Update python predict client with official python package

Author: tobegit3hub

python_predict_client/README.md

@@ -7,7 +7,7 @@ TensorFlow serving is the gRPC service for general TensorFlow models. We can imp
 ## Usage
 
 ```
-./predict_client.py --host 127.0.0.1 --port 9000 --model_name cancer --model_version 1
+./predict_client.py --host 127.0.0.1 --port 8500 --model_name default --model_version 1
 ```
 
 For sparse data, you can run with this command.

python_predict_client/model_pb2.py

@@ -1,51 +1,46 @@
 #!/usr/bin/env python
 
 import numpy
-
-from grpc.beta import implementations
 import tensorflow as tf
-
-import predict_pb2
-import prediction_service_pb2
+from grpc.beta import implementations
+from tensorflow_serving.apis import predict_pb2
+from tensorflow_serving.apis import prediction_service_pb2
 
 tf.app.flags.DEFINE_string("host", "127.0.0.1", "gRPC server host")
 tf.app.flags.DEFINE_integer("port", 9000, "gRPC server port")
-tf.app.flags.DEFINE_string("model_name", "cancer", "TensorFlow model name")
+tf.app.flags.DEFINE_string("model_name", "default", "TensorFlow model name")
 tf.app.flags.DEFINE_integer("model_version", -1, "TensorFlow model version")
+tf.app.flags.DEFINE_string("signature_name", "", "The signature name")
 tf.app.flags.DEFINE_float("request_timeout", 10.0, "Timeout of gRPC request")
 FLAGS = tf.app.flags.FLAGS
 
 
 def main():
-  host = FLAGS.host
-  port = FLAGS.port
-  model_name = FLAGS.model_name
-  model_version = FLAGS.model_version
-  request_timeout = FLAGS.request_timeout
-
   # Generate inference data
   keys = numpy.asarray([1, 2, 3])
   keys_tensor_proto = tf.contrib.util.make_tensor_proto(keys, dtype=tf.int32)
   features = numpy.asarray(
       [[1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 1, 1, 1, 1, 1, 1, 1, 1],
        [9, 8, 7, 6, 5, 4, 3, 2, 1], [9, 9, 9, 9, 9, 9, 9, 9, 9]])
-  features_tensor_proto = tf.contrib.util.make_tensor_proto(features,
-                                                            dtype=tf.float32)
+  features_tensor_proto = tf.contrib.util.make_tensor_proto(
+      features, dtype=tf.float32)
 
-  # Create gRPC client and request
-  channel = implementations.insecure_channel(host, port)
+  # Create gRPC client
+  channel = implementations.insecure_channel(FLAGS.host, FLAGS.port)
   stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
   request = predict_pb2.PredictRequest()
-  request.model_spec.name = model_name
-  if model_version > 0:
-    request.model_spec.version.value = model_version
-  request.inputs['keys'].CopyFrom(keys_tensor_proto)
-  request.inputs['features'].CopyFrom(features_tensor_proto)
+  request.model_spec.name = FLAGS.model_name
+  if FLAGS.model_version > 0:
+    request.model_spec.version.value = FLAGS.model_version
+  if FLAGS.signature_name != "":
+    request.model_spec.signature_name = FLAGS.signature_name
+  request.inputs["keys"].CopyFrom(keys_tensor_proto)
+  request.inputs["features"].CopyFrom(features_tensor_proto)
 
   # Send request
-  result = stub.Predict(request, request_timeout)
+  result = stub.Predict(request, FLAGS.request_timeout)
   print(result)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
   main()