
video.set(3, 1920)
video.set(4, 1080)
cv2.namedWindow("HQ OCR Camera")
#img_counter = 0
while True:
ret, frame = video.read()
cv2.imshow("HQ OCR Camera", frame)
if not ret:
break
k = cv2.waitKey(1)
if k%256 == 27: # ESC pressed
print("Escape hit, closing...")
break
elif k%256 == 32: # SPACE pressed
img_name = self.picture # format with counter for multiple pics
cv2.imwrite(img_name, frame)
print("{} written!".format(img_name))
#img_counter += 1
break
video.release()
cv2.destroyAllWindows()
def enhance(self):
"""
Edit image readability for Google OCR that is suuuuuuuuuuuuuper...
picky
1. Eliminate background on buttons (answers)
2. Turn to grayscale
3. Make image BIG because Google OCR likes it big ;)
4. Reduce the grayscale (eliminates button borders in good pictures)
5. Make anything not white, black because google can't see color -_-
"""
if self.verbose:
self.debug("method - enhance | starting")
start = time.time()
# Replace buttons (answers) background color, incease size scale/DPI
im = Image.open(self.picture)
im = im.convert("RGBA")
data = np.array(im)
red, green, blue, alpha = data.T # Unpack the bands for readability
gray_buttons = (red > 225) & (green > 225) & (blue > 225)
data[..., :-1][gray_buttons.T] = (255, 255, 255)
im = Image.fromarray(data)
width, height = im.size
# New file since we're going to edit it
file = self.picture.split('.')
self.picture = "source_edited." + file[len(file)-1]
im.crop((0, 300, width, height-400)).save(self.picture)
#im.resize((round(width*3), round(height*3))).save(
#self.picture, dpi=(600,600))
# Make grayscale
im = Image.open(self.picture)
im = im.convert('RGBA')
im = im.convert('L').save(self.picture)
#exit()
# Reduce the grayscale
#im = Image.open(self.picture)
#im = im.convert('RGBA')
#data = np.array(im)
#red, green, blue, alpha = data.T # Unpack the bands for readability
#gray_triming = (red > 158) & (green > 158) & (blue > 158)
#data[..., :-1][gray_triming.T] = (255, 255, 255)
#Image.fromarray(data).save(self.picture)
#exit()
# Replace non white with black
im = Image.open(self.picture)
im = im.convert('RGBA')
data = np.array(im)
red, green, blue, alpha = data.T # Unpack the bands for readability
non_white = (red < 255) & (green < 255) & (blue < 255)
data[..., :-1][non_white.T] = (0, 0, 0)
im = Image.fromarray(data)
width, height = im.size
im.resize((round(width*3), round(height*3))).save(self.picture)
#exit()
if self.verbose:
diff = time.time() - start
self.debug("method - enhance | elapsed {!s}".format(diff))
def vision_ocr(self, queue):
"""
Google Cloud Vision
The better OCR tool out there but requires additional setup. It is
free under limitations.
