def loop(self):
ui.draw_all()
try:
while True:
if self.player.death:
if self.wizard:
if ui.prompt('Die? [yn]', 'yn') == 'n':
ui.new_ui_turn()
self.player.resurrect()
ui.draw_all()
continue
ui.prompt(
'[Game over: %s. Press ENTER]' % self.player.death,
[T.KEY_ENTER, T.KEY_KPENTER])
self.save_character_dump()
raise Quit()
if self.player.won:
ui.prompt(
'Congratulations! You have won. Press ENTER',
[T.KEY_ENTER, T.KEY_KPENTER])
self.save_character_dump()
raise Quit()
while self.player.action_turns > 0:
key = ui.readkey()
self.do_command(key)
self.map.do_turn(self.turns)
self.turns += 1
ui.draw_all()
except Quit:
pass
def askForever(question):
answer = ""
time = 0
ui.tell(question)
ui.prompt()
while True:
time = time + 1
answer = ui.listen()
if answer == "" and time < 4:
ui.tell("\n")
ui.tell(phrasebase.getReply("waiting"))
ui.prompt()
if answer == "" and time >= 4 and time < 7:
ui.tell("\n")
ui.tell(phrasebase.getReply("worried_waiting"))
ui.prompt()
if time >= 7 and time < 10:
continue
if time >= 10:
ui.tell("\n")
ui.tell("It seems that you are a bit distracted right now. Or maybe dead. ... ...I hope you are not dead. But because it's been a while since your last answer...")
ui.prompt()
if askbreak():
time = 0
ui.tell("So my question was: " + question)
ui.prompt()
continue
else:
goodbye()
break
if answer != "":
return answer
def processPDFs(dirname):
images = []
targets = []
# Choose to load old images or create new ones
options = ["Load", "Create"]
res = ui.prompt("Load pre-processed images or create new ones?", options)
filetype = ".bmp" if res == "0" else ".file"
# Select a type
message = None
if options[int(res)] == "Create":
message = "Select an visualization type:"
else:
message = "Which type of plot are these images?"
options = ["Byte Map", "Markov Plot"]
res = ui.prompt(message, options)
type = options[int(res)]
for file in pb.progressbar(os.listdir(dirname)): # Iterate through files
if file.endswith(filetype): # Check if right file type
filepath = os.path.join(dirname, file)
if filetype == ".bmp": # We are just loading an image here
images.append(cv2.imread(filepath))
targets.append(file[:5]) # Either "CLEAN" or "INFEC"
elif filetype == ".file": # Creating new images here
if type == "Byte Map":
images.append(create_bmp(filepath))
elif type == "Markov Plot":
images.append(create_markov(filepath))
cv2.imwrite("{}.bmp".format(filepath), images[-1])
targets.append(file[:5]) # Either "CLEAN" or "INFEC"
return images, targets, type
def train(data, targets, filenames):
targets = [val == "INFEC" for val in targets] # Set INFEC as positive val
# Choose training mode
options = ["Cross validation", "Build and test model"]
res = ui.prompt(options=options)
mode = options[int(res)]
# Choose ML algorithm
options = ["Support Vector Machine", "Random Forest",
"Decision Tree Classifier", "KNN"]
res = ui.prompt("Choose a ML algorithm:", options)
switch = {
0: svm.SVC(C=100., random_state=0),
1: RandomForestClassifier(n_estimators=50, max_depth=None, random_state=0),
2: DecisionTreeClassifier(random_state=0),
3: KNeighborsClassifier()
}
clf = switch.get(int(res))
if mode == "Cross validation":
model_evaluation(data, targets, clf)
elif mode == "Build and test model":
# Train model
clf.fit(data, targets)
# Get test dir
while True:
dirname = ui.prompt("Which directory are the test files in?")
if os.path.isdir(dirname):
break
print("ERROR: Directory not found.")
