PHP微信机器人

github https://github.com/HanSon/vbot
https://github.com/HanSon/my-vbot

修改文件 Example.php

$this->config =  $default_config;//array_merge($default_config, $this->config);

修改了一个文件，以实现收到文字回复笔画的功能
MessageHandler.php

如需主动发起消息请安装swoole，并修改config文件。

pecl install swoole

<?php

namespace Hanson\MyVbot;

use Hanson\MyVbot\Handlers\Contact\ColleagueGroup;
use Hanson\MyVbot\Handlers\Contact\ExperienceGroup;
use Hanson\MyVbot\Handlers\Contact\FeedbackGroup;
use Hanson\MyVbot\Handlers\Contact\Hanson;
use Hanson\MyVbot\Handlers\Type\RecallType;
use Hanson\MyVbot\Handlers\Type\TextType;
use Hanson\Vbot\Contact\Friends;
use Hanson\Vbot\Contact\Groups;
use Hanson\Vbot\Contact\Members;

use Hanson\Vbot\Message\Emoticon;
use Hanson\Vbot\Message\Text;
use Illuminate\Support\Collection;

class MessageHandler
{
    public static function messageHandler(Collection $message)
    {
        /** @var Friends $friends */
        $friends = vbot('friends');

        /** @var Members $members */
        $members = vbot('members');

        /** @var Groups $groups */
        $groups = vbot('groups');

        Hanson::messageHandler($message, $friends, $groups);
        ColleagueGroup::messageHandler($message, $friends, $groups);
        FeedbackGroup::messageHandler($message, $friends, $groups);
        ExperienceGroup::messageHandler($message, $friends, $groups);

        TextType::messageHandler($message, $friends, $groups);
        RecallType::messageHandler($message);

        if ($message['type'] === 'new_friend') {
            Text::send($message['from']['UserName'], '客官，等你很久了！感谢跟 vbot 交朋友，如果可以帮我点个star，谢谢了！https://github.com/HanSon/vbot');
            $groups->addMember($groups->getUsernameByNickname('Vbot 体验群'), $message['from']['UserName']);
            Text::send($message['from']['UserName'], '现在拉你进去vbot的测试群，进去后为了避免轰炸记得设置免骚扰哦！如果被不小心踢出群，跟我说声“拉我”我就会拉你进群的了。');
        }

        if ($message['type'] === 'emoticon' && random_int(0, 1)) {
            Emoticon::sendRandom($message['from']['UserName']);
        }

        // @todo
        if ($message['type'] === 'official') {
            vbot('console')->log('收到公众号消息:'.$message['title'].$message['description'].
                $message['app'].$message['url']);
        }

        if ($message['type'] === 'request_friend') {
            vbot('console')->log('收到好友申请:'.$message['info']['Content'].$message['avatar']);
            if (in_array($message['info']['Content'], ['echo', 'print_r', 'var_dump', 'print'])) {
                $friends->approve($message);
            }
        }
        //print_r($message);
        $re = 0;
        if($message["fromType"] == "Friend"){
            $nick = $message['from']['NickName'];
            $re = 1;
        }

        if($message["fromType"] == "Group"){
            $nick = $message['sender']['NickName'];
            if(@$message['isAt']){
                $re = 1;
            }
        }
        if($re ==1 ){

            $zi = mb_substr($message["message"],0,1,'utf-8');
            $uni = self::unicode_encode($zi);


            $var = trim($uni);
            $len = strlen($var)-1;
            $las = $var{$len};
            $url = "http://www.shufaji.com/datafile/bd/gif/".$las."/".$uni.".gif";
            //Text::send($message['from']['UserName'], "@".$nick." ".$url);
            if(!is_file(__DIR__."/img/".$uni.'.gif')){

                $img = @file_get_contents($url);

                if(!empty($img)){
                    file_put_contents(__DIR__."/img/".$uni.'.gif',$img);
                    Emoticon::send($message['from']['UserName'], __DIR__."/img/".$uni.".gif");

                }else{
                    Text::send($message['from']['UserName'], "@".$nick." 找不到这个字的笔顺".$url);
                }
            }else{
                Emoticon::send($message['from']['UserName'], __DIR__."/img/".$uni.".gif");
            }
        }


    }
    private static function unicode_encode($name)
    {
        $name = iconv('UTF-8', 'UCS-2', $name);
        $len = strlen($name);
        $str = '';
        for ($i = 0; $i < $len - 1; $i = $i + 2)
        {
            $c = $name[$i];
            $c2 = $name[$i + 1];
            if (ord($c) > 0)
            {    // 两个字节的文字
                $s1 = base_convert(ord($c), 10, 16);
                $s2 = base_convert(ord($c2), 10, 16);

                if(ord($c) < 16){
                    $s1 = "0".$s1;
                }
                if(ord($c2) < 16){
                    $s2 = "0".$s2;
                }
                $str .= $s1 . $s2;
            }
            else
            {
                $str .= $c2;
            }

