cleanup

Author: SimionPutina

AI/predictor.py

@@ -17,7 +17,6 @@
 
     #good/bad
     identifiers = ['G', 'B']
-
     source = glob.glob("../2ndacc/testing/*")
     print source;
     counter = 0
@@ -41,15 +40,11 @@ def predict(self, source):
 
         # good/bad
         identifiers = ['G', 'B']
-
-        print source;
         testing = imread(source, 1)
         testing = imresize(testing, (200, 200))
         hog_features = hog(testing, orientations=12, pixels_per_cell=(16, 16),
                            cells_per_block=(1, 1))
         result_type = clf.predict(hog_features)
         # i[18:-4] - only id part of the filename
-        #print "source in predictor is: " + source
         Image.open(source).save('2ndacc/AIResults/svmHOG1/' + str(identifiers[result_type]) + source[9:-4] + ".jpg")
-        #print source[9:-4]
         return str(identifiers[result_type])

AI/test.py

@@ -9,21 +9,15 @@
 from PIL import Image
 import scipy.misc
 import warnings
+
 warnings.filterwarnings(action="ignore", category=DeprecationWarning)
 names = "http://images.gotinder.com/55f5f20482f96dff09d79380/7bfbe387-55e0-46bb-ab35-cef3ca3a6536.jpg"
-names2 =        "http://images.gotinder.com/577040ea19c4986f1250320c/6d9498f1-7967-4912-9e7a-6d32be8150eb.jpg"
-names3 =        "http://images.gotinder.com/57310df1d6290d1a1a7e5be8/d05a163a-1179-4bff-8a80-23633b0e3b2b.jpg"
-
-
 #print names[27:51]
-#print names2[27:51]
-#print names3[27:51]
 
 # good/bad
 identifiers = ['G', 'B']
 
 source = glob.glob("../2ndacc/testing/*")
-print source;
 counter = 0
 for i in source:
     testing = imread(i, 1)
@@ -32,8 +26,6 @@
                        cells_per_block=(1, 1))
     result_type = identifiers[0]
     print i[18:-4]
-    #Image.open(i).save('../2ndacc/AIResults/svmHOG/' + str(identifiers[result_type]) + i[18:-3] + ".jpg")
-
     counter += 1
 
 

AI/train_svm.py

@@ -29,7 +29,6 @@
 good =  glob.glob("./../2ndacc/good/*")
 bad = glob.glob("./../2ndacc/bad/*")
 
-print good
 data = []
 labels = []
 good_data = []
@@ -40,17 +39,13 @@
     image = imresize(image, (200, 200))
     hog_feat = hog(image, orientations=12, pixels_per_cell=(16,16), cells_per_block=(1,1))
     data.append(hog_feat)
-    #good_data.append(image.ravel())
-    #good_labels.append
     labels.append(0)
 
 for i in bad:
     image = imread(i, 1)
     image = imresize(image, (200, 200))
     hog_feat = hog(image, orientations=12, pixels_per_cell=(16,16), cells_per_block=(1,1))
     data.append(hog_feat)
-    #good_data.append(image.ravel())
-    #good_labels.append
     labels.append(1)
 
 print ' training svm'

AI/train_svm_ravel.py

@@ -7,7 +7,6 @@
 from scipy.misc import imread
 from sklearn.svm import LinearSVC
 from scipy.misc import imread,imsave,imresize
-
 import numpy as np
 import os
 import itertools
@@ -25,11 +24,9 @@
 import matplotlib
 import pickle
 
-
 good =  glob.glob("./../2ndacc/good/*")
 bad = glob.glob("./../2ndacc/bad/*")
 
-print good
 data = list()
 labels = list()
 good_data = []
@@ -38,24 +35,17 @@
 for i in range(len(good)):
     for filename in good[i]:
         image = imread(i, 1)
-        #image = imresize(image, (200, 200))
         imr = image.ravel()
         data.append(imr)
-        #good_data.append(image.ravel())
-        #good_labels.append
         labels.append(0)
 
 for i in range(len(bad)):
     for filename in bad[i]:
         image = imread(i, 1)
-        #image = imresize(image, (200, 200))
         imr = image.ravel()
         data.append(imr)
-        #good_data.append(image.ravel())
-        #good_labels.append
         labels.append(1)
 
-print ' training svm'
 clf = LinearSVC(dual=False, verbose=1)
 clf.fit(data, labels)
 pickle.dump(clf, open("svmravel.detector", "wb"))

