def main():
q1 = questions.Question(
"What year did the Beatles make it big in america? ", "A. 1965",
"B. 1945", "C, 1956", "D, 1964")
q2 = questions.Question(
"Which famous musician from the Beatles was murdered in 1980? ",
"A. Ringo Starr", "B. John Lennon", "C. George Harrison",
"D. Geezer Butler")
q3 = questions.Question("How many sides does a hexagon have?"
"A. 4", "B. 6", "C. 9", "D. 7")
q4 = questions.Question(
"Who wrote the Harry Potter series?"
"A. J.K. Rowling", "B. Stephen King", "C. George R. R. Martin",
"D. Thomas Kinkaid")
q5 = questions.Question(
"Whos the main character of the Harry Potter series?"
"A. Tigger", "B. John Wayne Gacy", "C. Harry Potter", "D. Morpheus")
q6 = questions.Question(
"Which band wrote the hit song: T.N.T?"
"A. Led Zeppelin", "B. AC/DC", "C. The Ramones", "D. The Who")
q7 = questions.Question(
"How many Queens of the Stone Age albums are there?"
"A. 4", "B. 9", "C. 7", "D. 6")
q8 = questions.Question(
"Who wrote the Game of Thrones books?"
"A. Stephen King", "B. JK Rowling", "C. James Patterson",
"D. George RR Martin")
q9 = questions.Question(
"The Beatles kick started which musical explosion?"
"A. The British Invasion", "B. The Grunge scene"
"C. The Garage Rock Revival scene", 'D. Glam Rock')
q10 = questions.Question(
"What band was did the late Lemmy Kilminster form?"
"A. Metallica", "B. Motorhead", "C. Opeth", "D. Kyuss")
all_questions = (q1, q2, q3, q4, q5, q6, q7, q8, q9, q10)
print("* Player 1 *")
player1 = ask(all_questions)
print("* Player 1 *")
player2 = ask(all_questions)
if player1 == player2:
print("You two are now tied!")
elif player1 > player2:
print("Player 1 is winner of this quiz!")
else:
print("Player 2 is the winner of this quiz!")
def main(): # make questions
q1 = questions.Question("What is 2+2?", "A. fish", "B. 5", "C. 4", "D. All of the above", "C")
q2 = questions.Question("What is the best food ever created?", "A. pizza", "B. sushi", "C. cheeseburger", "D. Spaghetti", "A")
q3 = questions.Question("What is the capital of Illinois?", "A. chicago", "B. crystal lake", "C. springfield", "D. nashville", "C")
q4 = questions.Question("What sport do the Packers play?", "A. baseball", "B. football", "C. hockey", "D. tennis", "B")
q5 = questions.Question("Who is our current president?", "A. Obama", "B. George W. Bush", "C. Teddy Roosevelt", "D. Trump", "D")
q6 = questions.Question("What does, Je parler en Francais, mean?", "A. I speak French", "B. I am French", "C. I love French", "D. Nothing", "A")
q7 = questions.Question("What country is Illinois part of?", "A. Canada", "B. USA", "C. Mexico", "D. China", "B")
q8 = questions.Question("What country touches the USA?", "A. Russia", "B. Thailand", "C. Canada", "D. Brazil", "C")
q9 = questions.Question("Who did Trump beat for the presidency?", "A. Clinton", "B. The Rock", "C. Romney", "D. Obama", "A")
q10 = questions.Question("What is Meijer?", "A. A food brand", "B. A school", "C. A computer", "D. A retail store", "D")
player1 = 0 # initial scores set to 0
player2 = 0
set_1 = [q1, q3, q5, q7, q9] # sets of questions
set_2 = [q2, q4, q6, q8, q10]
print("player1: ") # giving player 1 set 1
for query in set_1:
print("\n")
print(query.get_question())
print(query.get_a1())
print(query.get_a2())
print(query.get_a3())
print(query.get_a4())
guess = input("Please enter the letter for your answer: ").upper().strip(" ")
if guess == query.get_answer():
print("You're right!") # if correct then print so
player1 += 1
print("Player 1 earned:", str(player1), "point(s).")
