localhost_embeddings_provider.py

from flask import  Flask
from flask import request
from flask import redirect, url_for, jsonify
import traceback
from pprint import pprint

from    gensim.models.keyedvectors  import KeyedVectors

print('Loading Embeddings')
w2v_bin_path    = '/home/dpappas/bioasq_all/pubmed2018_w2v_30D.bin'
wv              = KeyedVectors.load_word2vec_format(w2v_bin_path, binary=True)
print('Done')

app         = Flask(__name__)
app.config['JSON_SORT_KEYS'] = False

@app.route('/get_embeds', methods=['GET','POST'])
def get_data_using_slug():
    try:
        app.logger.debug("received...")
        data    = request.get_json()
        pprint(data)
        token   = data['token']
        ret     = {'emb': wv[token].tolist()}
        app.logger.debug(ret)
        return jsonify(ret)
    except Exception as e:
        app.logger.debug(str(e))
        traceback.print_exc()
        ret = {'success': 0, 'message': str(e)+'\n'+traceback.format_exc()}
        app.logger.debug(ret)
        return jsonify(ret)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=9250, debug=False, threaded=True)
    # app.run(host='localhost', port=9250, debug=True, threaded=True)

'''

import json, urllib 
import numpy as np
data            = {'token' : 'the'}
params          = json.dumps(data).encode('utf8')
req             = urllib.request.Request('http://localhost:9250/get_embeds', data=params, headers={'content-type': 'application/json'})
response        = urllib.request.urlopen(req)
resp            = response.read().decode('utf8') 
pprint(np.array(json.loads(resp)['emb']))

'''