def post(self):
'''
Creates a new property in the dataset.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
lga = request.json['lga']
p_type = request.json['property_type']
r_type = request.json['room_type']
g_count = request.json['guest_count']
b_count = request.json['bed_count']
price = request.json['price']
if is_all_none(lga, price, p_type, g_count, b_count, r_type):
raise BadRequest
new_property = Property(lga, p_type, r_type, g_count, b_count, None,
price)
session = Session()
session.add(new_property)
session.commit()
# Analytics
status_code = 201
record = Recorder('property_create', status_code)
record.recordUsage()
response = {'message': 'Property Added.'}
return response, status_code
def patch(self, id):
'''
Updates a property in the dataset.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
session = Session()
prop = session.query(Property).filter(Property.id == id).first()
# Resource not found
if prop is None:
raise NotFound
lga = request.json.get('lga')
p_type = request.json.get('property_type')
r_type = request.json.get('room_type')
g_count = request.json.get('guest_count')
b_count = request.json.get('bed_count')
price = request.json.get('price')
# Empty request
if is_all_none(lga, price, p_type, g_count, b_count, r_type):
raise BadRequest
if lga is not None:
prop.lga = lga
if p_type is not None:
prop.property_type = p_type
if r_type is not None:
prop.room_type = r_type
if g_count is not None:
prop.guest_count = g_count
if b_count is not None:
prop.bed_count = b_count
if price is not None:
prop.price = price
session.commit()
# Analytics
status_code = 200
record = Recorder('property_patch', status_code)
record.recordUsage()
msg = {'message': 'Property ' + str(id) + ' updated successfully.'}
return msg, status_code
def put(self):
'''
Replaces the current machine learning model by retraining a new model.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
Trainer().train()
msg = {
'message' : 'The ML Model has been retrained successfully.'
}
status_code = 200
record = Recorder('train_model', status_code)
record.recordUsage()
return msg, status_code
def get(self):
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, False).authenticate()
lga = request.args['lga']
p_type = request.args['property_type']
r_type = request.args['room_type']
g_count = request.args['guest_count']
b_count = request.args['bed_count']
p = Predictor(lga, p_type, r_type, g_count, b_count)
msg = p.predict()
status_code = 200
record = Recorder('predictions', status_code)
record.recordUsage()
return msg, status_code
def get(self):
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, False).authenticate()
limit = request.args.get('limit', 5)
order = str(request.args.get('order', 'ascending'))
order = order == "ascending" #order is true for ascending, false otherwise
limit = limitCheck(limit)
data = UnemploymentRanker(limit, order).rank()
status_code = 200
record = Recorder('unemployment_rankings', status_code)
record.recordUsage()
resp = {'data': data}
return resp, status_code
def get(self):
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
start_date_string = request.args.get('start_date')
end_date_string = request.args.get('end_date')
user_id = request.args.get('user_id')
start_date = DateConverter(start_date_string).convert()
end_date = DateConverter(end_date_string).convert()
summarizer = Summarizer(user_id=user_id,
start_date=start_date,
end_date=end_date)
summary = summarizer.summarize()
status_code = 200
record = Recorder('analytics', status_code)
record.recordUsage()
return summary, status_code
def get(self, id):
'''
Shows detail of a property in the dataset.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
session = Session()
prop = session.query(Property).filter(Property.id == id).first()
if prop is None:
raise NotFound
prop = prop.__dict__
prop.pop('_sa_instance_state', None)
# Analytics
status_code = 200
record = Recorder('property_show', status_code)
record.recordUsage()
return prop, status_code
def post(self):
'''
Authenticates a user and creates a JWT token for the user
'''
email = request.json['email']
password = request.json['password']
authenticator = CredentialsAuthenticator(email, password)
user, token = authenticator.authenticate()
# Analytics here
status_code = 201
record = Recorder('login', status_code)
record.recordUsage()
response = {
'message': 'Login successful. Token is successfully created.',
'email': user.email,
'is_admin': user.is_admin,
'token': token
}
return response, status_code
def get(self):
'''
Shows ranking of local government areas around Sydney by average ratings given by Airbnb tenants
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, False).authenticate()
limit = request.args.get('limit', 5)
