# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import time import flask import torch from flask import Flask, json, request from flask_cors import CORS import nemo.collections.nlp as nemo_nlp from nemo.utils import logging MODELS_DICT = {} model = None api = Flask(__name__) CORS(api) def initialize(config_file_path: str): """ Loads 'language-pair to NMT model mapping' """ __MODELS_DICT = None logging.info("Starting NMT service") logging.info(f"I will attempt to load all the models listed in {config_file_path}.") logging.info(f"Edit {config_file_path} to disable models you don't need.") if torch.cuda.is_available(): logging.info("CUDA is available. Running on GPU") else: logging.info("CUDA is not available. Defaulting to CPUs") # read config with open(config_file_path) as f: __MODELS_DICT = json.load(f) if __MODELS_DICT is not None: for key, value in __MODELS_DICT.items(): logging.info(f"Loading model for {key} from file: {value}") if value.startswith("NGC/"): model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name=value[4:]) else: model = nemo_nlp.models.machine_translation.MTEncDecModel.restore_from(restore_path=value) if torch.cuda.is_available(): model = model.cuda() MODELS_DICT[key] = model else: raise ValueError("Did not find the config.json or it was empty") logging.info("NMT service started") @api.route('/translate', methods=['GET', 'POST', 'OPTIONS']) def get_translation(): try: time_s = time.time() langpair = request.args["langpair"] src = request.args["text"] do_moses = request.args.get('do_moses', False) if langpair in MODELS_DICT: if do_moses: result = MODELS_DICT[langpair].translate( [src], source_lang=langpair.split('-')[0], target_lang=langpair.split('-')[1] ) else: result = MODELS_DICT[langpair].translate([src]) duration = time.time() - time_s logging.info( f"Translated in {duration}. Input was: {request.args['text']} <############> Translation was: {result[0]}" ) res = {'translation': result[0]} response = flask.jsonify(res) response.headers.add('Access-Control-Allow-Origin', '*') return response else: logging.error(f"Got the following langpair: {langpair} which was not found") except Exception as ex: res = {'translation': str(ex)} response = flask.jsonify(res) response.headers.add('Access-Control-Allow-Origin', '*') return res if __name__ == '__main__': initialize('config.json') api.run(host='0.0.0.0')