print("player2: ")
for query in set_2:
print("\n")
print(query.get_question())
print(query.get_a1())
print(query.get_a2())
print(query.get_a3())
print(query.get_a4())
guess = input("Please enter the letter for your answer: ").upper().strip(" ")
if guess == query.get_answer():
print("You're right!")
player2 += 1
print("Player 2 earned:", str(player2), "point(s).")
def __init__(self):
logging.basicConfig()
self.loop_flag = True
self.bot_state = 0
self.QuestionObject = Q.Question()
trial1 = Pipeline([
('vectorizer',
TfidfVectorizer(tokenizer=stemming_tokenizer,
stop_words=stopwords.words('english'))),
('classifier', MultinomialNB()),
])
self.detector0 = train(trial1, xtrain, ytrain, xtest, ytest)
self.sentiment = sent.classifier()
def get_questions(players):
# create list for players questions and answers
player = []
# asks player for their questions and answers
print(players + ": Please enter 5 questions and answers")
for create in range(1, 6):
question = input("Question #" + str(create) + ": ")
ans1 = input("A: ")
ans2 = input("B: ")
ans3 = input("C: ")
ans4 = input("D: ")
# asks user to input the correct answer
correct = input("Which letter is the correct answer? ")
# upper case the letter
correct = correct.upper()
# error loop for correct answer
while correct != 'A' and correct != 'B' and correct != 'C' and correct != 'D':
print()
print("Error, Correct answer does not match any \n" +
"of the assign answers. Please try again.")
correct = input("Which letter is the correct answer? ")
correct = correct.upper()
print()
# sends question and answers to class
trivia = questions.Question(question, ans1, ans2, ans3, ans4, correct)
# append player 1 list
player.append(trivia)
# change player1 list to a tuple
player = tuple(player)
# clears screen for next player
for x in range(100):
print()
# return player tuple
return player
def mkQuestion(self, row):
try:
question, a, b, c, d, e, ca = row
except ValueError:
self.errors.append(
(row[0], "Wrong number of columns for this row."))
return
except Exception as e:
self.errors.append((row[0], e))
# munge a little
ca = ca.lower()
ansList = [i.strip() for i in [a, b, c, d, e] if i]
try:
self.ql.append(
questions.Question(question, ansList, ca, self.st,
self.getOrd()))
except questions.DuplicateError:
self.errors.append((question, "This question is a duplicate."))
except questions.QuestionFormatError as e:
self.errors.append((question, e))
def upload_q(user, window):
#window for uploading questions
window.destroy()
upload_q_window = tk.Tk()
upload_q_window.title('Upload Question')
upload_q_window.geometry('700x500')
upload_q_window.config(bg=bg)
Label(upload_q_window,
text="Enter Question:",
font=("Ariel black", 16),
bg=bg).grid()
question = tkst.ScrolledText(upload_q_window, width=35, height=5)
question.place(x=0, y=30)
Label(upload_q_window,
text="Give Description(optional):",
font=("Ariel black", 16),
bg=bg).place(x=0, y=120)
description = tkst.ScrolledText(upload_q_window, width=35, height=10)
description.place(x=0, y=160)
Label(upload_q_window,
text="Provide input if neccessary",
font=("Ariel black", 16),
bg=bg).place(x=0, y=330)
input_area = tkst.ScrolledText(upload_q_window, width=35, height=5)
input_area.place(x=0, y=360)
back_button = tk.Button(upload_q_window,
text="Back Home",
width=20,
height=2,
bg='powder blue',
command=lambda: Profile(user, upload_q_window))
back_button.place(x=0, y=450)
Label(upload_q_window,
text="Provide Solution",
font=("Ariel black", 16),
bg=bg).place(x=310, y=0)
solution = tkst.ScrolledText(upload_q_window, width=35, height=25)
solution.place(x=310, y=30)
Label(upload_q_window, text="Title: ", font=("Ariel black", 16),
bg=bg).place(x=310, y=445)
title_entry = Entry(upload_q_window)
title_entry.place(x=360, y=450)
submit_button = Button(
upload_q_window,
text="Submit",
bg="powder blue",
width=15,
font=("Ariel black", 15),
command=lambda: qt.Question(
user, upload_q_window, question.get('1.0', END),
solution.get('1.0', END), description.get('1.0', END),
title_entry.get(), input_area.get('1.0', END)))
