def likeAPerson():
time.sleep(5)
for key, value in api.get_recommendations().items():
match = []
if (key == "results"):
for person in value:
rating = 0
person_id = ""
name = ""
for key, value in person.items():
if (key == "_id"):
person_id = value
time.sleep(5)
if key == 'photos' and value:
if len(value) >= 2:
rating += 1
if key == 'name':
name = value
if rating > 0:
print("Like on : ", name)
match = api.like(person_id)
likes_remaining = match["likes_remaining"]
if likes_remaining == 0:
print("Acabaram os likes, terminei por hoje")
quit(0)
print(match)
else:
print("Unlike: ", name)
def matches():
if tinder.authverif() == True:
matches = tinder.get_recommendations()
return matches
else:
print('Sorry, something went wrong')
return None
def start_session():
api.authverif()
likes_remaining = True
num_likes = 0
num_dislikes = 0
while likes_remaining:
# Retrieve recommended profiles from Tinder.
for person in api.get_recommendations()['results']:
person_id = person['_id']
rating = rater.get_rating(person)
if hot_or_not(rating):
if not _like(person_id):
likes_remaining = False
break
num_likes += 1
else:
_dislike(person_id)
num_dislikes += 1
print(f"Swiping: {num_likes} likes, {num_dislikes} dislikes",
end='\r')
now = datetime.now()
print(
f"{now.strftime('%H:%M : %d/%m/%Y')} Session complete: {num_likes} likes, {num_dislikes} dislikes"
)
def recommend():
if not tinder_api.authverif():
login()
recommendations = tinder_api.get_recommendations(
) # output json {status='', results=[profile]}
# print(recommendations['results']) # get list of profiles
# print(recommendations['results'][0])
return render_template('recommend.html')
def recGen(ageMin=18, ageMax=22, distance=100):
ti.change_preferences(age_filter_min=ageMin,
age_filter_max=ageMax,
distance_filter=distance)
recommendations = ti.get_recommendations()
df = pd.DataFrame.from_dict(recommendations["results"])
df = df[['_id', 'name', 'gender', 'birth_date', 'bio', 'photos']]
df = df.rename(index=str, columns={'_id': 'ID', 'birth_date': 'dob'})
imgurls = []
for i in df['photos']:
imgurls.append(i[0]['processedFiles'][0]['url'])
df = df.drop(['photos'], axis=1)
df['imgurl'] = pd.Series(imgurls).values
df["dob"] = df["dob"].apply(lambda x: calculate_age(x))
df = df.rename(index=str, columns={"dob": "age"})
return df
if __name__ == '__main__':
if api.authverif() == True:
print("Gathering Data...")
#match_info = get_match_info()
targetAmount = 500
currentAmount = 0
cycle = 0
start_time = time.time()
while currentAmount < targetAmount:
if cycle > 0:
pause()
cycle += 1
progress = round((currentAmount / targetAmount) * 100, 2)
print(str(progress) + "% Running cycle #" + str(cycle) + "...")
people = api.get_recommendations()
n = findPeople(people)
currentAmount += n
if n == 0:
choice = input(
"Didn't receive any new records. Wanna continue?")
if not choice == 'y':
print("Aborting.")
break
print("Job completed in " + str(cycle) + " cycles, " +
str(datetime.timedelta(seconds=(time.time() - start_time))) +
". Added a total of " + str(currentAmount) +
" people to the datafile.")
else:
print("Something went wrong. You were not authorized.")
loop += 1
print(loop)
if EXISTS <= 0:
if all([location["visited"] for location in LOCATIONS]):
print("un-visiting locations...")
for location in LOCATIONS:
location["visited"] = False
for location in LOCATIONS:
if not location["visited"]:
print("Moving to {0}".format(location["label"]))
tinder_api.update_location(location["lat"], location["lon"])
location["visited"] = True
EXISTS = 5
CURRENT_LOCATION = location["label"]
break
r = tinder_api.get_recommendations()
try:
recommendations = r["results"]
except KeyError as e:
print(r)
raise e
print("Fetching {0} recommendations".format(len(recommendations)))
for recommendation in recommendations:
recommendation["t_id"] = recommendation.pop("_id")
try:
Human.objects.get(t_id=recommendation["t_id"])
except Human.DoesNotExist:
try:
human = Human(**recommendation)
except Exception as e:
raise e
sub_img, int(np.floor((side - sub_img.shape[1]) / 2)),
int(np.ceil((side - sub_img.shape[1]) / 2)),
int(np.floor((side - sub_img.shape[0]) / 2)),
int(np.ceil((side - sub_img.shape[0]) / 2)), cv2.BORDER_CONSTANT)
sub_image_resized = cv2.resize(src=sub_image_padded, dsize=(350, 350))
processed_images.append(sub_image_resized)
return processed_images
# Get recommendations, analyze and swipe them.
person_id = ''
liked_people = 0
all_poeple = 0
for request_ix in range(num_requests):
