你可以在NPM上查看我的套餐 https://www.npmjs.com/package/@guydev/native-python

它提供了一种非常简单而强大的方式来从node运行python函数

import { runFunction } from '@guydev/native-python'

const example = async () => {
   const input = [1,[1,2,3],{'foo':'bar'}]
   const { error, data } = await runFunction('/path/to/file.py','hello_world', '/path/to/python', input)

   // error will be null if no error occured.
   if (error) {
       console.log('Error: ', error)
   }

   else {
       console.log('Success: ', data)
       // prints data or null if function has no return value
   }
}

python模块

# module: file.py

def hello_world(a,b,c):
    print( type(a), a) 
    # <class 'int'>, 1

    print(type(b),b)
    # <class 'list'>, [1,2,3]

    print(type(c),c)
    # <class 'dict'>, {'foo':'bar'}

有Python背景，想要在Node.js应用程序中集成机器学习模型的人:

它使用了child_process核心模块:

const express = require('express')
const app = express()

app.get('/', (req, res) => {

    const { spawn } = require('child_process');
    const pyProg = spawn('python', ['./../pypy.py']);

    pyProg.stdout.on('data', function(data) {

        console.log(data.toString());
        res.write(data);
        res.end('end');
    });
})

app.listen(4000, () => console.log('Application listening on port 4000!'))

它不需要Python脚本中的sys模块。

下面是使用Promise执行任务的更模块化的方式:

const express = require('express')
const app = express()

let runPy = new Promise(function(success, nosuccess) {

    const { spawn } = require('child_process');
    const pyprog = spawn('python', ['./../pypy.py']);

    pyprog.stdout.on('data', function(data) {

        success(data);
    });

    pyprog.stderr.on('data', (data) => {

        nosuccess(data);
    });
});

app.get('/', (req, res) => {

    res.write('welcome\n');

    runPy.then(function(fromRunpy) {
        console.log(fromRunpy.toString());
        res.end(fromRunpy);
    });
})

app.listen(4000, () => console.log('Application listening on port 4000!'))

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

你可以在NPM上查看我的套餐 https://www.npmjs.com/package/@guydev/native-python

它提供了一种非常简单而强大的方式来从node运行python函数

import { runFunction } from '@guydev/native-python'

const example = async () => {
   const input = [1,[1,2,3],{'foo':'bar'}]
   const { error, data } = await runFunction('/path/to/file.py','hello_world', '/path/to/python', input)

   // error will be null if no error occured.
   if (error) {
       console.log('Error: ', error)
   }

   else {
       console.log('Success: ', data)
       // prints data or null if function has no return value
   }
}

python模块

# module: file.py

def hello_world(a,b,c):
    print( type(a), a) 
    # <class 'int'>, 1

    print(type(b),b)
    # <class 'list'>, [1,2,3]

    print(type(c),c)
    # <class 'dict'>, {'foo':'bar'}

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。

许多例子都是过时的，并且涉及复杂的设置。您可以尝试JSPyBridge/pythonia(完全披露:我是作者)。它是一种普通的JS，可以让你操作外部Python对象，就好像它们存在于JS中一样。事实上，它实现了互操作性，因此Python代码可以通过回调和传递函数返回调用JS。

numpy + matplotlib的例子，用ES6导入系统:

import { py, python } from 'pythonia'
const np = await python('numpy')
const plot = await python('matplotlib.pyplot')

// Fixing random state for reproducibility
await np.random.seed(19680801)
const [mu, sigma] = [100, 15]
// Inline expression evaluation for operator overloading
const x = await py`${mu} + ${sigma} * ${np.random.randn(10000)}`

// the histogram of the data
const [n, bins, patches] = await plot.hist$(x, 50, { density: true, facecolor: 'g', alpha: 0.75 })
console.log('Distribution', await n) // Always await for all Python access
await plot.show()
python.exit()

通过CommonJS(没有顶级await):

const { py, python } = require('pythonia')
async function main() {
  const np = await python('numpy')
  const plot = await python('matplotlib.pyplot')
  ...
  // the rest of the code
}
main().then(() => python.exit()) // If you don't call this, the process won't quit by itself.