def update_on_priv_msg(self, data):
if data['message'].find('.w ') == -1:
return
i18n_server = i18n()
w = wikipedia.set_lang(i18n_server.get_text('wiki_lang'))
q = data['message'].split(' ')
query = ''
for word in q:
if word.strip() != '.w':
query += word + ' '
w = wikipedia.search(query)
if w.__len__() == 0:
Connection.singleton().send_back(data['nick'] + ', ' + i18n_server.get_text('wiki_fail'), data)
return
try:
page = wikipedia.WikipediaPage(w.pop(0))
except wikipedia.DisambiguationError as error:
print('disambiguation page')
page = wikipedia.WikipediaPage(error.args[1][0])
Connection.singleton().send_back(data['nick'] + ' ' + page.url, data)
index = 51 + page.summary[50:230].find('. ')
if index == -1 or index > 230:
Connection.singleton().send_back(page.summary[0:230], data)
else:
Connection.singleton().send_back(page.summary[0:index], data)
def update_on_priv_msg(self, data):
if data['message'].find('.w ') == -1:
return
i18n_server = i18n()
w = wikipedia.set_lang(i18n_server.get_text('wiki_lang'))
q = data['message'].split(' ')
query = ''
for word in q:
if word.strip() != '.w':
query += word + ' '
w = wikipedia.search(query)
if w.__len__() == 0:
Connection.singleton().send_channel(data['nick'] + ', ' + i18n_server.get_text('wiki_fail'))
return
page = wikipedia.WikipediaPage(w.pop(0))
Connection.singleton().send_channel(data['nick'] + ' ' + page.url)
Connection.singleton().send_channel(page.summary[0:230])
def wiki(bot, event, *args):
"""
**Wikipedia:**
Usage: /wiki <keywords to search for> <optional: sentences to display [defaults to 3]>
Purpose: Get summary from Wikipedia on keywords.
"""
from wikipedia import wikipedia, PageError, DisambiguationError
def summary(self, sentences=3):
if not getattr(self, '_summary', False):
query_params = {
'prop': 'extracts',
'explaintext': '',
'exintro': '',
}
query_params['exsentences'] = sentences
if not getattr(self, 'title', None) is None:
query_params['titles'] = self.title
else:
query_params['pageids'] = self.pageid
request = wikipedia._wiki_request(query_params)
self._summary = request['query']['pages'][self.pageid]['extract']
return self._summary
wikipedia.WikipediaPage.summary = summary
try:
sentences = 3
try:
if args[-1].isdigit():
sentences = args[-1]
args = args[:-1]
page = wikipedia.page(' '.join(args))
except DisambiguationError as e:
page = wikipedia.page(wikipedia.search(e.options[0], results=1)[0])
segments = [
hangups.ChatMessageSegment(page.title, hangups.SegmentType.LINK, is_bold=True, link_target=page.url),
hangups.ChatMessageSegment('\n', hangups.SegmentType.LINE_BREAK),
hangups.ChatMessageSegment(page.summary(sentences=sentences))]
bot.send_message_segments(event.conv, segments)
except PageError:
bot.send_message(event.conv, "Couldn't find \"{}\". Try something else.".format(' '.join(args)))
def test_offset(self):
"""Test paginating a request results."""
self.assertEqual(wikipedia.search("Porsche", results=1, page=2), mock_data['data']["porsche.search.paginated"])
def test_limit(self):
"""Test limiting a request results."""
self.assertEqual(wikipedia.search("Porsche", results=3), mock_data['data']["porsche.search"])
self.assertEqual(_wiki_request.calls["{'list': 'search', 'srprop': '', 'srlimit': 3, 'limit': 3, 'srsearch': 'Porsche'}"], 1)
def test_search(self):
"""Test parsing a Wikipedia request result."""