README.md

@@ -3,6 +3,6 @@
 Uses machine learning to learn your type of people on tinder, and then autonomously swipes for you. 
 Collects instagrams accounts which you can mass-follow if you wish to do so.
 Able to view all of your friends* tinder accounts.
-Able to only swipe right the people who already swiped right on you.
+Able to only swipe right the people who already swiped you by spoofing locations.
 
 inspiration and API documentation by the beautiful [Alex Reed](https://github.com/0xdeafcafe)

correct_fn.py

@@ -11,10 +11,8 @@
 config = utils.read_file('CONFIG.cfg').splitlines()
 fbToken = config[0]  
 fbID = config[1]     
-print "fbtoken is " + str(fbToken) 
-
-
 source = glob.glob("2ndacc/to_correct/*")
+
 ids = []
 profiles = []
 print source
@@ -28,46 +26,30 @@
         for id in ids:
             if id in line:
                 profiles.append(str(id) + " "  + str(utils.get_url(line)))
-                #print "found one - " + str(id) + " url is " + str(utils.get_url(line))
-        #print lines[9:33]
-#print profiles
-
 
 def initialize():
     url = 'https://api.gotinder.com/auth'
     headers = {'Content-Type': 'application/json', 'User-Agent': 'Tinder/4.8.2 (iPhone; iOS 9.1; Scale/2.00)'}
     payload = {'force_refresh': 'False', 'facebook_id': fbID, 'facebook_token': fbToken}
     r = requests.post(url, headers=headers, data=json.dumps(payload))
-    #print r.text
     rjson = json.loads(r.text)
-    print "token is: " + rjson['token']
     return rjson['token']
 
 tinder_token = initialize()
-
-
-#print 'token is: ' + str(fbToken)
-#print 'fbid is: ' + str(fbID)
-
-
-
 tinder_headers = {'X-Auth-Token': tinder_token,
                   'Authorization': 'Token token="{0}"'.format(tinder_token).encode('ascii', 'ignore')
                   }
-print tinder_headers
 
 for profile in profiles:
     try:
         current = profile.split(' ')
-        #current0 = id, current1 = link to first pic
         link = 'https://api.gotinder.com/like/{0}'.format(current[0])
         liking_header = {'X-Auth-Token': tinder_token,
                             'Authorization': 'Token token="{0}"'.format(tinder_token).encode('ascii', 'ignore'),
                             'firstPhotoID': ''+str(current[1])
                             }
         likereq = requests.get(link, headers = liking_header)
         print 'status: ' + str(likereq.status_code) + ' text: ' + str(likereq.text)
-        print str(current[0]) + " "  + str(current[1])
     except Exception as ex:
         print "hit an exception i guess"
         print ex

getimages.py

@@ -16,14 +16,10 @@
 folder = "ladies/"
 counter = 0
 name = folder + str(counter)
-
-
-#urllib.urlretrieve("http://images.gotinder.com/5553606c7edc79cb1fa8ad5a/18d73531-3dc5-438e-9e21-52b4d5271dcc.jpg", "name")
 links = re.findall(regexRule, liked)
-#print links
+
 
 for link in links:
-    #save with id as the name
     urllib.urlretrieve(link, folder+link[27:51]+extension)
     counter+=1
     name = folder + str(counter)+round+extension
@@ -33,8 +29,3 @@
 if (round != "Z"):
     with open("identifier.txt", "w") as text_file:
         text_file.write(roundIdentifier[roundIdentifier.index(round)+1])
-
-
-print ""
-#print newS
-#print type(newS)

instadick.py

@@ -5,23 +5,5 @@
 
 
 access_token = "2260798150.5029a98.aa136b2cc1aa4367b51e8ce753f1e308"
-#r = requests.get("https://instagram.com/oauth/authorize/?client_id=5029a98b04474633aae46fffe2d8e6c6&redirect_uri=http://localhost:6379/oauth_callback&response_type=token")
-#print r.text
-#print r.url
-
 r = requests.get("https://api.instagram.com/v1/users/search?q=laura&access_token=2260798150.5029a98.aa136b2cc1aa4367b51e8ce753f1e308")
-print r.text
-print r.text[3]
-out = json.loads(r.text)
-#print out['data'][0]['username']
-print out
-
-
-
-# req = urllib2.urlopen("https://instagram.com/oauth/authorize/?client_id=5029a98b04474633aae46fffe2d8e6c6&redirect_uri=http://localhost:6379/oauth_callback&response_type=token")
-# print req.geturl()
-# print req.info()
-# print req.response.read()
-
-
-#2260798150.5029a98.aa136b2cc1aa4367b51e8ce753f1e308
+out = json.loads(r.text)