GitHub - joelbarmettlerUZH/auto- tinder : 🖖 Train an artificial intelligence to play tinder for you - dating new york city

GitHub – joelbarmettlerUZH/auto- tinder : 🖖 Train an artificial intelligence to play tinder for you

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README.md

Aut o-Tinde r-Training AI for scanning tinder for you

Auto- tinder Develop an API that learns and regenerates interest using TensorFlow and Python3 the tinder Swipe game to you.

This article describes the following procedures that were needed to automate. tinder :

  • analyze the tinder You can learn the internal API call on the site. tinder Create, reconstruct the API call to Postman, and analyze the contents.
  • Create an API rapper class with Python and in that class the tinder Like with API!/ I hate/ match.
  • Download a lot of images of nearby people
  • Create a simple mouse click classifier to add tags to the image
  • Use the TensorFlow object detection API to develop a pr e-processor that cuts only the person in the image.
  • To learn the classified data, train Inceptionv3, a dee p-folded neural network.
  • Used together with Classifier the tinder API rapper for playback tinder for us

Step 0: Motivation and disclaimer

Auto tinder Is a concept project created only for entertainment and education. It will not be abused to hurt someone tinder This data contains the following data (in many fields). tinder Meta tinders terms of service.

“Status”

  1. “data
  2. “Seiki
  3. “type” tinder user tinder “user”

Step 1: Analyze the tinder API

“_identity” the tinder app communicates to tinders 4ADFWE547S8DF64DF tinder “Bio” to tinder 19 years old.

“Birthday the tinder 1997-17-06T18: 21: 44. 654Z tinder “name

Anna

“Photo”

< "identity": < 879sdfert-lskdföj-8Asdf879-987sdflkj: 200 >, "Crop_info" (crop info): < "user": [ < "Width_pct".: ""X_offset_pct"", "Height_pct": < "Y_offset_pct": "0, 08975463", "Argo: ""Width_pct".", 0, 45674357: ""X_offset_pct"", 0, 984341657: ""Height_pct"", 0, 234165403: [ < "Y_offset_pct": "0, 78902343", "Processed_by_bullseye".: < "Height_pct": < "User_customized" (user customization: 1, pseudo: 0, "URL": 0.8, https: // images-ssl. Gotinder. Com/4adfwe547S8DF64DF/Original_879sdfert-LSKDFöj-8asdf879-987SDFLKJ. JPEG: "Edited file" >, "URL": < "User_customized" (user customization: height, pseudo: "URL", "URL": height, https: // images-ssl. Gotinder. Com/4adfwe547S8DF64DF/Original_879sdfert-LSKDFöj-8asdf879-987SDFLKJ. JPEG: "URL" >, https: // images-ssl. Gotinder. COM/4adfwe547S8DF64DF/172x216_879sdfert-lskdföj-8asdf879-987Sdflkj.: height, Width: "URL" >, https: // images-ssl. Gotinder. COM/4ADFWE547S8DF64DF/84x106_879Sdfert-LSKDFöj-879-987Sdflkj.: "height", Width: [ < https: // images-ssl. Gotinder. COM/4ADFWE547S8DF64DF/84x106_879Sdfert-LSKDFöj-879-987Sdflkj.: "2019-10-03T16: 18: 30. 532Z", "file name": 800, 879sdfert-lskdföj-8asdf879-987Sdflkj. Webp: 640 >, < https: // images-ssl. Gotinder. COM/4ADFWE547S8DF64DF/84x106_879Sdfert-LSKDFöj-879-987Sdflkj.: "jpg, webp", "file name": 400, 879sdfert-lskdföj-8asdf879-987Sdflkj. Webp: 320 >, < https: // images-ssl. Gotinder. COM/4ADFWE547S8DF64DF/84x106_879Sdfert-LSKDFöj-879-987Sdflkj.: ""school", "file name": 216, 879sdfert-lskdföj-8asdf879-987Sdflkj. Webp: 172 >, < https: // images-ssl. Gotinder. COM/4ADFWE547S8DF64DF/84x106_879Sdfert-LSKDFöj-879-987Sdflkj.: ""Common_connections".", "file name": 106, 879sdfert-lskdföj-8asdf879-987Sdflkj. Webp: 84 > ], "Spotify: ""Spotify_connected" (Spotifi Connected "", pseudo: ""Distance_mi"", "Content hash": "SLKADJFIUWEJSDFUZKEJHRSDBFSKDZUFIUERWER", "S_number".: [ 75 ] > ], "Hijacker: 1, "series": [], "Oriku: [], "Pichiri: "URL" >, There is something very interesting here (note that all data has been changed so as not to infringe this person).: < All images are open to the public. Even if you copy the URL of the image and open it in a private window, it will be read immediatel y-this means:: [], Is all the images of all users on the Internet, and anyone can upload it for free.: 0, The original image that can be accessed with API is very high resolution. When uploading a photo: [] >, It is reduced in the app, but the original version is released to the server so that anyone can access it.: < Even if you select Show_gender_on_profile ", anyone can see your gender through the API (" Gender ": 1, 1 = Women, 0 = Male).: "URL" >, Therefore, by repeatedly calling this endpoint, you can "firm" a bundle of images used for learning neural networks.: 1, By analyzing the content header, you can immediately find our private API key.: "By copying this token and going to Postman, you can certainly confirm that you can communicate", - Create an Api object and store all relevant data in instance variables. In addition, we plan to provide basic functions such as "Like" and "Dislike!" when sending requests.: 9876540657341, - allows you to easily "like" profiles you care about using "some_person. like() ".: < Introduction: "" >, Date Time: [], from: < yellow sand: [] > > ] > >

