def find_user(token, graph_api_user, with_friends=True, retrieved_time=datetime.utcnow(), with_picture=False):
"""Retrieve a User resource from the Graph API.
Args:
graph_api_user (str): The URL of the user node within the Graph API
Returns:
dict. A User resource.
"""
# Start to explore the Graph
# user = facebook.get('/' + graph_api_user)
user = graph_api_get(token, '/' + graph_api_user)
# Retrieve the admin groups
# accounts = facebook.get('/' + graph_api_user + '/accounts')
accounts = graph_api_get(token, '/' + graph_api_user + '/accounts')
if 'gender' in user.data:
gender = user.data['gender']
else:
gender = None
# Calculate the age
if 'birthday' in user.data:
user.data['age'] = user.data['birthday']
current_date = datetime.utcnow()
# If only a month and day are specified, do not attempt to calculate the age of the user
if re.match(r'\d{2}/\d{2}$', user.data['birthday']):
age = None
else:
birth_date = datetime.strptime( user.data['birthday'], '%m/%d/%Y' )
age = abs((current_date - birth_date).days / 365)
else:
age = None
# Formatting for the hometown
if 'hometown' in user.data:
user.data['hometown_name'] = user.data['hometown']['name']
else:
user.data['hometown_name'] = ''
# Formatting for the location
if 'location' in user.data:
user.data['location_name'] = user.data['location']['name']
else:
user.data['location_name'] = ''
admin = is_admin(user.data['id'])
if with_picture:
# Picture URL
# picture = facebook.get('/' + graph_api_user + '/picture?redirect=false&type=large')
picture = graph_api_get(token, '/' + graph_api_user + '/picture?redirect=false&type=large')
picture_url = picture.data['data']['url']
# Download the picture
h = httplib2.Http()
(resp_headers, picture_resp) = h.request(picture_url, "GET")
# os.path.dirname(__file__)
picture_file = open('tmp/user_picture.jpg', 'wb')
picture_file.write(picture_resp)
picture_file.close()
else:
picture_url = 'http://facebook.com'
# Serialize the User
# Only retrieve the User as its been serialized
# user_objects = User.objects(facebook_id=user.data['id'])
user_objects = User.objects(facebook_id=user.data['id'],retrieved_time=retrieved_time)
if len(user_objects) > 1:
raise Exception("More than one User serialized for %s" % user_objects.facebook_id)
elif len(user_objects) < 1:
user_object = User(facebook_id=user.data['id'],
name=user.data['name'],
age=age,
gender=gender,
hometown_name=user.data['hometown_name'],
location_name=user.data['location_name'],
friends=[],
admin=admin,
picture_url=picture_url,
retrieved_time=retrieved_time
)
if with_picture:
user_object.picture.put('tmp/user_picture.jpg')
user_object.save()
else:
user_object = user_objects.first()
user_object.facebook_id = user.data['id']
user_object.age = age
user_object.gender = gender
user_object.hometown_name = user.data['hometown_name']
user_object.location_name = user.data['location_name']
user_object.admin=admin
user_object.picture_url=picture_url
if with_picture:
user_object.picture.replace('tmp/user_picture.jpg')
user_object.save()
if with_picture:
os.remove('tmp/user_picture.jpg')
# Retrieve the friends
if with_friends:
friends = find_friends(token, user.data['id'], retrieved_time)
user_object.friends = friends
user_object.save()
return user_object
def download():
"""Download an export of Graph API data for any given User
"""
downloaded_resources = []
users = []
posts = []
friendships = []
publishings = []
comments = []
# Retrieve all shares left for posts by friends
sharedposts = []
# Retrieve all users who liked the post by this friend
likes = []
# Retrieve all reactions to the post by this friend
reactions = []
user = None
token = get_facebook_oauth_token()
if token:
user = facebook.get('/me')
if not is_admin(user.data['id']):
return redirect(url_for('index'))
min_date_str = request.args.get('min_date', None)
max_date_str = request.args.get('max_date', None)
# Filter for posts
if min_date_str is not None and max_date_str is not None:
min_date_str = re.sub(r'\.\d{3}Z$', '', min_date_str)
max_date_str = re.sub(r'\.\d{3}Z$', '', max_date_str)
min_date = datetime.strptime(min_date_str, '%Y-%m-%dT%H:%M:%S')
max_date = datetime.strptime(max_date_str, '%Y-%m-%dT%H:%M:%S')
else:
min_date = datetime(1, 1, 1)
max_date = datetime.utcnow()
graph_api_user = request.args.get('user', None)
if graph_api_user is not None:
# Start to explore the Graph
# graph_users = [ find_user(token, graph_api_user, retrieved_time=retrieved_time) ]
graph_users = User.objects(facebook_id=user.data['id'])
# graph_users = []
# for graph_user in User.objects(facebook_id=user.data['id']):
# graph_users.append(graph_user)
else:
# Retrieve serialized User resources
graph_users = User.objects()
# graph_users = []
# for graph_user in User.objects():
# graph_users.append(graph_user)
post_users = {}
for this_user in graph_users:
if not this_user.facebook_id in downloaded_resources:
users.append(this_user)
downloaded_resources.append(this_user.facebook_id)
# Retrieve the friends
friends = this_user.friends
for friend in friends:
if not friend.facebook_id in downloaded_resources:
# Add the friend as a user
users.append(friend)
downloaded_resources.append(friend.facebook_id)
# Add the friendship
friendships.append({'User A ID': this_user.facebook_id, 'User B ID': friend.facebook_id})
# Retrieve the posts
# user_posts = find_posts(token, this_user, '/' + str(this_user.facebook_id) + '/feed', min_date, max_date, retrieved_time)
user_posts = Post.objects(user=this_user)
# user_posts = []
# for user_post in Post.objects(user=user):
# user_posts.append(user_post)
for post in user_posts:
if not post.facebook_id in downloaded_resources:
posts.append(post)
downloaded_resources.append(post.facebook_id)
if post.user is not None:
post_users[post.user.facebook_id] = post.user
# Add the publishings
publishings.append({ 'User ID': post.user.facebook_id, 'Post ID': post.facebook_id })
# Graph API version 2.6 or later required
post_comments = find_comments(token, post, min_date, max_date)
comments.extend(post_comments)
post_sharedposts = find_sharedposts(post, min_date, max_date)
# Temporarily concatenate all comments and shares
comments.extend(sharedposts)
else:
return redirect(url_for('index'))
workbook = xlsxwriter.Workbook('tmp/socialmemories.xlsx')
users_worksheet = workbook.add_worksheet('Users')
posts_worksheet = workbook.add_worksheet('Posts')
friendships_worksheet = workbook.add_worksheet('Friendships')
publishings_worksheet = workbook.add_worksheet('Publishings')
comments_worksheet = workbook.add_worksheet('Comments')
denorm_posts_worksheet = workbook.add_worksheet('Denormalized Posts')
# Create the classifier
# Naive Bayes is performant (if not that accurate)
human_names = [(name, 'male') for name in names.words('male.txt')] + [(name, 'female') for name in names.words('female.txt')]
features = [({'name': name}, gender) for (name, gender) in human_names]
training_set = features[500:]
test_set = features[:500]
classifier = NaiveBayesClassifier.train(training_set)
export_users(csv, users, users_worksheet, classifier)
export_posts(csv, posts, posts_worksheet, denorm_posts_worksheet, post_users, classifier)
export_comments(csv, comments, comments_worksheet)
export_friendships(csv, friendships, friendships_worksheet)
export_publishings(csv, publishings, publishings_worksheet)
workbook.close()
return send_file('tmp/socialmemories.xlsx', as_attachment=True)