submit_button.place(x=500, y=440)
def migrate():
if not os.path.exists("migrated_questions_data"):
os.mkdir("migrated_questions_data")
files = glob.glob("migrated_questions_data/*")
for f in files:
os.remove(f)
files = glob.glob("questions_data/*")
for f in files:
m = re.search("(\d{12})\.(\d+)\.question\.xml", f)
version = int(m.group(2))
tree = etree.parse(f)
question_id = tree.xpath("/*")[0].get("id")
sourceId = tree.xpath("/*/source_id")[0].text
developerName = tree.xpath("/*/developer_name")[0].text
developerEmailAddress = tree.xpath("/*/developer_email")[0].text
language = tree.xpath("/*")[0].get("language")
dialect = tree.xpath("/*")[0].get("dialect")
calendar = tree.xpath("/*")[0].get("calendar")
currency = tree.xpath("/*")[0].get("currency")
unitSystem = tree.xpath("/*")[0].get("unit_system")
title = ""
if len(tree.xpath("/*/title")) > 0:
title = tree.xpath("/*/title")[0].text
parts = [
e for e in tree.xpath("/*/*")
if e.tag in ["statement", "mcq", "mrq", "frq"]
]
question = questions.Question()
question.id = question_id
question.version = version
question.lastModificationDate = datetime.datetime.now()
question.attribution.sourceId = sourceId
question.addDeveloper("writer", developerName, developerEmailAddress)
if language == "en":
question.language.name = "English"
if dialect == "american":
question.addDialect("American")
if dialect == "british":
question.addDialect("British")
if dialect == "american british":
question.addDialect("American")
question.addDialect("British")
if unitSystem == "si":
question.addUnitSystem("SI")
if unitSystem == "usc":
question.addUnitSystem("USC")
if unitSystem == "si usc":
question.addUnitSystem("SI")
question.addUnitSystem("USC")
question.title = title
partMap = {
"statement": "information",
"mcq": "mcq",
"mrq": "mcq",
"frq": "frq"
}
for part in parts:
p = question.addPart(partMap.get(part.tag, ""))
if part.tag == "mcq" or part.tag == "mrq":
rp = p.addChoicesResponseFormat()
keysAndDistractors = [
e for e in part.xpath("./*")
if e.tag in ["key", "distractor"]
]
for kd in keysAndDistractors:
choice = rp.addChoice()
choice.isCorrectAnswer = True if kd.tag == "key" else False
question.save("migrated_questions_data/" + question.id +
".question.xml")
class Bot:
loop_flag = False
QuestionObject = Q.Question()
bot_state = None
name = None
detector0 = None
movie = None
sentiment = None
skip_state = False
state_one_visited = False
def __init__(self):
logging.basicConfig()
self.loop_flag = True
self.bot_state = 0
self.QuestionObject = Q.Question()
trial1 = Pipeline([
('vectorizer',
TfidfVectorizer(tokenizer=stemming_tokenizer,
stop_words=stopwords.words('english'))),
('classifier', MultinomialNB()),
])
self.detector0 = train(trial1, xtrain, ytrain, xtest, ytest)
self.sentiment = sent.classifier()
def conversationNotOver(self):
return self.loop_flag
def endConversation(self):
self.loop_flag = False
def question(self):
if self.bot_state == 1:
if not self.state_one_visited:
self.state_one_visited = True
return self.QuestionObject.returnquestion(
self.bot_state
) + " " + str(
self.name
) + ". Do you want to know about a movie or want me to suggest something you would like to watch ? "
else:
return " Do you want to know about a movie or want me to suggest something you would like to watch ? "
return self.QuestionObject.returnquestion(self.bot_state)
def extractnames(self, sentences):
names = []
for tagged_sentence in sentences:
for chunk in nltk.ne_chunk(tagged_sentence):
if type(chunk) == nltk.tree.Tree:
if chunk.label() == 'PERSON':
names.append(' '.join([c[0] for c in chunk]))
return names
def preprosess(self, response):
stop = stopwords.words('english')
response = ' '.join([i for i in response.split() if i not in stop])
sentences = nltk.sent_tokenize(response)
sentences = [nltk.word_tokenize(sent) for sent in sentences]
sentences = [nltk.pos_tag(sent) for sent in sentences]
if self.bot_state == 0:
names = self.extractnames(sentences)
if len(names) == 0:
print "Movie Bot : Sorry , I couldn't get you , Could you please tell me your name?"