# Set up data/targets for test model
print("\n************************************")
print("* PREPARING MODEL FOR EVALUATION *")
print("************************************")
pageNames, y_true, filenames = pproc.process(dirname)
y_true = [val == "INFEC" for val in y_true] # Set INFEC as positive val
test_data = ft.features(pageNames)
y_pred = clf.predict(test_data)
save_filenames(y_true, y_pred, filenames)
conf_matrix = skm.confusion_matrix(y_true, y_pred)
accuracy = skm.accuracy_score(y_true, y_pred)
precision = skm.precision_score(y_true, y_pred, average=None)
recall = skm.recall_score(y_true, y_pred, average=None)
f1 = skm.f1_score(y_true, y_pred, average=None)
print("\n{}".format(conf_matrix))
print("Accuracy: {}".format(accuracy))
print("Precision: {}".format(precision[1]))
print("Recall: {}".format(recall[1]))
print("F1: {}".format(f1[1]))
def ainspect(): #function för att printa en del utav ui
ui.clear() #Clear call
ui.line(False) # printar en linje gjord utav *
ui.header("ARTIST DATABASE")
ui.line(True) # printar en linje gjord utav -
web.inspectart() #kallar på funktionen som tar fram informationen på specifika artister.
ui.line(False) # printar en linje gjord utav *
ui.echo("| Press enter to return ") # Printar våra val
ui.line(False) # printar en linje gjord utav *
ui.prompt()
def alist(): #function för att printa en del utav ui
ui.clear() #Clear call
ui.line(False) # printar en linje gjord utav -
ui.header("ARTIST DATABASE")
ui.line(True) # printar en linje gjord utav *
web.getart() #kallar på funktionen som tar fram listna av artister
ui.line(False) # printar en linje gjord utav -
ui.echo("| Press enter to return ") # Printar våra val
ui.line(False) # printar en linje gjord utav -
ui.prompt()
def train(data, targets, plotType, ftType):
options = [
"Support Vector Machine", "Random Forest", "Decision Tree Classifier",
"KNN"
]
res = ui.prompt("Choose a ML algorithm:", options)
clfType = options[int(res)]
switch = {
0: svm.SVC(C=100.),
1: RandomForestClassifier(max_depth=2),
2: DecisionTreeClassifier(),
3: KNeighborsClassifier()
}
clf = switch.get(int(res))
# Split-out test dataset and randomize order
seed = 42 # Seeded so as to create reproducible results
validation_size = 0.20
x_train, x_test, y_train, y_test = model_selection.train_test_split(
data, targets, test_size=validation_size, random_state=seed)
# Fit model
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
# Show confusion matrix for this fit
unique, counts = np.unique(y_pred, return_counts=True)
counts = dict(zip(unique, counts))
print("\nPredictions: {}".format(counts))
print("Confusion Matrix:\n{}".format(confusion_matrix(y_test, y_pred)))
# Cross validate and calculate scores
scoring = ["accuracy", "precision", "recall",
"f1"] # Choose scoring methods
targets = [val == "INFEC" for val in targets] # Set INFEC as positive val
scores = cross_validate(clf, data, targets, scoring=scoring, cv=5)
print("Scores calculated from 5-fold cross validation:")
print("Accuracy: {},\t{}".format(
round(np.mean(scores["test_accuracy"]), 4), scores["test_accuracy"]))
print("Precision: {},\t{}".format(
round(np.mean(scores["test_precision"]), 4), scores["test_precision"]))
print("Recall: {},\t{}".format(round(np.mean(scores["test_recall"]), 4),
scores["test_recall"]))
print("F1: {},\t{}".format(round(np.mean(scores["test_f1"]), 4),
scores["test_f1"]))
# Plot Learning Curve
options = ["Yes", "No"]
res = ui.prompt("Plot learning curve?", options)
if options[int(res)] == "Yes":
title = "Learning Curve ({}, {}, {})".format(clfType, ftType, plotType)
# plot_learning_curve(clf, title, data, targets, ylim=(0.7, 1.01), cv=5, n_jobs=4)
plot_learning_curve(clf, title, data, targets, cv=5, n_jobs=4)
plt.show()
def inspectart(): #funktionen som kombinerar urlen med artist Id så att vi kan komma åt djupare element i api'n.
