Python Flask for cnn-text-classification tensorflow POST request format - python-3.x

Question

I am doing cnn-text-classification-tf. My aim is to do prediction from frozen graph.
My question is how can the prediction be done from the frozen graph. I found a great tutorial https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc. where he is implementing frozen graph using flask.
I am using flask code like below to do prediction.
import json, argparse, time
import tensorflow as tf
from linkedin import load_graph
from flask import Flask, request
from flask_cors import CORS
##################################################
# API part
##################################################
app = Flask(__name__)
cors = CORS(app)
#app.route("/api/predict", methods=['POST'])
def predict():
start = time.time()
data = request.data.decode("utf-8")
if data == "":
params = request.form
x_in = json.loads(params['x'])
else:
params = json.loads(data)
x_in = params['x']
##################################################
# Tensorflow part
##################################################
y_out = persistent_sess.run(y, feed_dict={
x: x_in
# x: [[3, 5, 7, 4, 5, 1, 1, 1, 1, 1]] # < 45
})
##################################################
# END Tensorflow part
##################################################
json_data = json.dumps({'y': y_out.tolist()})
print("Time spent handling the request: %f" % (time.time() - start))
return json_data
##################################################
# END API part
##################################################
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename", default="frozen_model2.pb", type=str, help="Frozen model file to import")
parser.add_argument("--gpu_memory", default=.2, type=float, help="GPU memory per process")
args = parser.parse_args()
##################################################
# Tensorflow part
##################################################
print('Loading the model')
graph = load_graph(args.frozen_model_filename)
x = graph.get_tensor_by_name('prefix/input_x:0')
y = graph.get_tensor_by_name('prefix/output/predictions:0')
print('Starting Session, setting the GPU memory usage to %f' % args.gpu_memory)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
##################################################
# END Tensorflow part
##################################################
print('Starting the API')
app.run()
I am using terminal on MacOS to run this script
After running this I am using postman to POST the request:
How do I correctly frame this request to get proper response. What is the exact input needed in Postman in the body?

Answer 1

In Postman, you are sending a POST request and data in json format, so you would need to do changes in code.
for getting data, first do the validation as follows:
if not 'data' in request.json:
abort(400)
After that you can access the string as follows:
x_in = request.json["data"]
But in case, you want to do changes in the request then you can try sending data as follows:
{
"data" : {
"x" : "good movie it was"
}
}
This should work, in case not, then do let me know.