        }
        return $str;
    }
}

itchat 调试完毕后，开始折腾聊天的server

https://ask.julyedu.com/question/7410

首先准备好  torch 环境，然后安装 nn,rnn,async

sudo ~/torch/install/bin/luarocks install nn
sudo ~/torch/install/bin/luarocks install rnn
sudo ~/torch/install/bin/luarocks install async penlight cutorch cunn

下载程序和语料

git clone --recursive https://github.com/rustcbf/chatbot-zh-torch7 #代码
git clone --recursive https://github.com/rustcbf/dgk_lost_conv #语料
git clone --recursive https://github.com/chenb67/neuralconvo #以上两个在此源码进行改进，可作为参考

将 dgk_lost_conv 里的  xiaohuangji50w_fenciA.zip 解压放到外层目录

th train.lua –cuda –dataset 5000 –hiddenSize 100

报错

-- Epoch 1 / 30

/root/torch/install/bin/luajit: ./seq2seq.lua:50: attempt to call field 'recursiveCopy' (a nil value)
stack traceback:
	./seq2seq.lua:50: in function 'forwardConnect'
	./seq2seq.lua:67: in function 'train'
	train.lua:90: in main chunk
	[C]: in function 'dofile'
	/root/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
	[C]: at 0x00405d50

修改 seq2seq.lua 如下 （50 – 70 行间）

function Seq2Seq:forwardConnect(inputSeqLen)
  self.decoderLSTM.userPrevOutput =
    --nn.rnn.recursiveCopy(self.decoderLSTM.userPrevOutput, self.encoderLSTM.outputs[inputSeqLen])
    nn.utils.recursiveCopy(self.decoderLSTM.userPrevOutput, self.encoderLSTM.outputs[inputSeqLen])
  self.decoderLSTM.userPrevCell =
    nn.utils.recursiveCopy(self.decoderLSTM.userPrevCell, self.encoderLSTM.cells[inputSeqLen])
end

--[[ Backward coupling: Copy decoder gradients to encoder LSTM ]]--
function Seq2Seq:backwardConnect()
  if(self.encoderLSTM.userNextGradCell ~= nil) then
    self.encoderLSTM.userNextGradCell =
      nn.utils.recursiveCopy(self.encoderLSTM.userNextGradCell, self.decoderLSTM.userGradPrevCell)
  end
  if(self.encoderLSTM.gradPrevOutput ~= nil) then
    self.encoderLSTM.gradPrevOutput =
      nn.utils.recursiveCopy(self.encoderLSTM.gradPrevOutput, self.decoderLSTM.userGradPrevOutput)
  end
end

训练之，1080ti 一轮大概 两个多小时。。。 30轮估计需要70小时。妇女节后见了。

eval.lua 的时候报错，不明所以，先放弃这个了，试试别的。

/root/torch/install/bin/luajit: /root/torch/install/share/lua/5.1/nn/Container.lua:67:
In 3 module of nn.Sequential:
/root/torch/install/share/lua/5.1/torch/Tensor.lua:466: Wrong size for view. Input size: 100. Output size: 6561
stack traceback:
 [C]: in function 'error'
 /root/torch/install/share/lua/5.1/torch/Tensor.lua:466: in function 'view'
 /root/torch/install/share/lua/5.1/rnn/utils.lua:191: in function 'recursiveZeroMask'
 /root/torch/install/share/lua/5.1/rnn/MaskZero.lua:37: in function 'updateOutput'
 /root/torch/install/share/lua/5.1/rnn/Recursor.lua:13: in function '_updateOutput'
 /root/torch/install/share/lua/5.1/rnn/AbstractRecurrent.lua:50: in function 'updateOutput'
 /root/torch/install/share/lua/5.1/rnn/Sequencer.lua:53: in function </root/torch/install/share/lua/5.1/rnn/Sequencer.lua:34>
 [C]: in function 'xpcall'
 /root/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
 /root/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
 ./seq2seq.lua:115: in function 'eval'
 eval.lua:90: in function 'say'
 eval.lua:105: in main chunk
 [C]: in function 'dofile'
 /root/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
 [C]: at 0x00405d50

WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above.
stack traceback:
 [C]: in function 'error'
 /root/torch/install/share/lua/5.1/nn/Container.lua:67: in function 'rethrowErrors'
 /root/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
 ./seq2seq.lua:115: in function 'eval'
 eval.lua:90: in function 'say'
 eval.lua:105: in main chunk
 [C]: in function 'dofile'
 /root/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
 [C]: at 0x00405d50

换一个试试 ，tensorflow 的，而且还比较新

git:   https://github.com/qhduan/just_another_seq2seq

包含还有一个英译汉的功能。

把之前下载的预料库中的 dgk_shooter_min.conv.zip 解压缩放到 chatbot 目录里

python3 train.py 训练之。

20轮，一轮大概30-40分钟（1070 显卡）

训练完之后执行  Python3 test.py 测试。

注意，tensorflow 在 1.4.1 下。

并修改 test.py  否则 sequence_to_sequence   会报

NotFoundError (see above for traceback): Key decoder/multi_rnn_cell/cell_0/Attention_Wrapper/lstm_cell/bias not found in checkpoint

    test(
        bidirectional=False,
        cell_type='lstm',
        depth=2,
        attention_type='Bahdanau',
        use_residual=False,
        use_dropout=False,
        time_major=False,
        hidden_units=512
    )