url = 'https://5hyqtreww2.execute-api.eu-north-1.amazonaws.com/artists/'#tar samma url som innan.
r = requests.get(url) #sparar ned urlen i variabeln R
svar = r.json() #gör om json listan så att vi kan använda den.
artists = svar["artists"] #gör så att vi kan ta ut informationen med nyckel ordet Artists
val = ui.prompt() #kallar på promt funktionen så att vi kan göra ett val utav vilken artist vi vill åt.
for artist in artists: #itererar igenom våra artister så att vi kan komma längre in i dictionariet.
if val.title() == artist["name"]: #kollar så att artisten i loopen nu stämmer med vårat val.
ui.line(True) # printar en linje gjord utav -
Aid = artist["id"] #sparar ned Id's på våran matchande artist.
newurl= str(url + Aid) #tar Id's och kombinerar det med våran url
z = requests.get(newurl) #gör en request emot den nya urlen
inspection = z.json()#sparar ned json objektet så att programmet kan agera med det.
ui.echo("Name: " + inspection["artist"]["name"])
ui.echo(" ")
ui.echo("Genres")
for genre in inspection["artist"]["genres"]:#loopar och printar ut genres på våra artister
ui.echo(genre)
ui.echo(" ")
for years in inspection["artist"]["years_active"]:#loopar igenom och printar ut de åren som gruppen var aktiv
ui.echo("Years active: " + years)
print(" ")
ui.echo("Members")
for member in inspection["artist"]["members"]:#loopar igenom och printar ut de medlemar i bandet och deras stagenames, har de inte det så printar det bandnamnet.
ui.echo(member)
def features(pageNames):
"""Loop through images, extracting desired features"""
options = ["ORB", "SIFT", "LBP", "Gabor", "Entropy"]
res = ui.prompt("Choose a feature selection algorithm:", options)
switch = {
0: extract_ORB,
1: extract_SIFT,
2: extract_LBP,
3: extract_Gabor,
4: extract_Entropy,
}
fn = switch.get(int(res))
print("Should take less than {} minutes.".format(len(pageNames)*2))
print("Please wait...\n")
# Run this with a pool of 3 agents until finished
data = []
for pageName in pb.progressbar(pageNames):
gc.collect()
images = np.load(pageName)
# for image in images:
# data.append(fn(image))
with Pool(processes=3) as pool:
if len(data) == 0:
data = pool.map(fn, images, 16)
else:
data = np.concatenate((data, pool.map(fn, images, 16)))
# Remove paged files (to clear up disk space)
os.unlink(pageName)
return data
def main():
gc.collect() # Garbage collect
# Check arguments
if len(sys.argv) < 2:
return showUsage()
ui.clear()
# Pre-process PDFs
dirname = sys.argv[1]
images, targets, plotType = pproc.processPDFs(dirname)
# Extract feature vectors (until user quits)
doneExtracting = False
while not doneExtracting:
data, ftType = ft.extract_features(images)
# Create, train, and evaluate model (until user quits)
doneTraining = False
while not doneTraining:
tr.train(data, targets, plotType, ftType)
options = ["Try another model", "Extract new features", "Quit"]
res = ui.prompt(options=options)
if options[int(res)] == "Quit":
doneTraining = True
doneExtracting = True
elif options[int(res)] == "Extract new features":
doneTraining = True
gc.collect() # Garbage collect
def extract_features(images, vector_size=32):
options = ["ORB", "SIFT", "LBP", "Gabor", "Entropy", "LBP and Entropy"]
res = ui.prompt("Choose a feature selection algorithm:", options)
type = options[int(res)]
data = []