self.assertEqual(wikipedia.search("Barack Obama"), mock_data['data']["barack.search"])
self.assertEqual(_wiki_request.calls["{'list': 'search', 'srprop': '', 'srlimit': 10, 'limit': 10, 'srsearch': 'Barack Obama'}"], 1)
def test():
form = LoginForm()
if form.validate_on_submit():
flash(form.openid.data , 'Question')
text = form.openid.data.lower()
data = form.openid.data.lower()
data1 = form.openid.data
text = text.split()
negator = ['not', 'never', 'not possible', 'does not', 'abort', 'neither', 'nor', 'no', 'negative', 'negate']
assertor = ['may be', 'can be', 'not sure', 'might', 'may']
preposition = ['have', 'is', 'are', 'about', 'above', 'across', 'after', 'against', 'along', 'among', 'around', 'at', 'before', 'behind', 'below', 'beneath', 'beside', 'between', 'by', 'down', 'during', 'except', 'for', 'from', 'front', 'inside', 'instead', 'into', 'like', 'near', 'of', 'off', 'on', 'onto', 'top', 'out', 'outside', 'over', 'past', 'since', 'through', 'to', 'toward', 'under', 'underneath', 'until', 'up', 'upon', 'with', 'within', 'without']
wh = ['why', 'what', 'how', 'Who', 'whoever', 'whom', 'whomever', 'whose', 'which']
pronoun = ['i', 'me', 'you', 'she', 'her', 'he', 'him', 'it', 'we', 'us', 'you', 'they', 'them', 'my', 'mine', 'your', 'yours', 'hers', 'his', 'its', 'yours', 'ours', 'theirs', 'myself', 'yourself', 'himself', 'herself', 'itself', 'all', 'another', 'any', 'anybody', 'anyone', 'anything', 'both', 'each', 'either', 'everybody', 'everyone', 'everything', 'few', 'many', 'neither', 'nobody', 'none', 'nothing', 'one', 'several', 'some', 'somebody', 'someone', 'something', 'this', 'that', 'these', 'those']
# Removing Wh Question
wh_q=''
for ser in text:
inflag = 0
for w in wh:
if w == ser:
inflag = 1
if inflag == 0:
wh_q = wh_q + ser + ' '
# Removing Prepostion
wh_q = wh_q.split()
prep_q = ''
for ser in wh_q:
inflag = 0
for prep in preposition:
if ser == prep:
inflag = 1
if inflag == 0:
prep_q = prep_q + ser + ' '
# Removing Pronoun
prep_q = prep_q.split()
pro_q = ''
for ser in prep_q:
inflag = 0
for pro in pronoun:
if ser == pro:
inflag = 1
if inflag == 0:
pro_q = pro_q + ser + ' '
text = pro_q
text = text.split()
data = pro_q.strip()
flag = 0
answer = 0
wikiflag = 0
ans = 0
data = ''
asser = 0
nege = 0
posi = 0
#Assertive Section
for ser in text:
inflag = 0
for ass in assertor:
if ser == ass and flag == 0 or data.find(ass) != -1 and flag == 0:
inflag = 1
asser = 1
flash('Assertive', 'Answer')
flag=1
if inflag == 0:
data = data + ser + ' '
if asser == 1:
data = data.strip()
abc = models.Assertive.query.all()
for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
#return redirect ('http://www.lmgtfy.com/?q=' + data)
else:
finalans=a.answer
flash(a.answer, 'Answer')
#Negative Section
if asser == 0:
data = ''
for ser in text:
inflag = 0
for neg in negator:
if ser == neg and flag == 0 or data.find(neg) != -1 and flag == 0:
inflag = 1
nege = 1
flash('Negative', 'Answer')
flag = 1
if inflag == 0:
data = data + ser + ' '
if nege == 1:
data = data.strip()
abc = models.Negative.query.all()
for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
#return redirect ('http://www.lmgtfy.com/?q=' + data)
else:
finalans=a.answer
flash(a.answer, 'Answer')
#Postive Section
if flag == 0:
data = form.openid.data.lower()
flash('Positive', 'Answer')
abc = models.Positive.query.all()
for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
#return redirect ('http://www.lmgtfy.com/?q=' + data)
else:
finalans=a.answer
flash(a.answer, 'Answer')
#Wiki Section
ans = 0
if wikiflag == 1:
abc = models.Wikipedia.query.all()
for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in Wikipedia database... Lets search Wikipedia Internet', 'Answer')
ny = wikipedia.search(data)
if ny == []:
return redirect ('http://www.lmgtfy.com/?q=' + data1)
else:
try:
ny1 = wikipedia.summary(data1, chars=0, auto_suggest=True, redirect=True, sentences=3)
finalans=ny1
flash(ny1, 'Answer')
ny2 = wikipedia.page(data1)
flash('Source: '+ ny2.url, 'Answer')
#u = models.Wikipedia(question=data, answer=ny1)
#db.session.add(u)
#db.session.commit()
except Exception as inst:
flash('Your question is either out of scope of very trival for me to answer', 'Answer')
finalans = 'Your question is either out of scope of very trival for me to answer'
else:
finalans=a.answer
flash(a.answer, 'Answer')
display = '\n'
s = models.Chats.query.all()
for chat in reversed(s):
flash('Question: ' + chat.question, 'Display')
flash('Answer: ' + chat.answer , 'Display')
flash('.', 'Display')
u = models.Chats(question=data1, answer=finalans)
db.session.add(u)
db.session.commit()
return redirect('/test')
return render_template("index2.html",
title = 'ChatterBot',
form = form)
def test_suggestion(self):
"""Test getting a suggestion as well as search results."""