"""
if self.verbose:
pre = "method - vision_ocr | "
start = time.time()
self.debug(pre + "starting")
# See if we have an auth file, if not return
try:
file_path = os.path.join(self.location, self.google_auth_json)
if not os.path.isfile(file_path):
if self.verbose:
self.debug(pre + "no auth file")
queue.put("END")
return
except:
if self.verbose:
self.debug(pre + "no auth file")
queue.put("END")
return
# Credentials
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = file_path
# Instantiates a client
client = vision.ImageAnnotatorClient() # spits out shit, don't know why
# The image file
if not os.path.isfile(self.picture):
full_path = os.path.join(self.location, self.picture)
else:
full_path = self.picture
# Loads the image into memory
with io.open(full_path, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# Performs text detection on the image file
response = client.text_detection(image=image)
text = response.text_annotations
for t in text:
self.raw = t.description
break
# Clean up text
self.raw = self.raw.split('\n')
self.debug("method - vision_ocr | raw - " + str(self.raw))
index = 0
while index < len(self.raw):
value = self.raw[index].lower()
if len(value) < 10:
self.raw.pop(index)
#self.debug("method - ocr | delete [" + value + "]")
else:
index += 1
self.raw.pop(-1) # swipe left comment
if self.verbose:
self.debug("method - vision_ocr | raw - cleaned" + str(self.raw))
diff = time.time() - start
self.debug(pre + "elapsed {!s}".format(diff))
# Return data to parent process
queue.put(self.raw)
def tesseract_ocr(self, queue):
"""
Google Tesseract OCR
Finally read the image text if possible
"""
# Include the below line, if you don't have tesseract in your PATH
# Example tesseract_cmd: '/usr/local/bin/tesseract'
#pytesseract.pytesseract.tesseract_cmd = '<fullpath_to_tesseract>'
if self.verbose:
pre = "method - tesseract_ocr | "
self.debug(pre + "starting")
start = time.time()
# Enhance image first since tesseract doesn't do it
self.enhance()
# Get text from image (OCR)
self.raw = pytesseract.image_to_string(
Image.open(self.picture), config="-psm 11")
# Clean it up
self.raw = self.raw.split('\n')
if self.verbose:
self.debug(pre + "raw = " + str(self.raw))
index = 0
while index < len(self.raw):
value = self.raw[index].lower()
if len(value) < 1:
self.raw.pop(index)
#self.debug("method - ocr | delete [" + value + "]")
else:
index += 1
if self.verbose:
self.debug(pre + "raw - cleaned = " + str(self.raw))
diff = time.time() - start
self.debug(pre + "elapsed {!s}".format(diff))
# Return the data to main parent process
queue.put([self.picture, self.raw])
def parse(self):
"""
Parser for the OCR text
This is tricky because the OCR text won't always be the same. So
adjustments may have to be tweaked here.
"""
if self.verbose:
self.debug("method - parse | starting")
start = time.time()
# Parse text into question and answer variables
check_q = True
counter = 1 # for counting answers
for line in self.raw:
#print(len(line), end=' ['+line+']\n')
if check_q:
if len(line) > 2:
if '?' not in line:
self.question += line + ' '
else:
self.question += line
check_q = False
else:
if 'Swipe left' not in line:
if len(line) > 0 and line != '-':
ans = line
self.answers[str(counter)] = {
"answer": ans,
"keywords": [],
"score": 0,
"index": str(counter)
}
self.definitions[ans] = []
counter += 1
else:
break
if self.verbose:
self.debug("method - parse | question = " + str(self.question))
self.debug("method - parse | answer = " + str(self.answers))
diff = time.time() - start
self.debug("method - parse | elapsed {!s}".format(diff))
def lookup(self, index):
"""
Gets information about answer to determine relevance to question
This is a multiprocess function and therefore updated values have to be
returned to parent process.