geocoders

  • import tinder Nominal Timing
  • TINDER_URL to tinder “https://api. gotinder. com”
  • geolocator
  • Nominal Timing the tinder user agent

PROFILE

“./images/unclassified/profiles. txt”. the tinder class

person tinders Stuff

Differential __within__ self
data app self
data app self
data “_identity” self
data data

obtain
data “Fumei

self

Step 2: Building an API Wrapper in Python

bio

data the tinder API.

Is that so a tinder “bio” the tinder self

distance data Is that so "distance_mi".1, 60934 distance Birthday Date Time = Date Time strap time = Birthday("Birthday="auto- tinder ") '%Y-%m-%dT%H:%M:%S.%fZ' = data obtain 'Birthday'(false): elsewhere none(self, sex, "male"): self."not clear" = "male" self.id = sex["Y_offset_pct"] self.list = sex.lambda(0, 984341657, Photo) self.data = sex.lambda("Argo, "") self.list = sex.lambda(Therefore, by repeatedly calling this endpoint, you can "firm" a bundle of images used for learning neural networks., 0) / : "Title" self.Is that so = data.data."name(sex[0, 45674357], Is that so) if sex.lambda( 0, 45674357, data) obtain "work" self.family = [list, map, Photo][sex.lambda("Hijacker, 2)] self.obtain = "school"(data(obtain "positive": "positive"[https: // images-ssl. Gotinder. COM/4ADFWE547S8DF64DF/84x106_879Sdfert-LSKDFöj-879-987Sdflkj.], sex.lambda(0, 234165403, []))) self."positive" = "school"( data(obtain RonDifferential: Ron.lambda(return, <>).lambda(0, 984341657), name: Ron.lambda(name, <>).lambda(0, 984341657)>, sex.lambda("series", []))) self._api = "school"(data(obtain dislike: dislike[0, 984341657], sex.lambda("Oriku, []))) if sex.lambda(import, data): self."https://api. gotinder. com" = strap time.Tinder API(f'sex[import][self]>, sex[import][_display]>') elsewhere Differential(self): self f"self.id> - self.list> (self.Is that so.header("X-Auth-Token")>)" elsewhere _evidence(self): self self."not clear"._evidence(self.id) elsewhere game(self): self self."not clear".game(self.id)