search, suggestion = wikipedia.search("hallelulejah", suggestion=True)
self.assertEqual(search, [])
self.assertEqual(suggestion, u'hallelujah')
def test_limit(self):
"""Test limiting a request results."""
self.assertEqual(wikipedia.search("Porsche", results=3),
mock_data["data"]["porsche.search"])
def test_search(self):
"""Test parsing a Wikipedia request result."""
self.assertEqual(wikipedia.search("Barack Obama"),
mock_data["data"]["barack.search"])
def get_ticker(company_name, search_for_parent=True, sources=None):
if not sources:
sources = ["bing", "wikipedia"]
ticker = None
try:
if "bing" in sources:
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'
}
url = "https://www.bing.com/search?q=" + quote_plus(company_name +
" stock price")
req = urllib.request.Request(url, headers=headers)
page = urllib.request.urlopen(req)
soup = BeautifulSoup(page, "html.parser")
markup = str(soup.find_all("div", {"class": "fin_metadata"}))[1:-1]
text = BeautifulSoup(markup, "html.parser").get_text()
ticker_start = 0
ticker_end = 0
for i in range(len(text)):
if text[i] == ":":
ticker_start = i + 2
break
for i in range(len(text)):
if text[i] == "·":
ticker_end = i - 1
break
if not (ticker_start == 0 and ticker_end == 0):
ticker = text[ticker_start:ticker_end]
if not is_valid_ticker(ticker):
ticker = None
except:
pass
try:
if "bing" in sources and ticker is None:
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'
}
url = "https://www.bing.com/search?q=" + quote_plus(company_name)
req = urllib.request.Request(url, headers=headers)
page = urllib.request.urlopen(req)
soup = BeautifulSoup(page, "html.parser")
markup = str(soup.find_all("div", {"class": "fin_metadata"}))[1:-1]
text = BeautifulSoup(markup, "html.parser").get_text()
ticker_start = 0
ticker_end = 0
for i in range(len(text)):
if text[i] == ":":
ticker_start = i + 2
break
for i in range(len(text)):
if text[i] == "·":
ticker_end = i - 1
break
if not (ticker_start == 0 and ticker_end == 0):
ticker = text[ticker_start:ticker_end]
if not is_valid_ticker(ticker):
ticker = None
except:
pass
try:
if "wikipedia" in sources and ticker is None:
url = "https://en.wikipedia.org/wiki/" + "_".join(
company_name.split(" "))
req = urllib.request.Request(url)
try:
page = urllib.request.urlopen(req)
# Get info box > Get traded as > Get the ticker
soup = BeautifulSoup(page, "html.parser")
infobox = BeautifulSoup(
str(soup.find_all("table",
{"class": "infobox vcard"}))[1:-1],
"html.parser")
row_re = re.compile(r"Traded")
row = infobox.find(text=row_re)
if row is not None:
row = str(row.parent.parent.parent)
soup = BeautifulSoup(row, "html.parser")
markup = soup.find_all("a", {"rel": "nofollow"})
for m in markup:
if m.get_text().isupper():
ticker = m.get_text()
except:
company_name = wikipedia.search(company_name)[0]
url = "https://en.wikipedia.org/wiki/" + "_".join(
company_name.split(" "))
req = urllib.request.Request(url)
try:
page = urllib.request.urlopen(req)
# Get info box > Get traded as > Get the ticker
soup = BeautifulSoup(page, "html.parser")
infobox = BeautifulSoup(
str(soup.find_all("table",
{"class": "infobox vcard"}))[1:-1],
"html.parser")
row_re = re.compile(r"Traded")
row = infobox.find(text=row_re)
if row is not None:
row = str(row.parent.parent.parent)
soup = BeautifulSoup(row, "html.parser")
markup = soup.find_all("a", {"rel": "nofollow"})
for m in markup:
if m.get_text().isupper():
ticker = m.get_text()
except:
pass
except:
pass
if not is_valid_ticker(ticker):
ticker = None
if search_for_parent and ticker is None:
parent = find_parent(company_name)
if parent:
ticker = get_ticker(parent)
return ticker
def test_search(self):
_search = wikipedia.search("Queen")[1]
assert test_search == "u\'Queen (band)\'"
def process_request(req):
action = req.get("result").get("action")
if action != "yahooWeatherForecast":
if action == u"weather.search":
try:
parameters = req.get("result").get("parameters")
request_type = parameters.get('request_type')
current_time = parameters.get('current_time')
location = parameters.get('location')
return weather_request_hangouts(None, None, location)
except Exception as e:
return {}
# Do something else
if u"wisdom" in action:
parameters = req.get("result").get("parameters")
query_wisdom = parameters.get('q')
request_type = parameters.get('request_type')
try:
wikipedia_answer_url = None
wisdom_answer = None
speech = None
if query_wisdom:
wikipedia_search = wikipedia.search(query_wisdom,
results=2)[0]
wikipedia_answer_url = wikipedia.page(wikipedia_search).url
if wikipedia_answer_url:
wisdom_answer = wikipedia.summary(query_wisdom,
sentences=1)
else:
wikipedia_search = wikipedia.search(query_wisdom,
results=2)[1]
wikipedia_answer_url = wikipedia.page(
wikipedia_search).url
if wisdom_answer and wikipedia_answer_url:
speech = "According to Wikipedia.org (" + wikipedia_answer_url + "): " + wisdom_answer
else:
speech = "I am sorry, but I couldn't find a good article or result for your " \
"request on Wikipedia.org " \
"Why don't you click on the following link to see similar results: "
"https://en.wikipedia.org/w/index.php?search=" + wisdom_answer.replace(
" ", "+")
return {
"speech": speech,
"displayText": speech,
# "data": data,
# "contextOut": [],
"source": "apiai-weather-webhook-sample"
}
except Exception as e:
logger.error("Error")
return {}
baseurl = "https://query.yahooapis.com/v1/public/yql?"
yql_query = make_yql_query(req)
if yql_query is None:
return {}
yql_url = baseurl + urlencode({'q': yql_query}) + "&format=json"
result = urlopen(yql_url).read()
data = json.loads(result)
res = make_webhook_result(data)
return res
def test_search(self):
"""Test parsing a Wikipedia request result."""
self.assertEqual(wikipedia.search("Barack Obama"), mock_data['data']["barack.search"])
def test_suggestion(self):
"""Test getting a suggestion as well as search results."""
search, suggestion = wikipedia.search("hallelulejah", suggestion=True)
self.assertEqual(search, [])
self.assertEqual(suggestion, u"hallelujah")
def test_limit(self):
"""Test limiting a request results."""
self.assertEqual(wikipedia.search("Porsche", results=3), mock_data['data']["porsche.search"])
def test_suggestion_none(self):
"""Test getting a suggestion when there is no suggestion."""
search, suggestion = wikipedia.search("qmxjsudek", suggestion=True)
self.assertEqual(search, [])
self.assertEqual(suggestion, None)
def test_suggsetion_none(self):
"""Test getting a suggestion when there is no suggestion."""