for img in pb.progressbar(images): # Process each image
if type == "ORB": # Corner features
alg = cv2.ORB_create()
descriptor_size = 32
data.append(
describe_keypoints(img, alg, vector_size, descriptor_size))
elif type == "SIFT": # Corner features (patented)
alg = cv2.xfeatures2d.SIFT_create()
descriptor_size = 128
data.append(
describe_keypoints(img, alg, vector_size, descriptor_size))
elif type == "LBP": # Simple texture recognition
alg = LocalBinaryPatterns(32, 16)
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
data.append(alg.describe(grey))
elif type == "Gabor":
# prepare filter bank kernels
kernels = []
for theta in range(4):
theta = theta / 8. * np.pi
for sigma in (1, 3):
for frequency in (0.05, 0.25):
kernel = np.real(
gabor_kernel(frequency,
theta=theta,
sigma_x=sigma,
sigma_y=sigma))
kernels.append(kernel)
shrink = (slice(0, None, 3), slice(0, None, 3))
img_shrink = img_as_float(cv2.cvtColor(img,
cv2.COLOR_BGR2GRAY))[shrink]
feats = compute_feats(img_shrink, kernels).flatten()
hist = exposure.histogram(img_shrink, nbins=16)
data.append(np.append(feats, hist))
elif type == "Entropy":
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
grey = entropy(grey, disk(5))
hist = exposure.histogram(grey, nbins=16)[0]
data.append(hist)
elif type == "LBP and Entropy":
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
alg = LocalBinaryPatterns(32, 16)
entropy_grey = entropy(grey, disk(5))
hist = exposure.histogram(entropy_grey, nbins=16)[0]
data.append(np.append(alg.describe(grey), hist))
else:
print("ERROR: Type " + type +
" not found (features.extract_features())\n")
return 1
return data, type
def numbQ():
global questions
ui.echo("Number of questions, 1 - 25")
questions = ui.prompt("Answer >")
if ValNum(questions, 26): #om nummret ligger mellan 1-25 returneras true
return questions #skickar tillbaka antalet frågor som ska finnas
else:
ui.echo("Invalid input, try again")
ui.line()
numbQ() #funktionen körs om ogiltigt svar kommer in
def numbOfQuestions():
global questions #gör en global variabel av questions för att vi använder den i och utanför funktionen
ui.echo("Number of questions, 1 - 25")
questions = ui.prompt("Answer >")
if isValidNumber(
questions, 26
): #om nummret ligger mellan 1-25 returneras true från isValidNummer
return questions #returnerar antalet frågor som ska finnas
else:
ui.echo("Invalid input, try again")
ui.line()
numbOfQuestions() #om ett ogiltigt svar anges körs funktionen om
def whatDiff(): #funktion för svårighetsgrad
ui.header("Difficulty")
ui.echo("1: Easy")
ui.echo("2: Hard")
difficulty = ui.prompt("Answer >")
if difficulty == "1":
APIget(easyAPI) #svar 1 ger lättare frågor
elif difficulty == "2":
APIget(hardAPI) #svar 2 ger svåra frågor
else:
ui.echo("Invalid input, try again")
ui.line()
whatDiff() #om något annat än 1 eller 2 anges så körs den om
def whatDifficulty(): #funktion för att välja svårighetsgrad
ui.header("Difficulty")
ui.echo("1: Easy")
ui.echo("2: Hard")
difficulty = ui.prompt("Answer >")
if difficulty == "1":
getApi(apiEasy) #om man svarar 1 så används apin med lättare frågor
elif difficulty == "2":
getApi(apiHard) #om man svarar 2 så används apin med svårare frågor
else:
ui.echo("Invalid input, try again")
ui.line()
whatDifficulty(
) #om något annat än 1 eller 2 anges så körsfunnktionen om
def main():