"""
if self.verbose:
pre = "method - lookup | "
self.debug(pre + "starting")
start = time.time()
# Reference/copy of self values
answers = self.answers[index]
definitions = self.definitions[answers['answer']]
value = answers['answer']
question_nouns = ''
for q in nltk.pos_tag(nltk.word_tokenize(self.question)):
if q[1] == 'NN' or q[1] == 'NNP':
question_nouns += " " + q[0]
question_nouns = question_nouns.strip().split(' ')
if self.verbose:
self.debug(pre + "nouns in question - {!s}".format(question_nouns))
# First get wikipedia information (the most helpful)
time_wiki = time.time()
try:
page = self.wiki.page(value)
if page.exists():
definitions.append(page)
definitions.append("[Wikipedia]: " + page.summary)
except:
self.debug(pre + "issue with wikipedia... ")
self.debug(sys.exc_info()[0])
if self.verbose:
self.debug(pre + "wiki elapsed " + str(time.time() - time_wiki))
# Google search
time_gsearch = time.time()
try:
text = urllib.parse.quote_plus(value)
url = 'https://google.com/search?q=' + text
response = requests.get(url, timeout=2)
soup = BeautifulSoup(response.text, 'lxml')
results = ''
for g in soup.find_all(class_='st'):
results += " " + g.text
definitions.append("[Google]: " + results.strip().replace('\n',''))
except:
self.debug(pre + "issue with google search... ")
self.debug(sys.exc_info()[0])
if self.verbose:
self.debug(
pre +
"google search elapsed " +
str(time.time() - time_gsearch))
# Get dictionary definitions
time_define = time.time()
define = nltk.corpus.wordnet.synsets(value)
synset_found = False
if len(define) < 1:
# Means local dictionary didn't find anything so search online
if self.verbose:
self.debug(
pre +
"nltk nothing for {!s}, using vocabulary".format(value))
try:
define = self.vb.meaning(value, format='list')
if define != False:
# There may be multiple difinitions so count them up
counter = 1
for d in define:
definitions.append(
"[Meaning " + str(counter) + "]: " + d)
counter += 1
except:
self.debug(pre + "issue with vocabulary... ")
self.debug(sys.exc_info()[0])
else:
synset_found = True
definitions.append("[Meaning]: " + define[0].definition())
if self.verbose:
self.debug(
pre +
"dictionary elapsed " +
str(time.time() - time_define))
# Get synonyms
time_synonyms = time.time()
if synset_found:
synonyms = [l.name() for s in define for l in s.lemmas()]
# Remove duplicates
s = []
i = 0
while i < len(synonyms):
if synonyms[i] in s:
synonyms.pop(i)
else:
s.append(synonyms[i])
i += 1
definitions.append("[Synonyms]: " + ', '.join(s))
else:
# Means local dictionary didn't find anything so search online
try:
synonyms = self.vb.synonym(value, format='list')
if synonyms != False:
definitions.append("[Synonyms]: " + str(synonyms))
except:
self.debug(pre + "issue with vocabulary... ")
self.debug(sys.exc_info()[0])
if self.verbose:
self.debug(
pre +
"synonyms elapsed " +
str(time.time() - time_synonyms))
# Score the answer
if len(definitions) > 0:
for define in definitions:
if type(define) == str:
if "[Wikipedia]" not in define:
d = define.split(':') # remove pretag [Google]: bla blah
words = d[1].split(' ')
else:
# This is for WIKIPEDIA sections which isn't a string
try:
words = []
for i in page.sections:
words += i.text.split(' ')
except:
self.debug(pre + "issue with wikipedia")
for w in words:
if len(w) > 2:
if w in question_nouns:
if w not in answers['keywords']:
answers['keywords'].append(w)
answers['score'] += 1
if self.verbose:
diff = time.time() - start
self.debug("method - lookup | elapsed {!s} for {!s}".format(diff, index))
# Send data back to parent process
return answers, definitions, index
def display(self):
# Question
print('\n\nQuestion - ' + self.question, end='\n\n')
# Answers
choice = {'index': [], 'score': 0}
for a, ans in self.answers.items():
if ans['score'] == choice['score']:
choice['index'].append(a)
if 'NOT' in self.question:
if ans['score'] < choice['score']:
choice['index'] = [a]
choice['score'] = ans['score']
else:
if ans['score'] > choice['score']:
choice['index'] = [a]
choice['score'] = ans['score']
print("Choice - " + ans['answer'] + ' - Score ' + str(ans['score']))
for d in self.definitions[ans['answer']]:
if type(d) == str:
if len(d) > 140:
print(d[0:140])
else:
print(d)
print("[Keywords]: " + str(ans['keywords']))
print("")
# Choose answer
if len(choice['index']) > 0:
choose = []
for i in choice['index']:
choose.append(self.answers[i]['answer'])
print("Answer - " + ', '.join(choose), end='')
if 'NOT' in self.question:
print(" - NOT keyword so lowest score is " +
str(choice['score']))
else:
print(" - highest score is " + str(choice['score']))
else:
print("Answer - Unknown")
print("")
