def drawGameOver():
pygame.draw.rect(screen, (163, 196, 220),
(width // 4, 100, width // 2, height - 200))
pygame.draw.rect(screen, (0, 52, 114),
(width // 4, 100, width // 2, height - 200), 4)
textCenterX = width / 2
textCenterY = 200
createText("Game Over", textCenterX, textCenterY, 40)
textCenterY += 60
totalScore = score.calculateScore()
createText(f"Total Score: {totalScore}", textCenterX, textCenterY, 30)
textCenterY += 60
createText("Summary:", textCenterX, textCenterY, 30)
for key in score.pointsDict:
textCenterY += 40
caption = f"{key[0].upper() + key[1:]}: {score.pointsDict[key]}"
createText(caption, textCenterX, textCenterY, 20)
def interact(self,
username=None,
interviewId=None,
answer=None,
mode=0,
emotion={},
previousQuestion=""):
'''
mode determines where to run the chatbot on the console or the as web api
mode=0 web
mode=1 console
'''
#beginInterview(username,interviewId)
#question=""
#print(emotion)
if mode == 0:
#answer=input("user :"******""
while notasked:
if times < 5:
question = self.kernel.respond(answer)
notasked = checkIfQuestionAlreadyAsked(
interviewId, question)
times = times + 1
else:
break
if times == 5:
question = self.kernel.respond("ASK QUESTION")
print(question)
sentiment = self.sentiment(answer)
textAnalysis = self.textAnalysis(answer)
#print("Bot: ",question)
#saveInterview(interviewId,previousQuestion,answer,0.5)
#previousQuestion=question
q = getQuestionDetails(previousQuestion)
if q is not None:
evaluable = q.evaluable
keywords = list(q.keywords)
#print(keywords)
#print(q.question)
#print("Chatbot ",q.keywords,type(keywords))
else:
evaluable = False
keywords = None
score = calculateScore(emotion=emotion,
sentiment=sentiment,
lexical=textAnalysis["Lexical"],
evaluable=evaluable,
keywords=keywords,
answer=answer)
#print(score)
#print("Bot: ",question)
saveInterview(interviewId,
previousQuestion,
answer,
score=score,
emotion=emotion,
sentiment=sentiment,
textAnalysis=textAnalysis)
response = {}
response["question"] = question
response["sentiment"] = sentiment
response["textAnalytics"] = textAnalysis
response["score"] = score
response["previousQuestion"] = previousQuestion
response["answer"] = answer
#print(response)
return response
else:
while True:
print("==" * 10)
answer = input("user :"******"neutral": random.randint(1, 1000),
"sad": random.randint(1, 100),
"fear": random.randint(1, 100),
"disgust": random.randint(1, 100),
"anger": random.randint(1, 100),
"happy": random.randint(1, 500),
"suprise": random.randint(1, 100)
}
textAnalysis = self.textAnalysis(answer)
q = getQuestionDetails(previousQuestion)
if q is not None:
evaluable = q.evaluable
keywords = list(q.keywords)
#print("Chatbot ",q.keywords,type(keywords))
else:
evaluable = False
keywords = None
score = calculateScore(emotion=emotion,
sentiment=sentiment,
lexical=textAnalysis["Lexical"],
evaluable=evaluable,
keywords=keywords,
answer=answer)
#print(score)
print("Bot: ", question)
saveInterview(interviewId,
previousQuestion,
answer,
score=score,
emotion=emotion,
sentiment=sentiment,
textAnalysis=textAnalysis)
previousQuestion = question
#while True:
# iterate over requests and try to cache them
with tqdm(total=len(requests_sorted)) as pbar:
for request in requests_sorted:
pbar.update(1)
# add video to the closest cache to the request endpoint with enough free space
if cacheRequest(videos, caches, endpoint_caches, request):
continue
# not able to cache any request => we are done
# break
return caches
filename = sys.argv[1]
(cache_count, cache_capacity, videos, endpoints,
requests) = read.read(filename)
caches_state = solve(cache_count, cache_capacity, videos, endpoints, requests)
# SCORE
score = score.calculateScore(caches_state, requests, endpoints)
print("SCORE = " + str(score))
# SUBMISSION FORMAT
used_caches = [(i, cache) for i, cache in enumerate(caches_state)
if len(cache) > 0]
# print(len(used_caches))
# for i, cache in used_caches:
# print(str(i) + " " + " ".join(str(x) for x in cache))
def getBuzzyCompanies(number, a_id, date, ilvl):
hdr = {
'User-Agent':
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept':
'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'
}
obj = {}
fintech_obj = {}
all_scores = []
for accelerator in a_id:
url = "https://www.seed-db.com/accelerators/viewall?acceleratorid=" + str(
accelerator)
req = urllib2.Request(url, headers=hdr)
try:
page = urllib2.urlopen(req)
except urllib2.HTTPError, e:
print e.fp.read()
contents = page.read()
root = html.fromstring(contents)
max_array = []
min_array = []
print("getting id " + str(accelerator))
for i in root.xpath('//*[@id="seedcos"]/tbody/tr'):
return_object = []
return_object.append("")
crunchbase = i.xpath('td[3]/div/button[2]/a/@href')
if not crunchbase:
return_object.append("")
else:
return_object.append(crunchbase[0])
box = i.xpath('td')
for k in box:
info = "".join(k.itertext())
info = "".join(info.split()).replace("\n", "")
return_object.append(info)
# return_object: [angellist url = "", crunchbase url, exited?, name, url, acceleration date, exit value, ?, funding]
print(return_object)
skip = False
if (date != ""):
a_date = int(return_object[5].split("/")[1])
if (a_date < date):
skip = True
if (skip == False):
if (return_object[2] == "" and return_object[4] != "None"
and return_object[1] != ""):
pass_object = [
return_object[1], return_object[4], return_object[8],
return_object[3]
] #[cb url, url, funding, name]
reutrn_val = score.calculateScore(pass_object)
print(reutrn_val)
if (reutrn_val[0] != 0):
string = return_object[3] + ": " + str(
int(reutrn_val[0]))
all_scores.append(string)
obj[return_object[3]] = int(reutrn_val[0])
interestLvl = reutrn_val[8]
if (interestLvl > ilvl):
fintech_obj[return_object[3]] = int(reutrn_val[0])
file = open("./data/list.csv", "a+")
file.write(string + ",")
file.close()
print("all:")
print(all_scores)
else:
print("skipping " + return_object[3])
else:
print("year is too early for " + return_object[3])