# Create images & extract features (until user quits)
doneExtracting = False
while not doneExtracting:
pageNames, targets, filenames = pproc.process(sys.argv[1])
data = ft.features(pageNames)
# Create and evaluate model (until user quits)
doneTraining = False
while not doneTraining:
tr.train(data, targets, filenames)
options = ["Try another model", "Extract new features", "Quit"]
res = options[int(ui.prompt(options=options))]
if res == "Quit":
doneTraining = True
doneExtracting = True
elif res == "Extract new features":
doneTraining = True
def process(directory):
"""Process each file in a directory, saving or loading images as directed"""
files = []
options = ["Load", "Create"]
choice = options[int(ui.prompt(options=options))]
for item in os.listdir(directory):
if os.path.isfile(os.path.join(directory, item)):
filename = os.path.join(directory, item)
if choice == "Load" and item.endswith(".png"):
files.append(filename)
elif choice == "Create" and item.endswith(".file"):
files.append(filename)
filenames, pageNames = imagePages(files, choice)
targets = [name.split('/')[-1][:5] for name in filenames]
return pageNames, targets, filenames
def imagePages(files, choice):
"""Pages images into npy file in groups of 100"""
options = ["Byte", "Markov", "Hilbert"]
type = options[int(ui.prompt("Choose a visualization type", options))]
targets = []
pageNames = []
pageSize = 100
pages = range(math.ceil(len(files)/pageSize))
for page in pb.progressbar(pages):
# print("\nPage {}/{}".format(page+1, len(pages)))
gc.collect() # Garbage collect
images = []
start = page*pageSize
if choice == "Create":
images, targets = buildImages(files[start:start+pageSize], targets, type)
elif choice == "Load":
images, targets = loadImages(files[start:start+pageSize], targets)
pageNames.append("./pages/images_page{}.npy".format(page))
np.save(pageNames[-1], images)
return targets, pageNames
def spell_check_this():
ui.clr()
spell_check_menu = ui.Menu("Spell Check This™")
spell_check_menu.display(False)
print("\n")
print("Word To Spell Check >> ")
word = input()
# normalize the input
word = word.upper()
if not DICTIONARY.is_word(word):
print("\n")
# spell check the word
suggestions = suggest_correction(word)
if ui.prompt("Verbose Logging?"):
print("\n")
print("DICTIONARY FORMAT (Suggested Word: Proximity Score):\n",
"red")
print(suggestions)
print("\n\n")
# print the suggestion with the lowest proximity score
print("Suggestion: ")
if suggestions:
print(min(suggestions, key=suggestions.get), "green")
else:
print("None, word is garbled.")
print("\n")
else:
print("\n")
print("Input is a valid word!\n", "green")
import ui # importerar ui
# använder funktioner från ui
ui.line() # gör linje
ui.header("EXEMPEL") # skriver ut i mitten och med | på sidorna
ui.line(True) # gör linje av sjärnor
ui.echo("Detta är ett exempel på hur") # skriver ut med en | från början
ui.echo("ett grännsnitt kan se ut.")
ui.line()
ui.header("..vad vill du göra?")
ui.line()
ui.echo("A | Visa inköpslista")
ui.echo("B | Lägg till vara")
ui.echo("C | Ta bort vara")
ui.echo("X | Stäng programmet")
ui.line()
ui.prompt("Val") # finns möjlighet att göra input
import ui # importerar ui och web
import web
# använder funktioner från ui
ui.line()
ui.header("ARTIST DATABASE")
ui.line()
ui.echo("Welcome to a world of")
ui.echo("Musice!")
