import autogluon as ag
from autogluon import ObjectDetection as task
from console_logging.console import Console
console = Console()
console.log("Baixando Dataset...")
root = './'
filename_zip = ag.download(
'https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip',
path=root)
filename = ag.unzip(filename_zip, root=root)
console.log("Criando TASK TRAIN ")
import os
data_root = os.path.join(root, filename)
dataset_train = task.Dataset(data_root, classes=('motorbike', ))
console.info("TRAINING DATA MODEL...")
time_limits = 5 * 60 * 60 # 5 hours
epochs = 30
detector = task.fit(dataset_train,
num_trials=2,
epochs=epochs,
lr=ag.Categorical(5e-4, 1e-4),
ngpus_per_trial=1,
time_limits=time_limits)
console.success("TRAINING DONE !")
console.log("START TEST MODEL ")
dataset_test = task.Dataset(data_root,
index_file_name='test',
classes=('motorbike', ))
with open('jobs.json') as f:
job_data = json.load(f)
console.info("Crawling %d career pages." % len(job_data))
i = 0
for job_entry in job_data:
try:
url = job_entry['link']
page = requests.get(url)
tree = html.fromstring(page.content)
links = tree.xpath('//a')
job_postings = []
for link in links:
job_title = link.text_content().strip().lstrip()
if 'intern' in job_title: # only test if intern position
res = requests.post(
'http://127.0.0.1:8000/predict', json={'title': job_title})
prediction = res.text.strip().lstrip()
if prediction in ['IT/Software Development', 'Engineering']:
job_postings.append(job_title)
job_entry['positions'] = job_postings
except Exception as e:
console.error(e)
i = i + 1
if i % 20 == 0:
console.log("Processed %d pages." % i)
console.success("Finished crawling.")
with open('jobs.json', 'w') as f:
json.dump(job_data, f)
console.success("Dumped data.")
import dataset
from voiceit2 import VoiceIt2
from console_logging.console import Console
import os
console = Console()
console.log("Stating....")
apiKey = " " #
apiToken = " "
my_voiceit = VoiceIt2(apiKey, apiToken)
try:
#ENDPOINT_DB = os.getenv('ENDPOINT_DB')
#db = dataset.connect(ENDPOINT_DB)
db = dataset.connect('sqlite:///tovivo.db')
except:
db = dataset.connect('sqlite:///tovivo.db')
class CRUD:
@staticmethod
def cadastrar(data):
table = db['user']
user = my_voiceit.create_user()
print(user)
data['userId'] = user['userId']
table.insert(data)
# Debugging
console.setVerbosity(4)
# Training
# console.setVerbosity(3)
# Staging
# console.setVerbosity(2)
# Production
# console.mute()
# Neater logging inside VS Code
console.timeless()
console.monotone()
DATASET_FILEPATH = 'data/text_emotion.csv'
dataset_path = os.path.join(os.getcwd(), DATASET_FILEPATH)
console.log("Loading data from %s" % dataset_path)
def _clean(sentence):
regex_letters = "a-zA-Z"
regex_spaces = " "
regex_symbols = "!?@&;.,"
regex_pattern = regex_letters + regex_spaces + regex_symbols
new_sentence = re.sub('[^%s]' % regex_pattern, '', sentence)
regex_special_characters = "&.*?;"
regex_punctuation = "[.,]"
return re.sub(regex_punctuation, '',
re.sub(regex_special_characters, '', new_sentence))
def load_data():
class LISTA_USUARIO(Resource):
def post(self):
console.info("LISTANDO USUARIOS ")
argumentos = reqparse.RequestParser()
argumentos.add_argument("busca")
dados = argumentos.parse_args()
result = db.lista(dados['busca'])
return {"mesage": result}, 200
class DELETA_USUARIO(Resource):
def post(self):
console.error("DELETANDO USUARIO ")
argumentos = reqparse.RequestParser()
argumentos.add_argument("cpf")
dados = argumentos.parse_args()
print("DELETANDO USUARIO")
result = db.deleta(dados['cpf'])
return {"mesage": result}, 200
api.add_resource(about, "/")
api.add_resource(ATUALIZA_USUARIO, "/suinox/api/v1/update")
api.add_resource(LISTA_USUARIO, "/suinox/api/v1/lista")
api.add_resource(DELETA_USUARIO, "/suinox/api/v1/delete")
api.add_resource(CADASTRAR_USUARIO, "/suinox/api/v1/cadastro")
if __name__ == '__main__':
console.log("START APP ")
app.run(host="0.0.0.0", debug=True)
print("\nPlease provide a GPA and test score to chance.")
cur_gpa = float(input('GPA: '))
print("Given " + str(cur_gpa))
test_score = int(input('Test Score: '))
def new_samples():
return np.array([[0.0, 0], [cur_gpa, test_score], [maxgpa, maxtest]],
dtype=np.float32)
predictions = list(classifier.predict(input_fn=new_samples))
console.success("Made predictions:")
def returnChance(chance):
if chance == 0:
return "rejection"
if chance == 1:
return "admission"
console.log(
"Testing:\nGPA: 0\nTest Score: 0\nPrediction: %s\nExpected: rejection" %
returnChance(predictions[0]))
console.log(
"Testing:\nGPA: %0.1f\nTest Score: %d\nPrediction: %s\nExpected: admission"
% (maxgpa, maxtest, returnChance(predictions[2])))
console.success("Predicting:\nGPA: %d\nTest Score: %d\nPrediction:%s" %
(cur_gpa, test_score, returnChance(predictions[1])))
