def CreateDataResponse(request):
"""Creates a DataResponse from a DataRequest.
Args:
request: A DataRequest instance received from client.
Returns:
A DataResponse instance and extra headers.
"""
waveform_data_source = GetWaveformDataSource(request)
pred_outputs = GetPredictionsOutputs(request)
data_response = data_pb2.DataResponse()
data_response.waveform_metadata.CopyFrom(waveform_data_service.GetMetadata(
waveform_data_source, _MAX_SAMPLES_CLIENT))
data_response.waveform_chunk.CopyFrom(waveform_data_service.GetChunk(
waveform_data_source, request, _MAX_SAMPLES_CLIENT))
if pred_outputs:
waveforms_pred = prediction_data_service.PredictionDataService(
pred_outputs, waveform_data_source, _MAX_SAMPLES_CLIENT)
data_response.prediction_metadata.CopyFrom(waveforms_pred.GetMetadata())
data_response.prediction_chunk.CopyFrom(waveforms_pred.GetChunk(request))
# When only an SSTable file pattern is provided, the cache will return the TF
# Example under the first iterated key. Since the order of the keys is not
# guaranteed, this response will not be cached as it is not idempotent.
extra_headers = dict()
no_cache = request.tf_ex_sstable_path and not request.sstable_key
extra_headers['Cache-Control'] = 'no-cache' if no_cache else 'public'
return data_response, extra_headers
def CreateDataResponse(request):
"""Creates a DataResponse from a DataRequest.
Args:
request: A DataRequest instance received from client.
Returns:
A DataResponse instance.
Raises:
NotImplementedError: Try to load from SSTable or EDF
IOError: No tf path provided
"""
data_response = data_pb2.DataResponse()
pred_outputs = None
if request.tf_ex_sstable_path:
raise NotImplementedError('Loading SSTables')
elif request.edf_path:
raise NotImplementedError('Loading EDF')
elif request.tf_ex_file_path:
tf_example = FetchTfExFromFile(request.tf_ex_file_path)
waveform_data_source = TfExDataSourceConstructor(tf_example, '')
if request.prediction_file_path:
pred_outputs = FetchPredictionsFromFile(
request.prediction_file_path)
else:
raise IOError('No path provided')
data_response.waveform_metadata.CopyFrom(
waveform_data_service.GetMetadata(waveform_data_source, _MAX_SAMPLES))
data_response.waveform_chunk.CopyFrom(
waveform_data_service.GetChunk(waveform_data_source, request,
_MAX_SAMPLES))
if pred_outputs:
waveforms_pred = prediction_data_service.PredictionDataService(
pred_outputs, waveform_data_source, _MAX_SAMPLES)
data_response.prediction_metadata.CopyFrom(
waveforms_pred.GetMetadata())
data_response.prediction_chunk.CopyFrom(
waveforms_pred.GetChunk(request))
return data_response