ui.line()
ui.echo(" L | List artists")
ui.echo(" V | View artist profile")
ui.echo(" E | Exit application")
ui.line()
s = ui.prompt("Selection")
s
# urlen går genom get funktionen från web
url = "https://5hyqtreww2.execute-api.eu-north-1.amazonaws.com/artists/"
r = web.get(url)
# om man skriver in L
if s == "L" or s == "l":
ui.clear()
ui.line()
ui.header("ARTIST DATABASE")
ui.line()
ui.echo("Ariana Grande")
ui.echo("Avicii")
ui.echo("Blink -182")
ui.echo("Brad Paisley")
ui.echo("Ed Sheeran")
ui.echo("Imagine Dragons")
ui.echo("Maroon 5")
ui.header("Questions")
numbQ()
ui.line()
while True: #frågesporten körs tills antalet frågor har uppnåtts, då stoppas den
if count != int(questions):
count += 1 #antalet gånger programmet har ställt en fråga, lägger till 1 när en ny fråga ställs
NumbersGet()
ui.echo(replace(QA[index]["fråga"])) #frågan
ui.echo("Alternatives:")
AltGet(index) #hämtar alternativ
for i in alt:
if i == QA[index]["correct"]: #om rätt svar
correctAnsNum = integer #sparar det rätta värdets plats
ui.echo(replace(str(integer) + ": " + i)) #skriver ut alternativen
integer += 1
svar = ui.prompt("Answer >")
if ValNum(svar, 5): #om svaret är mellan 1 och 4
if alt[int(svar) - 1] == alt[correctAnsNum -
1]: #om svaret är korrekt
correctAns += 1 #lägger till ett poäng i minne
outpttxt(
"Your score: " + str(correctAns),
"Correct! Your answer was " +
str(correctAnsNum)) #anropar outpttxt som sätter fel/rätt
integer = 1
else:
outpttxt("Your score: " + str(correctAns),
"Wrong! Correct answer was " + str(correctAnsNum))
integer = 1
else:
outpttxt("Your score: " + str(correctAns),
def cmd_quit(self):
if ui.prompt('Quit? [yn]', 'yn') == 'y':
raise Quit()
else:
ui.new_ui_turn()
def main():
options = ["Threshold", "Select scanners"]
type = options[int(ui.prompt(options=options))]
dir = sys.argv[1]
queue = []
data = []
targets = []
min_pos = [1, 0, None]
max_neg = [0, 0, None]
scans = {}
for s in SCANNERS:
scans[s] = []
# Report / scan each file
for item in pb.progressbar(os.listdir(dir)):
if item.endswith(".bmp") or item.endswith(".png"):
continue
filename = os.path.join(dir, item)
file = open(filename, 'rb').read()
hash = hashlib.sha256(file).hexdigest()
res = report(hash)
if res["response_code"] == 0: # File not found
scan(filename, file)
queue.append([filename, hash])
elif res["response_code"] == -2: # Scan in Queue
queue.append([filename, hash])
elif res["response_code"] == 1: # File found
targets.append(item[:5])
if type == "Threshold":
data, max_neg, min_pos = thresholdReport(
res, item, targets, data, max_neg, min_pos)
elif type == "Select scanners":
for s in SCANNERS:
if s in res["scans"]:
scans[s].append(not res["scans"][s]["detected"])
else:
scans[s].append(False)
# Check up on items in the queue
while len(queue) > 0:
print("Left in Queue: {}".format(len(queue)))
for item in queue:
res = report(item[1])
code = res["response_code"]
if code == 0: # File not found
print("ERROR: File {} not enqueued.".format(item[0]))
elif code == 1: # File found
targets.append(item[:5])
if type == "Threshold":
data, max_neg, min_pos = thresholdReport(
res, item, targets, data, max_neg, min_pos)
elif type == "Select scanners":
for s in SCANNERS:
if s in res["scans"]:
scans[s].append(not res["scans"][s]["detected"])
else:
scans[s].append(False)
queue.remove(item)
if type == "Threshold":
data = np.array(data)
