不写R包的分析师不是好全栈

R构件后台服务器

    R

这是一个R为微信后台做服务的项目,这个项目并不需要其他的公众号权限,只要个人的公众号权限就可以。

项目地址: https://github.com/Lchiffon/Example-for-R-Weixin

文件:

Inception/
api.R
config.ini
utils.R

  • Inception/
    • 这里存储了一个深度学习的模型文件
  • api.R
    • 这里是后台的脚本
  • utils.R
    • 这里存储了模型的执行文件

接口部分

这里是通过fiery包构建了后端的服务器,后台需要做的是把自己的服务器IP登记到微信的公众平台上, 登记之后运行这个服务,就可以开启整个服务了

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library(fiery)

source('config.ini')

source("utils.R")

# Create a New App

app <- Fire$new()

app$host = IP

app$port = port


app$on('start', function(server, ...) {

server$set_data('visits', 0)

server$set_data('cycles', 0)

})




# Count the number of cycles (internal loops)

app$on('cycle-start', function(server, ...) {

server$set_data('cycles', server$get_data('cycles') + 1)

})




# Count the number of requests

app$on('before-request', function(server, ...) {

server$set_data('visits', server$get_data('visits') + 1)

})




# Handle requests

app$on('request', function(server, request, ...) {



request$parse_raw()

message(request$as_message())

print(rawToChar(request$body))




msgXML = rawToChar(request$body)

ori = extractWeixin(msgXML, "ToUserName")

user = extractWeixin(msgXML, "FromUserName")

time = as.POSIXct(as.numeric(extractWeixin(msgXML, "CreateTime",F)),

origin = "1970-01-01")

msgType = extractWeixin(msgXML, "MsgType")

content = extractWeixin(msgXML, "Content")

messageId = extractWeixin(msgXML, "MsgId",F)

PicUrl = extractWeixin(msgXML, "PicUrl")

print(PicUrl)

# output = sprintf('"%s","%s","%s","%s","%s"\n',user,time,msgType,content,messageId)



response <- request$respond()

response$status <- 200L

#response$body <- paste0('<h1>This is indeed a test. You are number ', server$get_data('visits'), '</h1>')



theQuery = request$query

# theQ <<- theQuery

if('echostr' %in% names(theQuery)){



response$body = regmatches(request$querystring,

gregexpr("(?<=echostr=).+(?=&t)",

request$querystring,

perl = TRUE))

}else{

print(123)

response$body = returnMsg(ori,user, time, msgType, content, messageId, PicUrl)

}

# cat(response$body)

# response$body <- 'success'

response$type <- 'html'

})




# Show number of requests in the console

app$on('after-request', function(server, ...) {

#message(server$get_data('visits'))

flush.console()

})


# Be polite

app$on('end', function(server) {

# sink()

#message('Goodbye')

flush.console()

})


app$ignite(showcase = TRUE)

函数部分

函数部分这里主要有三个函数,它会自动被api.R调用:

  • extractWeixin
    • 从微信发送的XML格式的消息中截取出需要的信息
  • showPic
    • 一个深度学习的封包函数,返回最合适的类别
  • returnMsg
    • 构建返回给微信的XML格式的函数
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# 微信数据截取函数

extractWeixin = function(msgXML, pattern,CDATA = T){

if(CDATA)

regPattern = paste0("(?<=", pattern, "><\\!\\[CDATA\\[)[\\s\\S]+(?=\\]\\]></",

pattern, ")")

else

regPattern = paste0("(?<=", pattern, ">).+(?=</",

pattern, ")")

regmatches(msgXML,

gregexpr(regPattern,

msgXML,

perl = TRUE))[[1]]

}

if(!require(mxnet)){

install.packages("drat", repos="https://cran.rstudio.com")

drat:::addRepo("dmlc")

install.packages("mxnet")

}




library(mxnet)

library(imager)

# 载入模型

model = mx.model.load("Inception/Inception_BN", iteration=39)

# 载入mean image

mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])




im <- load.image(system.file("extdata/parrots.png", package = "imager"))

plot(im)

preproc.image <- function(im, mean.image) {

# crop the image

shape <- dim(im)

short.edge <- min(shape[1:2])

xx <- floor((shape[1] - short.edge) / 2)

yy <- floor((shape[2] - short.edge) / 2)

cropped <- crop.borders(im, xx, yy)

# resize to 224 x 224, needed by input of the model.

resized <- resize(cropped, 224, 224)

# convert to array (x, y, channel)

arr <- as.array(resized) * 255

dim(arr) <- c(224, 224, 3)

# subtract the mean

normed <- arr - mean.img

# Reshape to format needed by mxnet (width, height, channel, num)

dim(normed) <- c(224, 224, 3, 1)

return(normed)

}

normed <- preproc.image(im, mean.img)




prob <- predict(model, X=normed)

max.idx <- max.col(t(prob))

synsets <- readLines("Inception/synset.txt")

print(paste0("Predicted Top-class: ", synsets[[max.idx]]))




showPic = function(input){

cat <- load.image(input)

# plot(cat)

normed <- preproc.image(cat, mean.img)

prob <- predict(model, X=normed)

max.idx <- max.col(t(prob))

print(paste0("Predicted Top-class: ", synsets[[max.idx]]))

output = strsplit(synsets[[max.idx]]," ")[[1]]

output[1] = ''

return(paste(output,collapse=" "))

}

returnMsg = function(ori,user, time, msgType, content, messageId,PicUrl){

if(length(PicUrl)==0){

cat(234)

return('success')

}

cat(123)
filename = paste0("data/",format(Sys.time(),"%Y%m%d%M"))

download.file(PicUrl,destfile = filename)
output = sprintf("<xml>
<ToUserName><![CDATA[%s]]></ToUserName>
<FromUserName><![CDATA[%s]]></FromUserName>
<CreateTime>%s</CreateTime>
<MsgType><![CDATA[text]]></MsgType>
<Content><![CDATA[介尼玛似一个%s]]></Content>
</xml>",user,ori,as.numeric(Sys.time()),showPic(filename))
return(output)
}
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