我有一个Express Node.js应用程序,但我也有一个机器学习算法在Python中使用。是否有一种方法可以从我的Node.js应用程序调用Python函数来利用机器学习库的强大功能?
当前回答
之前的大多数答案都将承诺的成功称为on(“数据”),这不是正确的方法,因为如果你收到很多数据,你只会得到第一部分。相反,你必须在end事件上做。
const { spawn } = require('child_process');
const pythonDir = (__dirname + "/../pythonCode/"); // Path of python script folder
const python = pythonDir + "pythonEnv/bin/python"; // Path of the Python interpreter
/** remove warning that you don't care about */
function cleanWarning(error) {
return error.replace(/Detector is not able to detect the language reliably.\n/g,"");
}
function callPython(scriptName, args) {
return new Promise(function(success, reject) {
const script = pythonDir + scriptName;
const pyArgs = [script, JSON.stringify(args) ]
const pyprog = spawn(python, pyArgs );
let result = "";
let resultError = "";
pyprog.stdout.on('data', function(data) {
result += data.toString();
});
pyprog.stderr.on('data', (data) => {
resultError += cleanWarning(data.toString());
});
pyprog.stdout.on("end", function(){
if(resultError == "") {
success(JSON.parse(result));
}else{
console.error(`Python error, you can reproduce the error with: \n${python} ${script} ${pyArgs.join(" ")}`);
const error = new Error(resultError);
console.error(error);
reject(resultError);
}
})
});
}
module.exports.callPython = callPython;
电话:
const pythonCaller = require("../core/pythonCaller");
const result = await pythonCaller.callPython("preprocessorSentiment.py", {"thekeyYouwant": value});
python:
try:
argu = json.loads(sys.argv[1])
except:
raise Exception("error while loading argument")
其他回答
/*eslint-env es6*/
/*global require*/
/*global console*/
var express = require('express');
var app = express();
// Creates a server which runs on port 3000 and
// can be accessed through localhost:3000
app.listen(3000, function() {
console.log('server running on port 3000');
} )
app.get('/name', function(req, res) {
console.log('Running');
// Use child_process.spawn method from
// child_process module and assign it
// to variable spawn
var spawn = require("child_process").spawn;
// Parameters passed in spawn -
// 1. type_of_script
// 2. list containing Path of the script
// and arguments for the script
// E.g : http://localhost:3000/name?firstname=Levente
var process = spawn('python',['apiTest.py',
req.query.firstname]);
// Takes stdout data from script which executed
// with arguments and send this data to res object
var output = '';
process.stdout.on('data', function(data) {
console.log("Sending Info")
res.end(data.toString('utf8'));
});
console.log(output);
});
这对我很管用。必须将python.exe添加到此代码段的路径变量中。另外,确保你的python脚本在你的项目文件夹中。
你可以把你的python编译,然后像调用javascript一样调用它。我已经成功地为screeps做了这件事,甚至让它在浏览器中运行la brython。
Boa很适合您的需求,请参阅扩展Python tensorflow keras的示例。JavaScript中的顺序类。
const fs = require('fs');
const boa = require('@pipcook/boa');
const { tuple, enumerate } = boa.builtins();
const tf = boa.import('tensorflow');
const tfds = boa.import('tensorflow_datasets');
const { keras } = tf;
const { layers } = keras;
const [
[ train_data, test_data ],
info
] = tfds.load('imdb_reviews/subwords8k', boa.kwargs({
split: tuple([ tfds.Split.TRAIN, tfds.Split.TEST ]),
with_info: true,
as_supervised: true
}));
const encoder = info.features['text'].encoder;
const padded_shapes = tuple([
[ null ], tuple([])
]);
const train_batches = train_data.shuffle(1000)
.padded_batch(10, boa.kwargs({ padded_shapes }));
const test_batches = test_data.shuffle(1000)
.padded_batch(10, boa.kwargs({ padded_shapes }));
const embedding_dim = 16;
const model = keras.Sequential([
layers.Embedding(encoder.vocab_size, embedding_dim),
layers.GlobalAveragePooling1D(),
layers.Dense(16, boa.kwargs({ activation: 'relu' })),
layers.Dense(1, boa.kwargs({ activation: 'sigmoid' }))
]);
model.summary();
model.compile(boa.kwargs({
optimizer: 'adam',
loss: 'binary_crossentropy',
metrics: [ 'accuracy' ]
}));
完整的示例在:https://github.com/alibaba/pipcook/blob/master/example/boa/tf2/word-embedding.js
我在另一个项目pipook中使用了Boa,这是为了解决JavaScript开发人员的机器学习问题,我们通过Boa库在Python生态系统(tensorflow,keras,pytorch)上实现了ML/DL模型。
之前的大多数答案都将承诺的成功称为on(“数据”),这不是正确的方法,因为如果你收到很多数据,你只会得到第一部分。相反,你必须在end事件上做。
const { spawn } = require('child_process');
const pythonDir = (__dirname + "/../pythonCode/"); // Path of python script folder
const python = pythonDir + "pythonEnv/bin/python"; // Path of the Python interpreter
/** remove warning that you don't care about */
function cleanWarning(error) {
return error.replace(/Detector is not able to detect the language reliably.\n/g,"");
}
function callPython(scriptName, args) {
return new Promise(function(success, reject) {
const script = pythonDir + scriptName;
const pyArgs = [script, JSON.stringify(args) ]
const pyprog = spawn(python, pyArgs );
let result = "";
let resultError = "";
pyprog.stdout.on('data', function(data) {
result += data.toString();
});
pyprog.stderr.on('data', (data) => {
resultError += cleanWarning(data.toString());
});
pyprog.stdout.on("end", function(){
if(resultError == "") {
success(JSON.parse(result));
}else{
console.error(`Python error, you can reproduce the error with: \n${python} ${script} ${pyArgs.join(" ")}`);
const error = new Error(resultError);
console.error(error);
reject(resultError);
}
})
});
}
module.exports.callPython = callPython;
电话:
const pythonCaller = require("../core/pythonCaller");
const result = await pythonCaller.callPython("preprocessorSentiment.py", {"thekeyYouwant": value});
python:
try:
argu = json.loads(sys.argv[1])
except:
raise Exception("error while loading argument")
我在节点10和子进程1.0.2上。来自python的数据是一个字节数组,必须进行转换。这是另一个用python发出http请求的快速示例。
node
const process = spawn("python", ["services/request.py", "https://www.google.com"])
return new Promise((resolve, reject) =>{
process.stdout.on("data", data =>{
resolve(data.toString()); // <------------ by default converts to utf-8
})
process.stderr.on("data", reject)
})
request.py
import urllib.request
import sys
def karl_morrison_is_a_pedant():
response = urllib.request.urlopen(sys.argv[1])
html = response.read()
print(html)
sys.stdout.flush()
karl_morrison_is_a_pedant()
p.s.不是一个人为的例子,因为节点的http模块不加载我需要做的一些请求
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