# Retrieve recommended profiles from Tinder.
for key, value in api.get_recommendations(headers).items():
if (key == "results"):
# Loop over profiles.
for person in value:
print("---------------------------------------")
all_poeple += 1
liked = False
ratings = [0]
for key, value in person.items():
if (key == "_id"):
person_id = value
print('Person id: ', person_id)
person_data = api.get_person(person_id, headers)
name = person_data["results"]["name"]
print("Name: " + name)
if (key == "photos"):
get_my_locations('locations.csv') # получаем 3 случайных локации из файла и записываем в список my_locations
for location in my_locations:
try:
lat = location.split(":")[0]
lon = location.split(":")[1]
api.get_ping(lat,lon)
matches = get_match_info()
for match in matches:
try:
match_id = matches[i]['match_id']
offer_id = 1
text = select_msg(match_id,offer_id)
api.send_msg(match_id, text)
except:
continue
rec = api.get_recommendations()
i = 0
for user in rec:
try:
if i%3=0:
api.dislike(user["person_id"])
else:
api.like(user["person_id"])
i ++
except:
continue
except:
break
def gettoken_id(fb_username, fb_password):
fb_username = '******'
fb_password = '******'
global _id
global fig
host = 'https://api.gotinder.com'
fb_access_token = fb_auth_token.get_fb_access_token(
fb_username, fb_password)
fb_user_id = fb_auth_token.get_fb_id(fb_access_token)
tinder_api.get_auth_token(fb_access_token, fb_user_id, host)
print("#################################################")
while (like < 30 or dislike < 30):
try:
recommendations = tinder_api.get_recommendations()
except:
recommendations = tinder_api.get_recommendations()
for index in range(len(recommendations['results'])):
global images
images = []
name = recommendations['results'][index]['name']
birth_date = features.calculate_age(
recommendations['results'][index]['birth_date'])
ping_time = recommendations['results'][index]['ping_time']
if name == '5 GUM':
continue
_id = recommendations['results'][index]['_id']
print("name is {} and is {} years old and bio is {}".format(
name, birth_date, recommendations['results'][index]['bio']))
try:
if recommendations['results'][index]['bio']:
fig = plt.figure('Biography', figsize=(6, 4))
t = (recommendations['results'][index]['bio'])
text = fig.text(0.5, 0.5, t, ha='center', va='center', size=10)
text.set_path_effects([path_effects.Normal()])
plt.axis('off')
plt.tight_layout()
plt.show(block=False)
except:
plt.close()
pass
number_of_subplots = len(recommendations['results'][index]['photos'])
if number_of_subplots < 1:
continue
elif number_of_subplots == 1:
fig, axes = plt.subplots(figsize=(15, 15))
elif number_of_subplots <= 4:
fig, axes = plt.subplots(1, number_of_subplots, figsize=(15, 15))
elif number_of_subplots > 4:
fig, axes = plt.subplots(int(number_of_subplots / 4) + 1,
4,
figsize=(15, 15))
fig.canvas.mpl_connect('key_press_event', press)
subplots_adjust(hspace=0.000)
for i, v in enumerate(range(number_of_subplots)):
p = recommendations['results'][index]['photos'][i]
image = io.imread(p['url'])
images.append(image)
if number_of_subplots == 1:
axes.axis('off')
axes.imshow(image)
axes.axis('off')
elif number_of_subplots <= 4:
axes[i].axis('off')
axes[i].imshow(image)
axes[i].axis('off')
else:
axes[int(i / 4), int(i % 4)].axis('off')
axes[int(i / 4), int(i % 4)].imshow(image)
axes[int(i / 4), int(i % 4)].axis('off')
if number_of_subplots == 5:
for j in range(4):
axes[int(i / 4), int(j)].axis('off')
elif number_of_subplots > 5:
while i % 4 != 0:
axes[int(i / 4), int(i % 4)].axis('off')
i += 1
plt.tight_layout()
#plt.axis('off')
plt.show(block=True)
print("###################################")
def get_recs(self):
return tinder_api.get_recommendations(self.auth_token)
for circuit in circuits:
q = Q_program.get_quantum_register("q")
c = Q_program.get_classical_register('c')
decision_circuit = Q_program.create_circuit(circuit, [q], [c])
decision_circuit.h(q[0]) #Turn the qubit into |0> + |1>
decision_circuit.measure(q[0], c[0])
_ = Q_program.get_qasms(circuits) # Suppress the output
###Main Loop###
recommendations = Queue() #Queue to hold tinder recommendations
decisions = Queue() #Queue to hold QX decisions
while True:
#Replenish the queues
if recommendations.empty():
results = get_recommendations()
results = results['results']
for key in results:
recommendations.put(key['_id'])
if decisions.empty():
results = Q_program.execute(circuits,
device,
shots=1,
max_credits=5,
wait=10,
timeout=240)
for ciruit in circuits:
for key in results.get_counts(ciruit):
decisions.put(int(key))