Remove warning messages

+import logging
+import os
 import numpy as np
 import numpy as np
 import tensorflow as tf
 import tensorflow as tf
-from tensorflow import logging
 from tensorflow import gfile
 from tensorflow import gfile
 import operator
 import operator
 import src.pb_util as pbutil
 import src.pb_util as pbutil
 import src.video_recommender as recommender
 import src.video_recommender as recommender
 import src.video_util as videoutil
 import src.video_util as videoutil
+import json
+import urllib3
+
+logging.disable(logging.WARNING)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
 
 # Old model
 # Old model
 MODEL_PATH = "./model/inference_model/segment_inference_model"
 MODEL_PATH = "./model/inference_model/segment_inference_model"
@@ -13,12 +20,6 @@ TAG_VECTOR_MODEL_PATH = "./model/tag_vectors.model"
 VIDEO_VECTOR_MODEL_PATH = "./model/video_vectors.model"
 VIDEO_VECTOR_MODEL_PATH = "./model/video_vectors.model"
 VIDEO_TAGS_PATH = "./statics/kaggle_solution_40k.csv"
 VIDEO_TAGS_PATH = "./statics/kaggle_solution_40k.csv"
 
 
-# New model
-# MODEL_PATH = "./new_model/inference_model/segment_inference_model"
-# TAG_VECTOR_MODEL_PATH = "./new_model/tag_vectors.model"
-# VIDEO_VECTOR_MODEL_PATH = "./new_model/video_vectors.model"
-# VIDEO_TAGS_PATH = "./statics/new_kaggle_solution_40k.csv"
-
 # Define static file paths.
 # Define static file paths.
 SEGMENT_LABEL_PATH = "./statics/segment_label_ids.csv"
 SEGMENT_LABEL_PATH = "./statics/segment_label_ids.csv"
 VOCAB_PATH = "./statics/vocabulary.csv"
 VOCAB_PATH = "./statics/vocabulary.csv"
@@ -27,7 +28,6 @@ VOCAB_PATH = "./statics/vocabulary.csv"
 TAG_TOP_K = 5
 TAG_TOP_K = 5
 VIDEO_TOP_K = 10
 VIDEO_TOP_K = 10
 
 
-
 def get_segments(batch_video_mtx, batch_num_frames, segment_size):
 def get_segments(batch_video_mtx, batch_num_frames, segment_size):
     """Get segment-level inputs from frame-level features."""
     """Get segment-level inputs from frame-level features."""
     video_batch_size = batch_video_mtx.shape[0]
     video_batch_size = batch_video_mtx.shape[0]
@@ -95,7 +95,9 @@ def normalize_tag(tag):
 def inference_pb(file_path, threshold):
 def inference_pb(file_path, threshold):
     VIDEO_TOP_K = int(threshold)
     VIDEO_TOP_K = int(threshold)
     inference_result = {}
     inference_result = {}
-    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
+
+    graph = tf.Graph()
+    with tf.Session(graph=graph, config=tf.ConfigProto(allow_soft_placement=True)) as sess:
 
 
         # 0. Import SequenceExample type target from pb.
         # 0. Import SequenceExample type target from pb.
         target_video = pbutil.convert_pb(file_path)
         target_video = pbutil.convert_pb(file_path)
@@ -222,10 +224,22 @@ def inference_pb(file_path, threshold):
         # 6. Dispose instances.
         # 6. Dispose instances.
         sess.close()
         sess.close()
 
 
+    tf.reset_default_graph()
     return inference_result
     return inference_result
 
 
 
 
 if __name__ == '__main__':
 if __name__ == '__main__':
     filepath = "./featuremaps/features.pb"
     filepath = "./featuremaps/features.pb"
     result = inference_pb(filepath, 5)
     result = inference_pb(filepath, 5)
-    print(result)
+    print("=============== Old Model ===============")
+    print(result["tag_result"])
+    print(json.dumps(result["video_result"], sort_keys=True, indent=2))
+
+    # New model
+    MODEL_PATH = "./new_model/inference_model/segment_inference_model"
+    TAG_VECTOR_MODEL_PATH = "./new_model/tag_vectors.model"
+    VIDEO_VECTOR_MODEL_PATH = "./new_model/video_vectors.model"
+    VIDEO_TAGS_PATH = "./statics/new_kaggle_solution_40k.csv"
+    result = inference_pb(filepath, 5)
+    print("=============== New Model ===============")
+    print(json.dumps(result, sort_keys=True, indent=2))