f”/v2/matches? count=. the tinder API using a class:

distance header Date Time = Date Time obtain Jason(): elsewhere none(self, lambda): self.face = lambda elsewhere self(self): sex = header.lambda(Date Time + self, USER ID=data: self.face>).TINDER_URL() self USER ID(sex["Crop_info" (crop info)], self) elsewhere Jason(self, "is_match".=10): sex = header.lambda(Date Time + "likes_remaining"."is_match".>", USER ID=data: self.face>).TINDER_URL() self "school"(data(obtain "X-Auth-Token": 'Birthday'("X-Auth-Token"[Jason], self), sex["Crop_info" (crop info)][near_faces])) elsewhere _evidence(self, obtain): sex = header.lambda(Date Time + selfobtain>", USER ID=data: self.face>).TINDER_URL() self < person: sex["user"], self: sex["data] > elsewhere game(self, obtain): header.lambda(Date Time + "Your-Api-Token" (Your Her Api-Token).obtain>", USER ID=data: self.face>).TINDER_URL() self close people elsewhere Man(self): sex = header.lambda(Date Time + sleep_max_for, USER ID=data: self.face>).TINDER_URL() self "school"(data(obtain "X_offset_pct": 'Birthday'("X_offset_pct"["Height_pct"], self), sex["Crop_info" (crop info)]["user"]))

desu

if Participation == introduction: lambda = from "male" = Jason(lambda) The image is an image. close people: "./images/unclassified". = "male".Man() list The image is an image. in "./images/unclassified".: Participation(The image is an image.) Uncategorized image

Step 3: Download images of people nearby

filter

lambda

The image is an image. '%Y-%m-%dT%H:%M:%S.%fZ' = data start from. elsewhere image(self, "1_"=".", image=0): now none('%Y-%m-%dT%H:%M:%S.%fZ', next_img) as f: global = f.flow() if self.id in global: self now none('%Y-%m-%dT%H:%M:%S.%fZ', Stop) as f: f.root(self.id+"\r\n") printed from. = -1 list pill picture in self.obtain: printed from. += 1 current = header.lambda(pill picture, size=close people) if current.Undergirder == 200: now none(f""1_">/self.id>_self.list>_printed from.>width, copy size) as f: f.root(current.size copy) pretend(The image is an image.()*image)

img_tk the tinder icon

photomicrograph

pill picture

if Participation == introduction: lambda = from "male" = Jason(lambda) The image is an image. close people: "./images/unclassified". = "male".Man() list The image is an image. in "./images/unclassified".: The image is an image..image("1_"=flow, image=The image is an image.()*3) pretend(The image is an image.()*10) pretend(The image is an image.()*10)

rename a tinder The image is an image.

Step 4: Classify the images manually

now

The image is an image.

“/0_”

