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])))