def predict():
args = get_args(request)
sentence = args.get("keyword", "from ")
model_name = args.get("model", "char")
if model_name not in models:
models[model_name] = get_model(model_name)
suggestions = neural_complete(models[model_name], sentence, [0.2, 0.5, 1])
return jsonify({"data": {"results": [x.strip() for x in suggestions]}})
import os
from cors import crossdomain
from flask import Flask, jsonify, request
from neural_complete import neural_complete
from neural_complete import get_model
def read_models(base_path="models/"):
return set([x.split(".")[0] for x in os.listdir(base_path)])
app = Flask(__name__)
models = {x: get_model(x) for x in read_models()}
def get_args(req):
if request.method == 'POST':
args = request.json
elif request.method == "GET":
args = request.args
return args
# url设置：predict
@app.route("/predict", methods=["GET", "POST", "OPTIONS"])
@crossdomain(origin='*', headers="Content-Type")
# 响应函数：predict，预测
def predict():
args = get_args(request)