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{
    "meta": {
        "status": 200
    },
    "data": {
        "results": [
            {
                "type": "user",
                "user": {
                 "_id": "4adfwe547s8df64df",

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import datetime
from geopy.geocoders import Nominatim

TINDER_URL = "https://api.gotinder.com"
geolocator = Nominatim(user_agent="auto-tinder")
PROF_FILE = "./images/unclassified/profiles.txt"

class Person(object):

    def __init__(self, data, api):

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import requests

TINDER_URL = "https://api.gotinder.com"

class tinderAPI():

    def __init__(self, token):
        self._token = token

    def profile(self):

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if __name__ == "__main__":
    token = "YOUR-API-TOKEN"
    api = tinderAPI(token)

    while True:
        persons = api.nearby_persons()
        for person in persons:
            print(person)
            # person.like()

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# At the top of auto_tinder.py
PROF_FILE = "./images/unclassified/profiles.txt"

# inside the Person-class
    def download_images(self, folder=".", sleep_max_for=0):
        with open(PROF_FILE, "r") as f:
            lines = f.readlines()
            if self.id in lines:
                return
        with open(PROF_FILE, "a") as f:

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if __name__ == "__main__":
    token = "YOUR-API-TOKEN"
    api = tinderAPI(token)

    while True:
        persons = api.nearby_persons()
        for person in persons:
            person.download_images(folder="./images/unclassified", sleep_max_for=random()*3)
            sleep(random()*10)
        sleep(random()*10)

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from os import listdir, rename
from os.path import isfile, join
import tkinter as tk
from PIL import ImageTk, Image

IMAGE_FOLDER = "./images/unclassified"

images = [f for f in listdir(IMAGE_FOLDER) if isfile(join(IMAGE_FOLDER, f))]
unclassified_images = filter(lambda image: not (image.startswith("0_") or image.startswith("1_")), images)
current = None

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import tensorflow as tf

def open_graph():
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb', 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

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import numpy as np

def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)

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import numpy as np
from object_detection.utils import ops as utils_ops
import tensorflow as tf

def run_inference_for_single_image(image, sess):
    ops = tf.get_default_graph().get_operations()
    all_tensor_names = {output.name for op in ops for output in op.outputs}
    tensor_dict = {}
    for key in [
        'num_detections', 'detection_boxes', 'detection_scores',