y_pred = data[:, 0] # Just first column of data[]
y_true = [val == "CLEAN"
for val in targets] # Set INFEC as positive val
conf_matrix = skm.confusion_matrix(y_true, y_pred)
accuracy = skm.accuracy_score(y_true, y_pred)
precision = skm.precision_score(y_true, y_pred, average=None)
recall = skm.recall_score(y_true, y_pred, average=None)
f1 = skm.f1_score(y_true, y_pred, average=None)
print("\n{}".format(conf_matrix))
print("Accuracy: {}".format(accuracy))
print("Precision: {}".format(precision[0]))
print("Recall: {}".format(recall[0]))
print("F1: {}".format(f1[0]))
print("Max Negative: {}\nMin Positive: {}".format(max_neg, min_pos))
else:
for s in SCANNERS:
print("\nResults for {}:".format(s))
data = np.array(scans[s])
y_pred = data
y_true = [val == "CLEAN"
for val in targets] # Set INFEC as positive val
conf_matrix = skm.confusion_matrix(y_true, y_pred)
accuracy = skm.accuracy_score(y_true, y_pred)
precision = skm.precision_score(y_true, y_pred, average=None)
recall = skm.recall_score(y_true, y_pred, average=None)
f1 = skm.f1_score(y_true, y_pred, average=None)
print(conf_matrix)
print("Accuracy: {}".format(accuracy))
print("Precision: {}".format(precision[0]))
print("Recall: {}".format(recall[0]))
print("F1: {}".format(f1[0]))
url = "https://5hyqtreww2.execute-api.eu-north-1.amazonaws.com/artists/"
artists = web.get(
"https://5hyqtreww2.execute-api.eu-north-1.amazonaws.com/artists/"
)["artists"] #artists blir en variabel av svaret man får från apin som skickas till web.py och funktionen get
ui.line()
ui.header("ARTIST DB")
ui.line()
ui.echo("Welcome to a world of")
ui.echo("Music!")
ui.line()
ui.echo(" L | List artists")
ui.echo(" V | View artist profile")
ui.echo(" E | Exit application")
ui.line()
selection = ui.prompt(
"Selection >") #en input av användaret som bestämmer vad man vill göra
while True:
if selection.lower() == "l": #om inputen är l
ui.line()
ui.header("ARTIST DB")
ui.line()
for i in artists: #för varje plats i listan artists
ui.echo(i["name"])
ui.line(True)
ui.echo(" L | List artists")
ui.echo(" V | View artist profile")
ui.echo(" E | Exit application")
ui.line()
selection = ui.prompt(
"Selection >") #tar in en ny input så att programmet kan fortsätta
artistsArr = requests.get(idUrl)
artistJson = artistsArr.json()["artists"]
for artist in artistJson:
artists.append(artist["name"])
while True != False:
ui.line(True)
ui.header("Välkommen till artist-wiki")
ui.line(True)
nummer = 0
for art in artists:
ui.echo(art + " ----- " + str(nummer))
nummer += 1
u_inp = ui.prompt(
"Ange en artists nummer eller 'exit' om du vill avsluta programmet> ")
if "exit" in u_inp.lower():
break
else:
try:
a_artist = int(u_inp)
ui.line()
ui.echo("Du har valt " + artists[a_artist])
dinArtist = artistJson[a_artist]["id"]
getIdUrl = idUrl + dinArtist
artArr = requests.get(getIdUrl)
import ui #importerar ui.py
ui.line()
ui.header("Exempel")
ui.line(True)
ui.echo("Detta är ett exempel på hur")
ui.echo("ett gränssnitt kan se ut")
ui.line()
ui.header("..vad vill du göra?")
ui.line()
ui.echo("A | Visa inköpslista")
ui.echo("B | Lägg till vara")
ui.echo("C | Ta bort vara ")
ui.echo("X | Stäng programmet")
ui.line()
ui.prompt("Val")
#anrop till funktioner i ui.py
import ui #importerar våra tidigare filer
import web
ui.line()
ui.header("ARTIST DATABASE")
ui.line()
ui.echo("Welcome to a world of")
ui.echo("Music!")