search, suggestion = wikipedia.search("qmxjsudek", suggestion=True)
self.assertEqual(search, [])
self.assertEqual(suggestion, None)
def test():
form = LoginForm()
if form.validate_on_submit():
flash(form.openid.data , 'Question')
text = form.openid.data.lower()
data = form.openid.data.lower() # for processing of answer(data mining)
data1 = form.openid.data # for finding verbs nouns adjectives and number
text = text.split() # for finding positive negative and assertive
# Finding Nouns
tokenized = nltk.word_tokenize(data1)
p = nltk.pos_tag(tokenized)
flash(p, 'Answer')
name = nltk.ne_chunk(p, binary=True)
ent = re.findall(r'NE\s(.*?)/', str(name))
chunkGram = r"""Noun: {<NN\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
NNnoun = chunkParser.parse(p)
ip_noun = re.findall(r'Noun\s(.*?)/', str(NNnoun))
#noun = re.findall(r'<NN\w?>*', str(p))
#print ent
#nouns = ''
#for n in ip_noun:
# nouns = nouns + n + ' '
#flash ('Nouns: ' + str(nouns), 'Answer')
flash ('Nouns list: ' + str(ip_noun), 'Answer')
# Finding Verbs
tokenized = nltk.word_tokenize(data1)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<VB\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
VBverb = chunkParser.parse(p)
ip_verb = re.findall(r'Verb\s(.*?)/', str(VBverb))
#noun = re.findall(r'<NN\w?>*', str(p))
#print ent
#verbs = ''
#for v in ip_verb:
# verbs = verbs + v + ' '
#flash ('Verbs: ' + str(verbs), 'Answer')
flash ('Verb List: ' + str(ip_verb), 'Answer')
# Finding Adjective
tokenized = nltk.word_tokenize(data1)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<JJ\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
JJAdj = chunkParser.parse(p)
ip_adj = re.findall(r'Verb\s(.*?)/', str(JJAdj))
#noun = re.findall(r'<NN\w?>*', str(p))
#print ent
#adjs = ''
#for a in ip_adj:
# adjs = adjs + a + ' '
#flash ('Ajectives: ' + str(adjs), 'Answer')
flash ('Adjective list: ' + str(ip_adj), 'Answer')
# Finding Numbers
tokenized = nltk.word_tokenize(data1)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Number: {<CD\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
CDNumber = chunkParser.parse(p)
ip_number = re.findall(r'Number\s(.*?)/', str(CDNumber))
flash ('Number list: ' + str(ip_number), 'Answer')
max_check = len(ip_noun) + len(ip_verb) + len(ip_adj) + len(ip_number) #counting the number of max hits
# Similar Noun Form
simi = models.Similar.query.all()
count_n = len(ip_noun)
max_n = 0
for noun_sim in ip_noun:
for sim in simi:
if sim.word1 == noun_sim:
ip_noun.append(str(sim.word2))
ip_noun.append(str(sim.word3))
if sim.word2 == noun_sim:
ip_noun.append(str(sim.word1))
ip_noun.append(str(sim.word3))
if sim.word3 == noun_sim:
ip_noun.append(str(sim.word1))
ip_noun.append(str(sim.word2))
max_n = max_n + 1
if max_n >= count_n:
break
# Similar Verb Form
simi = models.Similar.query.all()
count_v = len(ip_verb)
max_v = 0
for verb_sim in ip_verb:
for sim in simi:
if sim.word1 == verb_sim:
ip_verb.append(str(sim.word2))
ip_verb.append(str(sim.word3))
if sim.word2 == verb_sim:
ip_verb.append(str(sim.word1))
ip_verb.append(str(sim.word3))
if sim.word3 == verb_sim:
ip_verb.append(str(sim.word1))
ip_verb.append(str(sim.word2))
max_v = max_v + 1
if max_v >= count_v:
break
# Similar Adjective Form
simi = models.Similar.query.all()
count_a = len(ip_adj)
max_a = 0
for adj_sim in ip_adj:
for sim in simi:
if sim.word1 == adj_sim:
ip_adj.append(str(sim.word2))
ip_adj.append(str(sim.word3))
if sim.word2 == adj_sim:
ip_adj.append(str(sim.word1))
ip_adj.append(str(sim.word3))
if sim.word3 == adj_sim:
ip_adj.append(str(sim.word1))
ip_adj.append(str(sim.word2))
max_a = max_a + 1
if max_a >= count_a:
break
#Printing the new appended list
flash ('Nouns list: ' + str(ip_noun), 'Answer')
flash ('Verb List: ' + str(ip_verb), 'Answer')
flash ('Adjective list: ' + str(ip_adj), 'Answer')
flash ('Number list: ' + str(ip_number), 'Answer')
ip_total = ip_noun + ip_verb + ip_adj + ip_number
ip_total = list(set(ip_total))
negator = ['not', 'never', 'not possible', 'does not', 'abort', 'neither', 'nor', 'negative', 'negate', 'can\'t', 'doesn\'t','can not','cant','doesnt','dont','don\'t']
assertor = ['may be', 'can be', 'not sure', 'might', 'may']
'''preposition = ['have', 'is', 'are', 'about', 'above', 'across', 'after', 'against', 'along', 'among', 'around', 'at', 'before', 'behind', 'below', 'beneath', 'beside', 'between', 'by', 'down', 'during', 'except', 'for', 'from', 'front', 'inside', 'instead', 'into', 'like', 'near', 'of', 'off', 'on', 'onto', 'top', 'out', 'outside', 'over', 'past', 'since', 'through', 'to', 'toward', 'under', 'underneath', 'until', 'up', 'upon', 'with', 'within', 'without']
wh = ['why', 'what', 'how', 'Who', 'whoever', 'whom', 'whomever', 'whose', 'which']
pronoun = ['i', 'me', 'you', 'she', 'her', 'he', 'him', 'it', 'we', 'us', 'you', 'they', 'them', 'my', 'mine', 'your', 'yours', 'hers', 'his', 'its', 'yours', 'ours', 'theirs', 'myself', 'yourself', 'himself', 'herself', 'itself', 'all', 'another', 'any', 'anybody', 'anyone', 'anything', 'both', 'each', 'either', 'everybody', 'everyone', 'everything', 'few', 'many', 'neither', 'nobody', 'none', 'nothing', 'one', 'several', 'some', 'somebody', 'someone', 'something', 'this', 'that', 'these', 'those']
# Removing Wh Question
wh_q=''
for ser in text:
inflag = 0
for w in wh:
if w == ser:
inflag = 1
if inflag == 0:
wh_q = wh_q + ser + ' '
# Removing Prepostion
wh_q = wh_q.split()
prep_q = ''
for ser in wh_q:
inflag = 0
for prep in preposition:
if ser == prep:
inflag = 1
if inflag == 0:
prep_q = prep_q + ser + ' '
# Removing Pronoun
prep_q = prep_q.split()
pro_q = ''
for ser in prep_q:
inflag = 0
for pro in pronoun:
if ser == pro:
inflag = 1
if inflag == 0:
pro_q = pro_q + ser + ' '
text = pro_q
text = text.split()
data = pro_q.strip()
'''
flag = 0
answer = 0
wikiflag = 0
ans = 0
asser = 0
nege = 0
posi = 0
#Assertive Section
for ser in text:
for ass in assertor:
if ser == ass and flag == 0 or data.find(ass) != -1 and flag == 0:
asser = 1
flash('Assertive', 'Answer')
flag=1
if asser == 1:
display_ans = ''
max_value = int(max_check * 0.8 + 0.5) # counting the no of hits
abc = models.Positive.query.all()
for a in abc:
# Noun
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
ent = re.findall(r'NE\s(.*?)/', str(name))
chunkGram = r"""Noun: {<NN\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
NNnoun = chunkParser.parse(p)
db_noun = re.findall(r'Noun\s(.*?)/', str(NNnoun))
# Verbs
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<VB\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
VBverb = chunkParser.parse(p)
db_verb = re.findall(r'Verb\s(.*?)/', str(VBverb))
# Adjective
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<JJ\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
JJAdj = chunkParser.parse(p)
db_adj = re.findall(r'Verb\s(.*?)/', str(JJAdj))
# Number
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Number: {<CD\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
CDNumber = chunkParser.parse(p)
db_number = re.findall(r'Number\s(.*?)/', str(CDNumber))
db_total = db_noun + db_adj + db_verb + db_number
db_total = list(set(db_total))
count = 0
for ip in ip_total:
for dbs in db_total:
db_plural = re.escape(dbs) + 's?'
ip_plural = re.escape(ip) + 's?'