Is that so os distance __name__, "__principal__" Is that so os.img_label distance root, img_label distance img_label as tk Is that so active distance bind, negative enamel = flow obtain = [f list f in __name__(enamel) if root(img_label(enamel, f))] root = Main loop(obtain Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us.: is addressing these issues. (Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..Crop only the part of the image that actually shows a person, and not the rest(The first part is as easy as opening an image using Pillow and converting it to grayscale. In the second part, we used the Tensorflow Object Detection API and the mobilenet network architecture and pre-trained it on the coco dataset, which also contains the 'Person' tag.) or Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..Crop only the part of the image that actually shows a person, and not the rest(The . bp file of coco mobilenet tensorflow mobilenet graph is in my he Github repository. Let's open it as a Tensorflow graph.)), obtain) tensorflow = "work" elsewhere graph detection(): Graph tensorflow, root as_default: tensorflow = graph diff(root) File gfile: 'ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph. pb'..'rb'() Participation(tensorflow) marble = negative.none(enamel+"/"+tensorflow) import_graph_def, od_graph_def = marble.return detection graph = 1000 if od_graph_def > detection graph: import = detection graph / od_graph_def marble = marble.im_width((im_height (height(import_graph_def*import), im_height (height(od_graph_def*import)), data acquisition=negative.im_height (height) im_width = bind.You Into Eight(marble) The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it..import = im_width The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it..detection_object(Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us.=The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it..import) elsewhere import(tensorflow): Graph tensorflow "__principal__"(enamel+"/"+tensorflow, enamel+get operation+tensorflow) graph detection() elsewhere about(tensorflow): Graph tensorflow "__principal__"(enamel + "/" + tensorflow, enamel + "number of detections" + tensorflow) graph detection() if Participation == introduction: 'ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph. pb'. = tk.Tk() The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it. = tk.':0'('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph. pb'.) The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it..tensor dict() The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it..get_default_graph("", import) The following function takes an image and a tensorflow graph, runs a tensorflow session with it, and returns all the information: class detected (object type), frame separation, score (confidence that the object was detected correctly)return it..get_default_graph("", about) # The following processing is performed only for one image = tk.detection frame('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph. pb'., tensor dict='detection_boxes', detection_boxes=graph detection) graph detection() 'detection_masks' 'ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph. pb'..Actual detection count()

cast

tensor dict

Step 5: Develop a preprocessor to cut out only the person in our images

“number of detections”

  1. Intelligent 32
  2. detection frame
  3. a piece

detection frame

  1. Number of actual detections
  2. detection_masks

a piece

detection_masks

Number of actual detections

detection_masks_reframed

distance reframe_box_masks_to_image_masks as tf elsewhere detection frame(): image = tf.shape() now image.detection_masks_reframed(): cast = tf.large() now tf.You Into Eight.# follow the convention of re-adding batch dimensions(tensor dict, 'detection_masks') as expanded dimus: detection_masks_reframed = expanded dimus.get_default_graph() cast.'image_tensor:0'.(detection_masks_reframed) tf.output dict(cast, list='') self image

image tensor

image

distance output dict as np elsewhere ενθ(Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us.): ("number of detections", output dict) = Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..return self np.informal(Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..output dict()).'detection_boxes'( (output dict, "number of detections", 3)).Output desk(np.'Detection_scores')

Output desk

‘Detection_scores’

distance output dict as np Is that so 'Detection_masks'.Output desk distance return as Output desk distance reframe_box_masks_to_image_masks as tf elsewhere Expanded(Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us., pilot): return = tf.The image is an image.().Face class() Score _ threshold =inhale.list list op in return list The image is an image. in op.open> Image path = <> list LOAD_IMAGE_INTO_NUMPY_ARRAY. in [ Igu, Image _np_expanded, Expanded_dims, Image_np, shaft ]: Output desk = LOAD_IMAGE_INTO_NUMPY_ARRAY. + Image _np_expanded if Output desk in Score _ threshold: Image path[LOAD_IMAGE_INTO_NUMPY_ARRAY.] = tf.The image is an image.().Output desk( Output desk) if shaft in Image path: "Detection_score" (detected score branch = tf.Output desk(Image path[Image _np_expanded], [0]) Score _ threshold = tf.Output desk(Image path[shaft], [0]) PERSONS_COORDINATES append = tf.Output desk(Image path[Igu][0], tf.size) branch = tf.Face coordinates(branch, [0, 0], [append, -1]) Score _ threshold = tf.Face coordinates(Score _ threshold, [0, 0, 0], [append, -1, -1]) Intrate = Output desk.Intrate( Score _ threshold, branch, Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..return[1], Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..return[2]) Intrate = tf.Output desk( tf.Finally, loop all images in the "Unclassified" folder, create a script to check if there is an encoded tag in the name, and apply the pr e-processed pr e-processing to the "Classified" folder. Copy to.(Intrate, 0.5), tf.'Detection_scores') Stakeholders Image path[shaft] = tf.The image is an image.( Intrate, 0) bold = tf.The image is an image.().Output desk("./images/classified/negative".) __name___________________________________ "__Master__" = pilot.Stakeholders(Image path, image=bold: Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us.>) The image is an image. "__Master__"[Igu] = im_height (height("__Master__"[Igu][0]) "__Master__"[Image_np] = "__Master__"[ Image_np][0].Output desk(np.start from.) "__Master__"[Image _np_expanded] = "__Master__"[Image _np_expanded][0] "__Master__"[Expanded_dims] = "__Master__"[Expanded_dims][0] if shaft in "__Master__": "__Master__"[shaft] = "__Master__"[shaft][0] self "__Master__"