self.name = raw_input('You : ')
else:
self.name = names[0]
if self.bot_state == 1:
label = self.detector0.predict([str(response)])
if label == 0:
print "Movie Bot : Could you please tell the movie name?"
moviename = raw_input('You : ')
self.movie = moviename
url = "http://www.omdbapi.com/?t=" + str(moviename)
json = requests.get(url).json()
try:
print "Movie Bot : " + "The title of the movie is " + (
json['Title']) + ". It was released in the year " + (
json['Year']) + " and is a " + (
json['Genre']
) + " movie. It is directed by " + (
json['Director']
) + ". It has the following awards :" + (
json['Awards'])
except:
print "Movie Bot : Sorry I couldn't understand"
self.skip_state = True
elif label == 1:
genre = self.detectgenre(response)
if genre in movies:
self.movie = movies[genre][random.randint(
0, (len(movies[genre]) - 1))]
print "Movie Bot : I suggest you to watch " + self.movie
else:
self.movie = movies['action'][random.randint(
0, (len(movies[genre]) - 1))]
print "Movie Bot : I suggest you to watch " + self.movie
if self.bot_state == 2:
if response.lower() == 'yes' or response.lower() == 'yeah':
url = "https://api.nytimes.com/svc/movies/v2/reviews/search.json?api-key=73073227b3f343a384cb585053cd5a32&critics-pick=N&query=" + str(
self.movie)
try:
json = requests.get(url).json()
review_url = str(
json['results'][0]['link']['url']).replace(
'http://www.', 'https://')
except:
print "Sorry I am not able to fetch the review for that movie"
return
print "Movie Bot : You can read the complete review here . " + str(
review_url)
review = requests.get(review_url)
soup = BeautifulSoup(review.text, 'html.parser')
soup_review = soup.find_all(
"p", class_="story-body-text story-content")
review_final = ''
for x in soup_review:
review_final = review_final + str(
unicodedata.normalize('NFKD', x.get_text()).encode(
'ascii', 'ignore'))
sent = self.sentiment.classify({'a': review_final})
if sent == 'pos':
print "Movie Bot : After analysing the review, I think it is a good movie "
if sent == 'neg':
print "Movie Bot : After analysing the review, I think it is not a good movie "
def detectgenre(self, sentence):
url = "https://westus.api.cognitive.microsoft.com/luis/v2.0/apps/ddba8f91-8175-4878-a28e-a91519272397?subscription-key=23183b4aa8e64c2bbb20bdc225dc257e&verbose=true&q=" + sentence
try:
json = requests.get(url).json()
except:
print " I am not able to reach my server right now"
print "Movie Bot : Can you please tell me which genre movies do you like? "
genre = raw_input("You : ")
return genre
def next_state(self):
if not self.skip_state:
if self.bot_state == 2:
self.bot_state = 0
self.bot_state = self.bot_state + 1
else:
self.skip_state = False