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@ysyun
Created August 21, 2018 07:52
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webserver.py snippet of data2vis
def run_inference():
# tf.reset_default_graph()
with graph.as_default():
saver = tf.train.Saver()
checkpoint_path = loaded_checkpoint_path
if not checkpoint_path:
checkpoint_path = tf.train.latest_checkpoint(model_dir_input)
def session_init_op(_scaffold, sess):
saver.restore(sess, checkpoint_path)
tf.logging.info("Restored model from %s", checkpoint_path)
scaffold = tf.train.Scaffold(init_fn=session_init_op)
session_creator = tf.train.ChiefSessionCreator(scaffold=scaffold)
with tf.train.MonitoredSession(
session_creator=session_creator, hooks=hooks) as sess:
sess.run([])
# print(" ****** decoded string ", decoded_string)
return decoded_string
@app.route("/examplesdata")
def examplesdata():
source_data = data_utils.load_test_dataset()
f_names = data_utils.generate_field_types(source_data)
data_utils.forward_norm(source_data, destination_file, f_names)
print('1 >>>>')
print('source data: ', source_data)
run_inference()
# Perform post processing - backward normalization
# decoded_post_array = []
# for row in decoded_string:
# decoded_post = data_utils.backward_norm(row, f_names)
# decoded_post_array.append(decoded_post)
decoded_string_post = data_utils.backward_norm(decoded_string[0], f_names)
print('2 >>>>')
print('f_names: ', f_names)
print('decoded string post: ', decoded_string_post)
try:
vega_spec = json.loads(decoded_string_post)
vega_spec["data"] = {"values": source_data}
response_payload = {"vegaspec": vega_spec, "status": True}
print('3 >>>>')
print('response: ', response_payload)
except JSONDecodeError as e:
response_payload = {
"status": False,
"reason": "Model did not produce a valid vegalite JSON",
"vegaspec": decoded_string
}
return jsonify(response_payload)
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