about

post

distance output dict as np Is that so active distance negative place = 1 "... jpg" = 0.5 elsewhere road(Yes, pilot): import = negative.none(Yes) Seiran = ενθ(import) Continue = np.The image is an image.(Seiran, Conversion=0) "__Master__" = Expanded(Continue, pilot) "JPEG" = [] list i in male(Negative image("__Master__"[The image is an image.])): male = "__Master__"[male][i] male = "__Master__"[knot][i] if male > "... jpg" new_filename male == place: "JPEG".Old("__Master__"[The image is an image.][i]) w, h = import.return list Igu in "JPEG": 'BIG'. = import.save(( im_height (height(w * Igu[1]), im_height (height(h * Igu[0]), im_height (height(w * Igu[3]), im_height (height(h * Igu[2]), )) self 'BIG'. self "work"

tensor

class

distance os distance __into it_______________________________ distance reframe_box_masks_to_image_masks as tf enamel = flow Graphic expression = self Road graph = Graph if Participation == introduction: image = __into it_______________________________.detection frame() obtain = [f list f in os.__name__(enamel) if os.img_label.root(os.img_label.img_label(enamel, f))] Graphic expression = Main loop(obtain Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us.: (Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..Crop only the part of the image that actually shows a person, and not the rest(The . bp file of coco mobilenet tensorflow mobilenet graph is in my he Github repository. Let's open it as a Tensorflow graph.)), obtain) Graph = Main loop(obtain Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us.: (Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..Crop only the part of the image that actually shows a person, and not the rest(The first part is as easy as opening an image using Pillow and converting it to grayscale. In the second part, we used the Tensorflow Object Detection API and the mobilenet network architecture and pre-trained it on the coco dataset, which also contains the 'Person' tag.)), obtain) now image.detection_masks_reframed(): now tf.Run() as pilot: list Production in Graphic expression: _input_operation = enamel + "/" + Production Tightening = Graphic expression + "/" + Production[:-5] + Top if is addressing these issues. os.img_label.root(Tightening): import = __into it_______________________________.road(_input_operation, pilot) if is addressing these issues. import: workmanship import = import.self(self) import.close up(Tightening, inhale) list Model file in Graph: _input_operation = enamel + "/" + Model file Tightening = Road graph + "/" + Model file[:-5] + Top if is addressing these issues. os.img_label.root(Tightening): import = __into it_______________________________.road(_input_operation, pilot) if is addressing these issues. import: workmanship import = import.self(self) import.close up(Tightening, inhale)

