def generateQADoc(pretrained_path=FS.PRE_TRAINED.PUB_MED,
ffn_weight_file=None,
bert_ffn_weight_file=FS.MODELS.BERT_FFN,
embedding_file=FS.EMBEDDINGS.BERT_FFN_PKL):
doc = GenerateQADoc(pretrained_path=pretrained_path,
ffn_weight_file=None,
bert_ffn_weight_file=bert_ffn_weight_file,
embedding_file=embedding_file)
Log.debug(
doc.predict('my eyes hurts and i have a headache.',
search_by='answer',
topk=5,
answer_only=False))
Log.debug(
doc.predict('my eyes hurts and i have a headache.',
search_by='question',
topk=5,
answer_only=False))
def explain(api: infermedica_api.API):
# Prepare diagnosis request it need to have sufficient amount of evidences
# The most appropriate way to get a request way for explain method is to
# use the one which has been created while interacting with diagnosis.
# For the example purpose a new one is created.
request = infermedica_api.Diagnosis(sex='female', age=35)
request.add_symptom('s_10', 'present')
request.add_symptom('s_608', 'present')
request.add_symptom('s_1394', 'absent')
request.add_symptom('s_216', 'present')
request.add_symptom('s_9', 'present')
request.add_symptom('s_188', 'present')
# call the explain method
request = api.explain(request, target_id='c_62')
# and see the results
Log.pretty(request)
def diagnosis(api: infermedica_api.API):
request = infermedica_api.Diagnosis(sex='female', age=35)
request.add_symptom('s_21', 'present', initial=True)
request.add_symptom('s_98', 'present', initial=True)
request.add_symptom('s_107', 'absent')
# call diagnosis
request = api.diagnosis(request)
Log.pretty(request)
# ask patient the questions returned by diagnosis and update request with patient answers
request.add_symptom('s_99', 'present')
request.add_symptom('s_8', 'absent')
request.add_symptom('s_25', 'present')
# ... and so on until you decided that enough question have been asked
# or you have sufficient results in request.conditions
# call diagnosis again with updated request
request = api.diagnosis(request)
# repeat the process
Log.pretty(request)
def conditions(api: infermedica_api.API):
Log.info('Conditions list:')
Log.pretty(api.conditions_list())
Log.info('Conditions details:')
Log.pretty(api.condition_details('c_221'))
Log.warn('Conditions details (Fail Test)')
Log.pretty(api.condition_details('fail'))
})
api = infermedica_api.get_api()
# resp = api.parse('I feel stomach pain but no coughing today.')
# Log.debug(resp)
if __name__ == '__main__':
FLAGS = argparse.ArgumentParser(
epilog='Infermedica diagnosis API.\n',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Test infermedica API'
)
FLAGS.add_argument('-id', '--id', type=str, default='',
help='Your infermedica APP ID.')
FLAGS.add_argument('-k', '--key', type=str, default='',
help='Your infermedica API Key.')
FLAGS.add_argument('-m', '--mode', type=bool, default=True,
help='Make requests in development mode (Only during development).')
args = FLAGS.parse_args()
# Display command-line arguments & their values.
Log.info('\n{0}\n{1:^55}\n{0}'
.format('-' * 55, 'Command Line Arguments'))
for k, v in vars(args).items():
Log.info('{:<20} = {!s:>20}'.format(k, v))
Log.info('\n{}\n'.format('-' * 55))
main()
gpt2_input = self._get_gpt2_inputs(questions[0], topk_question,
topk_answer)
gpt2_pred = self.estimator.predict(
lambda: gpt2_estimator.predict_input_fn(inputs=gpt2_input,
batch_size=self.batch_size,
checkpoint_path=self.
gpt2_weight_file))
raw_output = gpt2_estimator.predictions_parsing(
gpt2_pred, self.encoder)
# result_list = [re.search('`ANSWER:(.*)`QUESTION:', s)
# for s in raw_output]
# result_list = [s for s in result_list if s]
# try:
# r = result_list[0].group(1)
# except (AttributeError, IndexError):
# r = topk_answer[0]
refine1 = re.sub('`QUESTION:.*?`ANSWER:',
'',
str(raw_output[0]),
flags=re.DOTALL)
refine2 = refine1.split('`QUESTION: ')[0]
return refine2
if __name__ == "__main__":
from config.util import Log
gen = GenerateQADoc()
Log.info(gen.predict('my eyes hurt'))