def __init__(self,id_request,logController):
self.logController = logController
self.crawlerState='Esperando'
self.crawlerProgress=0
self.totalCrawling=0
self.scraperState='Esperando'
self.scraperProgress=0
self.totalScraping=0
self.IRState='Esperando'
self.IRProgress=0
self.totalIR=0
self.exception= ""
self.stop=False
self.state=dict()
self.id_request = id_request
self.config = config()
self.db = self.config.getDbProgress()
self.cursor = self.db.cursor()
with db_session:
estado = WsRequestState(estado = unicode(self.state), stop = False , search_keys = id_request)
commit()
flush()
# para inicializar el estado en la bd
self.get_progress()
self.actualizar_estado()
def pending_flush(request):
""" empties the pending queue without doing anything about the jobs. for debug use. """
try :
config = models.config()
except DeadlineExceededError, e :
logging.warning( '%s error getting whistler_config' % e)
logging.warning( traceback.format_exc() )
return None
def pending(request):
""" returns json with data for angles."""
try :
config = models.config()
except DeadlineExceededError, e :
logging.warning( '%s error getting whistler_config' % e)
logging.warning( traceback.format_exc() )
return None
def complete(request):
""" called from poller on sucessful run."""
config = models.config()
response = authenticate(request, config)
if response: return response
run_id = request.POST.get("run_id")
run = get_object_or_404(models.AnglesRun, id=run_id)
try :
run.completed = datetime.datetime.utcnow()
# make a post per uploaded file
for key,infile in request.FILES.items():
file_name = files.blobstore.create(mime_type='image/png')
with files.open(file_name, 'a') as f:
f.write(infile.read())
files.finalize(file_name)
blob_key = files.blobstore.get_blob_key(file_name)
logging.info("Uploaded a file to blob_key %s" % blob_key)
logging.info("Making a blog post on %s" % run.weavr_token.weavr_url)
post = run.weavr_token.post("/1/weavr/post/", {
"category":"article",
"title": request.POST.get("message"),
"keywords": "Angles",
"body" : "<img src='http://weavrs-angles.appspot.com/angles/blob/%s/'>" % blob_key
})
run.post_id = post["post_id"]
run.success = True
run.error_record = None
run.save()
except OAuthForbiddenException, e :
logging.info( '%s error posting to weavr' % e)
logging.info( traceback.format_exc() )
run.success = False
run.error_record = '%s error posting to weavr' % e
run.save()
ckpt,
mode='train')
# test data
wav_list = glob.glob(os.path.join(FLAGS.test_data_dir, '*wav'))
util_train.infer_testset(model_graph, sess, audio_length,
word_dict, wav_list, FLAGS.result_dir,
batch_size, ckpter.saver, ckpt)
# validation stats
evaluator.stats_validation(ckpt.split('-')[-1])
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
models.config(parser)
MODEL_FLAGS, _ = parser.parse_known_args()
parser.add_argument('--data_url',
type=str,
default='http://download.tensorflow.org/data/'
'speech_commands_v0.01.tar.gz')
parser.add_argument('--train_data_dir',
type=str,
default='dataset/train/audio/')
parser.add_argument('--test_data_dir',
type=str,
default='dataset/test/audio/')
parser.add_argument('--result_dir', type=str, default='run')
parser.add_argument('--log_dir', type=str, default='run/logs')
parser.add_argument('--ckpt_dir', type=str, default='run/ckpts')