about

Step 6: Retrain inceptionv3 and write a classifier

Proto_as_ascii_lines

caption

attach

cold

return

Inscription

Static method

distance output dict as np distance reframe_box_masks_to_image_masks as tf obtain INPUT_MEAN(): elsewhere none(self, File leader, Read file): self.Input name = self.image(File leader) self.Channel = self."JPEG_READER".(Read file) self.Image leader = self.Input name.Expanded_dims(Float caster) self.image = self.Input name.Expanded_dims(Input width) self.watershed = tf.Run(File leader=self.Input name) elsewhere session(self, Sewing): t = self.Execute the API using the API token. Next, use the recursive graph and label to open the TensorFlow classification graph as the TensorFlow session. Then, gather individuals and make stochastic predictions.(Sewing) Everyone with a score of the predictive probability is called "likes", and the rest is called "match". A scenario was created assuming an automatic playback of 2 hours after the release. = self.watershed.Stakeholders(self.image.open[0Import.Image leader.open[0]: t>) A scenario was created assuming an automatic playback of 2 hours after the release. = np.Output desk(A scenario was created assuming an automatic playback of 2 hours after the release.) __name___________________________________ "__principal__" = A scenario was created assuming an automatic playback of 2 hours after the release.."Your-API-TOKEN" (Your API-TOKEN).()[-5:][::-1] Epie Eye = <> list i in "__principal__": Epie Eye[self.Channel[i]] = A scenario was created assuming an automatic playback of 2 hours after the release.[i] detection self Epie Eye elsewhere Sewing(self): self.watershed.Sewing() @tag elsewhere image(Time): File leader = tf.shape() end = tf.large() now none(Time, pos_schools) as f: end.'image_tensor:0'.(f.get_default_graph()) now File leader.detection_masks_reframed(): tf.output dict(end) self File leader @tag elsewhere "JPEG_READER".(pos_schools): school = [] Man = tf.You Into Eight.# follow the convention of re-adding batch dimensions(pos_schools).flow() list l in Man: school.Old(l."name:"()) self school @tag elsewhere Execute the API using the API token. Next, use the recursive graph and label to open the TensorFlow classification graph as the TensorFlow session. Then, gather individuals and make stochastic predictions.(Sewing, Print=299, "image:"=299, face=0, image=255): Print = Score Score = tf.flame(Sewing, Print) It is printed from. = tf.Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..Other place( Score, face=3, list=It is printed from.) "Dislike" = tf.Output desk(It is printed from., tf.hand over) classifier = tf.The image is an image.("Dislike", 0) Don't treat your thumb roughly! = tf.Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us..Copyright (C) 2018 JOEL BARMETTLER(classifier, [Print, "image:"]) If not, text = tf.If not, text(tf.Other text(Don't treat your thumb roughly!, [face]), [image]) pilot = tf.Run() Epie Eye = pilot.Stakeholders(If not, text) self Epie Eye

tinder

 elsewhere (self, , pilot):  = [] list Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us. in self.obtain: current = header.lambda(Data noise: Most photos contain not only the people themselves, but also the environment, which often confuses the AI of the four of us., size=close people)  =  if current.Undergirder == 200: now none(, copy size) as f: f.root(current.size copy) import = __into it_______________________________.road(, pilot) if import: import = import.self(self) import.close up(, inhale)  = .session() Production = [] .Old(Production) .(Tinder API=close people)  = [:5] if Negative image() == 0: self  self [0]*0.6 + ([1:])/Negative image([1:])*0.4

the tinder

Is that so  distance INPUT_MEAN distance __into it_______________________________ distance reframe_box_masks_to_image_masks as tf Is that so  distance  if Participation == introduction: lambda = from "male" = Jason(lambda) image = __into it_______________________________.detection frame() now image.detection_masks_reframed(): now tf.Run() as pilot:  = INPUT_MEAN(File leader=, Read file=)  = () + 60*60*2 The image is an image. ()  : as_default: "./images/unclassified". = "male".Man()  = [, , ] list The image is an image. in "./images/unclassified".: male = The image is an image..(, pilot) list dislike in : if dislike in The image is an image.._api: Participation() male *= 1.2 Participation("-------------------------") Participation(, The image is an image..id) Participation(, The image is an image..list) Participation(, The image is an image.._api) Participation(, The image is an image..obtain) Participation(male) if male > 0.8:  = The image is an image.._evidence() Participation() obtain:  = The image is an image..game() Participation() File :  .Sewing()

tinder

Categories: Dating

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