order = str(request.args.get('order', 'descending'))
order = order == "ascending" #order is true for ascending, false otherwise
limit = check_limit(limit)
data = RatingRanker(limit, order).rank()
status_code = 200
record = Recorder('rating_ranking', status_code)
record.recordUsage()
resp = {'data': data}
return resp, status_code
def get(self):
'''
Shows ranking of local government areas around Sydney by monthly average number of crime offences
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, False).authenticate()
limit = request.args.get('limit', 5)
order = str(request.args.get('order', 'ascending'))
order = order == "ascending" #order is true for ascending, false otherwise
limit = check_limit(limit)
data = CrimeRanker(limit, order).rank()
status_code = 200
record = Recorder('safety_ranking', status_code)
record.recordUsage()
resp = {'data': data}
return resp, status_code
def put(self, id):
'''
Replaces a property in the dataset.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
session = Session()
prop = session.query(Property).filter(Property.id == id).first()
# Resource not found
if prop is None:
raise NotFound
lga = request.json.get('lga')
p_type = request.json.get('property_type')
r_type = request.json.get('room_type')
g_count = request.json.get('guest_count')
b_count = request.json.get('bed_count')
price = request.json.get('price')
prop.lga = lga
prop.property_type = p_type
prop.room_type = r_type
prop.guest_count = g_count
prop.bed_count = b_count
prop.price = price
session.commit()
# Analytics
status_code = 200
record = Recorder('property_put', status_code)
record.recordUsage()
msg = {'message': 'Property ' + str(id) + ' updated successfully.'}
return msg, status_code
def get(self):
'''
Shows per-night rent price prediction of a property with specified attributes by using a trained machine learning model
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, False).authenticate()
lga = request.args['lga']
p_type = request.args['property_type']
r_type = request.args['room_type']
g_count = request.args['guest_count']
b_count = request.args['bed_count']
g_count = validate_integer_param(g_count)
b_count = validate_integer_param(b_count)
p = Predictor(lga, p_type, r_type, g_count, b_count)
msg = p.predict()
status_code = 200
record = Recorder('property_price_prediction', status_code)
record.recordUsage()
return msg, status_code
def delete(self, id):
'''
Deletes a property in the dataset.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
session = Session()
prop = session.query(Property).filter(Property.id == id).first()
# Resource not found
if prop is None:
raise NotFound
session.delete(prop)
session.commit()
# Analytics
status_code = 200
record = Recorder('property_delete', status_code)
record.recordUsage()
msg = {'message': 'Property ' + str(id) + ' deleted successfully.'}
return msg, status_code
def get(self):
'''
Shows list of properties in the dataset.
'''
auth_header = request.headers.get('Authorization')
TokenAuthenticator(auth_header, True).authenticate()
start = request.args.get('start', 0)
limit = request.args.get('limit', 100)
sort = str(request.args.get('sort', ''))
order = str(request.args.get('order', 'asc'))
lga = str(request.args.get('lga', ''))
p_type = str(request.args.get('property_type', ''))
r_type = str(request.args.get('room_type', ''))
g_count = request.args.get('guest_count')
b_count = request.args.get('bed_count')
property_attributes = [
'lga',
'property_type',
'room_type',
'bed_count',
'guest_count',
]
session = Session()
query = session.query(Property)
sortText = str()
if not sort:
sortText = 'properties.id' + " " + order
else:
if sort not in property_attributes:
raise BadRequest
if order not in ['asc', 'desc', '']:
raise BadRequest
sortText = "properties." + sort + " " + order
if lga:
query = query.filter(text("properties.lga = '" + lga + "'"))
if p_type:
query = query.filter(
text("properties.property_type = '" + p_type + "'"))
if r_type:
query = query.filter(
text("properties.room_type = '" + r_type + "'"))
if b_count is not None:
b_count = validate_integer_param(b_count)
query = query.filter(text("properties.bed_count = " +
str(b_count)))
if g_count is not None:
g_count = validate_integer_param(g_count)
query = query.filter(
text("properties.guest_count = " + str(g_count)))
start = validate_integer_param(start)
limit = validate_integer_param(limit)
end = start + limit
records = query.order_by(text(sortText))[start:end]
respJson = list()
for record in records:
record = record.__dict__
record.pop('_sa_instance_state', None)
respJson.append(record)
# Analytics
status_code = 200
record = Recorder('property_index', status_code)
record.recordUsage()
msg = {'data': respJson}
return msg, status_code