if re.match(db_plural, ip,flags=0|re.IGNORECASE):
count = count + 1
if re.match(ip_plural,dbs,flags=0|re.IGNORECASE):
count = count + 1
if ip == dbs:
count = count - 1
if max_value < count:
display_ans = a.answer
max_value = count
if display_ans == '':
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
else:
extra = 'Please be more sure about the problem you are facing so that we can provide you with precise answers. According to me the most relevant solution to your problem is: '
display_ans = extra + '\n' + display_ans
flash(display_ans, 'Answer')
"""for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
#return redirect ('http://www.lmgtfy.com/?q=' + data)
else:
finalans=a.answer
flash(a.answer, 'Answer')"""
#Negative Section
if asser != 1:
for ser in text:
for neg in negator:
if ser == neg and flag == 0 or data.find(neg) != -1 and flag == 0:
nege = 1
flash('Negative', 'Answer')
flag = 1
if nege == 1:
display_ans = ''
max_value = int(max_check * 0.8 + 0.5) # counting the no of hits
abc = models.Negative.query.all()
for a in abc:
# Noun
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
ent = re.findall(r'NE\s(.*?)/', str(name))
chunkGram = r"""Noun: {<NN\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
NNnoun = chunkParser.parse(p)
db_noun = re.findall(r'Noun\s(.*?)/', str(NNnoun))
# Verbs
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<VB\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
VBverb = chunkParser.parse(p)
db_verb = re.findall(r'Verb\s(.*?)/', str(VBverb))
# Adjective
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<JJ\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
JJAdj = chunkParser.parse(p)
db_adj = re.findall(r'Verb\s(.*?)/', str(JJAdj))
# Number
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Number: {<CD\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
CDNumber = chunkParser.parse(p)
db_number = re.findall(r'Number\s(.*?)/', str(CDNumber))
db_total = db_noun + db_adj + db_verb + db_number
db_total = list(set(db_total))
count = 0
for ip in ip_total:
for dbs in db_total:
db_plural = re.escape(dbs) + 's?'
ip_plural = re.escape(ip) + 's?'
if re.match(db_plural, ip,flags=0|re.IGNORECASE):
count = count + 1
if re.match(ip_plural,dbs,flags=0|re.IGNORECASE):
count = count + 1
if ip == dbs:
count = count - 1
if max_value < count:
display_ans = a.answer
max_value = count
if display_ans == '':
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
else:
flash(display_ans, 'Answer')
"""for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
#return redirect ('http://www.lmgtfy.com/?q=' + data)
else:
finalans=a.answer
flash(a.answer, 'Answer')"""
#Postive Section
if asser != 1 and nege != 1:
if flag == 0:
data = form.openid.data.lower()
flash('Positive', 'Answer')
flag = 1
display_ans = ''
max_value = int(max_check * 0.8 + 0.5) # counting the no of hits
abc = models.Positive.query.all()
for a in abc:
# Noun
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
ent = re.findall(r'NE\s(.*?)/', str(name))
chunkGram = r"""Noun: {<NN\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
NNnoun = chunkParser.parse(p)
db_noun = re.findall(r'Noun\s(.*?)/', str(NNnoun))
# Verbs
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<VB\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
VBverb = chunkParser.parse(p)
db_verb = re.findall(r'Verb\s(.*?)/', str(VBverb))
# Adjective
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<JJ\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
JJAdj = chunkParser.parse(p)
db_adj = re.findall(r'Verb\s(.*?)/', str(JJAdj))
# Number
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Number: {<CD\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
CDNumber = chunkParser.parse(p)
db_number = re.findall(r'Number\s(.*?)/', str(CDNumber))
db_total = db_noun + db_adj + db_verb + db_number
db_total = list(set(db_total))
count = 0
for ip in ip_total:
for dbs in db_total:
db_plural = re.escape(dbs) + 's?'
ip_plural = re.escape(ip) + 's?'
if re.match(db_plural, ip,flags=0|re.IGNORECASE):
count = count + 1
if re.match(ip_plural,dbs,flags=0|re.IGNORECASE):
count = count + 1
if ip == dbs:
count = count - 1
if max_value < count:
display_ans = a.answer
max_value = count
if display_ans == '':
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
else:
flash(display_ans, 'Answer')
"""abc = models.Positive.query.all()
for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not in database... Lets search Wikipedia Database', 'Answer')
wikiflag = 1
#return redirect ('http://www.lmgtfy.com/?q=' + data)
else:
finalans=a.answer
flash(a.answer, 'Answer')"""
#Wiki Section
ans = 0
if wikiflag == 1:
display_ans = ''
max_value = int(max_check * 0.8 + 0.5) # counting the no of hits
abc = models.Wikipedia.query.all()
for a in abc:
# Noun
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
ent = re.findall(r'NE\s(.*?)/', str(name))
chunkGram = r"""Noun: {<NN\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
NNnoun = chunkParser.parse(p)
db_noun = re.findall(r'Noun\s(.*?)/', str(NNnoun))
# Verbs
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<VB\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
VBverb = chunkParser.parse(p)
db_verb = re.findall(r'Verb\s(.*?)/', str(VBverb))
# Adjective
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<JJ\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
JJAdj = chunkParser.parse(p)
db_adj = re.findall(r'Verb\s(.*?)/', str(JJAdj))
# Number
tokenized = nltk.word_tokenize(a.question)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Number: {<CD\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
CDNumber = chunkParser.parse(p)
db_number = re.findall(r'Number\s(.*?)/', str(CDNumber))
db_total = db_noun + db_adj + db_verb + db_number
db_total = list(set(db_total))
count = 0
for ip in ip_total:
for dbs in db_total:
db_plural = re.escape(dbs) + 's?'
