def __init__(self, host='0.0.0.0', port=8000, server='paste'):
import bottle
self.host = host
self.port = port
self.server = server
self.graph = tf.get_default_graph()
self.sess = K.get_session()
self.set_session = K.set_session
self.bottle = bottle
def __init__(self, host='0.0.0.0', port=8000, server='paste'):
import tensorflow as tf
from bert4keras.backend import K
import bottle
self.host = host
self.port = port
self.server = server
self.graph = tf.get_default_graph()
self.sess = K.get_session()
self.set_session = K.set_session
self.bottle = bottle
# bert4keras加载CDial-GPT
import numpy as np
from bert4keras.models import build_transformer_model
from bert4keras.tokenizers import Tokenizer
from bert4keras.snippets import AutoRegressiveDecoder
from bert4keras.snippets import uniout
import tensorflow as tf
from bert4keras.backend import K
from flask import Flask, request, render_template, send_file
app = Flask(__name__)
graph = tf.get_default_graph()
sess = K.get_session()
set_session = K.set_session
config_path = r'GPT_large-tf\gpt_config.json'
checkpoint_path = r'GPT_large-tf\gpt_model.ckpt'
dict_path = r'GPT_large-tf\vocab.txt'
tokenizer = Tokenizer(dict_path, do_lower_case=True) # 建立分词器
speakers = [
tokenizer.token_to_id('[speaker1]'),
tokenizer.token_to_id('[speaker2]')
]
model = build_transformer_model(config_path=config_path,
checkpoint_path=checkpoint_path,
model='GPT_OpenAI') # 建立模型，加载权重