聊天效果，chatbot_ad 的还行

训练chatbot_ad 的时候，readme 少了一步  train_tfidf.py  。

Input Chat Sentence:我生病了
rl: ['我', '睡', '着', '了', '</s>', '</s>', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:我想我爱你
rl: ['我', '不', '知', '道', '你', '在', '说', '什', '么', '</s>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:你多大了？
rl: ['你', '看', '上', '去', '不', '错', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:你好吗？
rl: ['很', '高', '兴', '见', '到', '你', '</s>', '</s>', '<unk>', '<unk>']
Input Chat Sentence:什么时间了
rl: ['你', '要', '去', '哪', '儿', '</s>', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:去北京
rl: ['维', '克', '多', '，', '过', '来', '</s>', '</s>', '</s>', '</s>', '<unk>', '<unk>']
Input Chat Sentence:去哪？
rl: ['我', '们', '得', '走', '了', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:走
rl: ['我', '们', '得', '走', '了', '</s>', '</s>', '<unk>']
Input Chat Sentence:走了
rl: ['你', '们', '都', '走', '了', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:去哪
rl: ['我', '也', '不', '知', '道', '</s>', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:干啥
rl: ['你', '在', '干', '啥', '啊', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:他是谁？
rl: ['不', '知', '道', '为', '什', '么', '</s>', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:你是谁？
rl: ['我', '是', '麦', '克', '墨', '菲', '医', '生', '</s>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:你哎我 吗？
rl: ['我', '有', '话', '跟', '你', '说', '</s>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:你爱我 吗？
rl: ['什', '么', '东', '西', '？', '</s>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:你爱我吗？
rl: ['我', '爱', '你', '，', '宝', '贝', '</s>', '<unk>', '<unk>', '<unk>', '<unk>']
Input Chat Sentence:

chatbot_ad  用 bottle 改造了一个 url api接口用于和 itchat 对接。代码如下。

# -*- coding: utf-8 -*-
"""
对SequenceToSequence模型进行基本的参数组合测试
"""

import sys
import random
import pickle

import numpy as np
import tensorflow as tf
import bottle

sys.path.append('..')

from data_utils import batch_flow
from sequence_to_sequence import SequenceToSequence
from word_sequence import WordSequence # pylint: disable=unused-variable
random.seed(0)
np.random.seed(0)
tf.set_random_seed(0)
_, _, ws = pickle.load(open('chatbot.pkl', 'rb'))
config = tf.ConfigProto(
        device_count={'CPU': 1, 'GPU': 0},
        allow_soft_placement=True,
        log_device_placement=False
    )
save_path_rl = './s2ss_chatbot_ad.ckpt'
graph_rl = tf.Graph()

with graph_rl.as_default():
        model_rl = SequenceToSequence(
            input_vocab_size=len(ws),
            target_vocab_size=len(ws),
            batch_size=1,
            mode='decode',
            beam_width=12,
            bidirectional=False,
            cell_type='lstm',
            depth=1,
            attention_type='Bahdanau',
            use_residual=False,
            use_dropout=False,
            parallel_iterations=1,
            time_major=False,
            hidden_units=1024,
            share_embedding=True
        )
        init = tf.global_variables_initializer()
        sess_rl = tf.Session(config=config)
        sess_rl.run(init)
        model_rl.load(sess_rl, save_path_rl)


@bottle.route('/login/<w>', method='GET')
def do_login(w):
    user_text = w
    x_test = list(user_text.lower())
    x_test = [x_test]
    bar = batch_flow([x_test], [ws], 1)
    x, xl = next(bar)
    pred_rl = model_rl.predict(
            sess_rl,
            np.array(x),
            np.array(xl)
        ) 
    #word = bottle.request.forms.get("word")
    str2 = ''.join(str(i) for i in ws.inverse_transform(pred_rl[0]))
    return str2


bottle.run(host='0.0.0.0', port=8080)                                          #表示本机，接口是8080

注意不要聊的太猛，容易被腾讯封了。

[2018-03-12 02:34:54][INFO] please scan the qrCode with wechat.
[2018-03-12 02:35:01][INFO] please confirm login in wechat.
Array
(
    [ret] => 1203
    [message] => 当前登录环境异常。为了你的帐号安全，暂时不能登录web微信。你可以通过Windows微信、Mac微信或者手机客户端微信登录。
)
[2018-03-12 02:35:03] vbot.ERROR: Undefined index: skey [] []
PHP Fatal error:  Uncaught ErrorException: Undefined index: skey in /Users/zhiweipang/my-vbot/vendor/hanson/vbot/src/Core/Server.php:194