ip_plural = re.escape(ip) + 's?'
if re.match(db_plural, ip,flags=0|re.IGNORECASE):
count = count + 1
if re.match(ip_plural,dbs,flags=0|re.IGNORECASE):
count = count + 1
if ip == dbs:
count = count - 1
if max_value < count:
display_ans = a.answer
max_value = count
if display_ans == '':
answer = 0
else:
answer = 1
"""abc = models.Wikipedia.query.all()
for a in abc:
if (data.find(a.question.lower()) != -1 or a.question.lower().find(data) != -1) and len(data) >= 4:
ans = 1
break
if ans == 0:
answer = 0
else:
answer = 1"""
if answer == 0:
flash('Answer not in Wikipedia database... Lets search Wikipedia Internet', 'Answer')
ny = wikipedia.search(data)
if ny == []:
return redirect ('http://www.lmgtfy.com/?q=' + data1)
else:
try:
ny1 = wikipedia.summary(data1, chars=0, auto_suggest=True, redirect=True, sentences=3)
max_value = int(max_check * 0.8 + 0.5)
ip_wiki = ny1.encode('ascii','ignore')
# Noun
tokenized = nltk.word_tokenize(ip_wiki)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
ent = re.findall(r'NE\s(.*?)/', str(name))
chunkGram = r"""Noun: {<NN\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
NNnoun = chunkParser.parse(p)
db_noun = re.findall(r'Noun\s(.*?)/', str(NNnoun))
# Verbs
tokenized = nltk.word_tokenize(ip_wiki)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<VB\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
VBverb = chunkParser.parse(p)
db_verb = re.findall(r'Verb\s(.*?)/', str(VBverb))
# Adjective
tokenized = nltk.word_tokenize(ip_wiki)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Verb: {<JJ\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
JJAdj = chunkParser.parse(p)
db_adj = re.findall(r'Verb\s(.*?)/', str(JJAdj))
# Number
tokenized = nltk.word_tokenize(ip_wiki)
p = nltk.pos_tag(tokenized)
name = nltk.ne_chunk(p, binary=True)
chunkGram = r"""Number: {<CD\w?>} """
chunkParser = nltk.RegexpParser(chunkGram)
CDNumber = chunkParser.parse(p)
db_number = re.findall(r'Number\s(.*?)/', str(CDNumber))
db_total = db_noun + db_adj + db_verb + db_number
db_total = list(set(db_total))
count = 0
for ip in ip_total:
for dbs in db_total:
db_plural = re.escape(dbs) + 's?'
ip_plural = re.escape(ip) + 's?'
if re.match(db_plural, ip,flags=0|re.IGNORECASE):
count = count + 1
if re.match(ip_plural,dbs,flags=0|re.IGNORECASE):
count = count + 1
if ip == dbs:
count = count - 1
if max_value <= count:
display_ans = ny1
if display_ans == '':
answer = 0
else:
answer = 1
if answer == 0:
flash('Answer not precise in wikipedia Interet', 'Answer')
flash(ny1, 'Answer')
wikiflag = 1
else:
display_ans=ny1
flash(ny1, 'Answer')
ny2 = wikipedia.page(data1)
flash('Source: '+ ny2.url, 'Answer')
#u = models.Wikipedia(question=data1, answer=ny1)
#db.session.add(u)
#db.session.commit()
except Exception as inst:
flash('Your question is either out of scope of very trival for me to answer ' + str(inst), 'Answer')
display_ans = 'Your question is either out of scope of very trival for me to answer'
else:
flash(display_ans, 'Answer')
#s = models.Chats.query.all()
#for chat in reversed(s):
#flash('Question: ' + chat.question, 'Display')
#flash('Answer: ' + chat.answer , 'Display')
#flash('.', 'Display')
#u = models.Chats(question=data1, answer=display_ans)
#db.session.add(u)
#db.session.commit()
return redirect('/test')
return render_template("index2.html",
title = 'ChatterBot',
form = form)