ui.line()
ui.echo("L", "List artists")
ui.echo("V", "View artist profile")
ui.echo("E", "Exit application")
a = ui.prompt("Selection")
running = "yes" #sätter någonting vi kan loopa med
while running == "yes":
b = web.get(
"https://5hyqtreww2.execute-api.eu-north-1.amazonaws.com/artists/"
) #tidigare url:en, b kommer alltid att uppdateras när vi frågar efter information
if a.title() == "L": #om valet är l eller L
ui.line()
ui.header("ARTIST DATABASE")
ui.line()
for x in b["artists"]: #för alla värden i artists
ui.echo(x["name"]
) #letar den efter namn och skriver ut det som finns där
ui.line(True)
ui.echo("L", "List artists")
ui.echo("V", "View artist profile")
ui.echo("E", "Exit application")
def askStory(story):
#if story type is already explained, go on
#otherwise, explain
#intro
if not domain.getExplained(story.group):
ui.tell(phrasebase.getReply("new_topic"))
ui.tell(domain.groups[story.group])
answer = askForever(phrasebase.getReply("want_topic_explanation"))
meaning = parsing.parse(answer)
if meaning == "yes":
ui.tell(domain.getExplanation(story.group))
domain.setExplained(story.group)
ui.tell("Now let's get to the question!")
if meaning == "no":
ui.tell("Ok, then let's get right to the question.")
ui.tell(story.text)
ui.tell(story.question)
ui.prompt()
answer = ui.listenLong()
meaning = parsing.parseQuiz(answer, story)
# if answer == "":
# ui.tell(phrasebase.getReply("offer_hint"))
# ui.prompt()
# answer = ui.listen()
# meaning = parsing.parse(answer)
# if meaning == "yes":
# ui.tell(story.hint)
# ui.prompt()
# if meaning == "no":
# ui.tell("ok!")
# answer = askForever(phrasebase.getReply("introbla") + story.question)
# meaning = parsing.parse(answer)
if meaning == "whatquestion":
ui.tell("The question was: " + story.question)
# if meaning == "hint":
# ui.tell(story.hint)
# answer = askForever(phrasebase.getReply("introbla") + story.question)
if meaning == "explain":
ui.tell(phrasebase.getReply("offer_explanation"))
ui.prompt()
answer = ui.listen()
meaning = parsing.parse(answer)
if meaning == "yes":
ui.tell(story.explain)
#explained the answer
#student model: didn't know the answer
return
if meaning == "no":
ui.tell("ok!")
answer = askForever(phrasebase.getReply("introbla") + story.question)
meaning = parsing.parse(answer)
if meaning == "correct":
ui.tell(phrasebase.getReply("correct"))
#student model: knew the answer
elif meaning == "incorrect":
ui.tell(phrasebase.getReply("incorrect"))
ui.tell(phrasebase.getReply("offer_explanation"))
ui.prompt()
answer = ui.listen()
meaning = parsing.parse(answer)
if meaning == "yes":
ui.tell(story.explain)
#explained the answer
#student model: didn't know the answer
return
ui.line(False) # printar en linje gjord utav *
ui.header("ARTIST DATABASE")
ui.line(True) # printar en linje gjord utav -
web.inspectart() #kallar på funktionen som tar fram informationen på specifika artister.
ui.line(False) # printar en linje gjord utav *
ui.echo("| Press enter to return ") # Printar våra val
ui.line(False) # printar en linje gjord utav *
ui.prompt()
while True: #kör en stor while sålänge som end variabeln inte innehåller
ui.clear() #Clear call
ui.line(False) #printar en linje gjord utav -
ui.header("Welcome to the Music Library") #printar en header
ui.line(False)#printar en linje gjord utav -
ui.line(False)#printar en linje gjord utav -
ui.echo("| L | Lista alla Artister") #Printar våra val
ui.echo("| V | Inspektera Artist ")#Printar våra val
ui.echo("| E | Stäng av ")
ui.line(False)
choice = ui.prompt()#variable input så att du kan kalla på
if choice == "L": #gör så att vi kan välja olika val
alist() #kallar på functionen alist
if choice == "V": #gör så att vi kan välja olika val.
ainspect() #kallar på functionen ainspect
if choice == "E":
break
