如何将python应用程序转换为Rshiny应用程序?它是一个脑筋急转弯！无法找到R中需要更改的UI [英] How do I make/convert my python app into an Rshiny app? Its a brainteaser! Unable to find what change UI needs in R

问题描述

我是R的新手，并试图了解Rshiny来构建UI.我正在尝试为我的python应用程序创建一个UI，以转录多个wav文件.下面有两部分，第一部分是我的python应用程序，第二部分是R中的闪亮应用程序，它使用网状结构调用了transcribe.py应用程序.但是由于某种原因，我没有收到任何输出.

i am new to R and trying to understand Rshiny to build UIs. I am trying to create a UI for my python app that transcribes mulitple wav files. There are two parts below, first my python app and the second my shiny app in R which uses reticulate to call my transcribe.py app. For some reason though, i do not receive any output.

我的Python应用程序可以完美运行并且不需要代码审查.但是，Rshiny应用程序无法正确执行python应用程序以产生所需的结果.目的是让用户从UI抄录文件并决定是否要下载csv.

My Python app works perfectly and does NOT need code review.However, the Rshiny app does not execute the python app correctly to produce the desired result. The objective is to let the user transcribe the files from the UI and decide if they want to download the csv.

我有一个用于转录文件的python应用程序transcribe.py-

I have a python app for transcribing files called transcribe.py-

import os
import json
import time
# import threading
from pathlib import Path

import concurrent.futures

# from os.path import join, dirname
from ibm_watson import SpeechToTextV1
from ibm_watson.websocket import RecognizeCallback, AudioSource
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

import pandas as pd

# Replace with your api key.
my_api_key = "abc123"

# You can add a directory path to Path() if you want to run
# the project from a different folder at some point.
directory = Path().absolute()


authenticator = IAMAuthenticator(my_api_key)

service = SpeechToTextV1(authenticator=authenticator)
service.set_service_url('https://api.us-east.speech-to-text.watson.cloud.ibm.com')
# I used this URL.
# service.set_service_url('https://stream.watsonplatform.net/speech-to-text/api') 


models = service.list_models().get_result()
#print(json.dumps(models, indent=2))

model = service.get_model('en-US_BroadbandModel').get_result()
#print(json.dumps(model, indent=2))



# get data to a csv
########################RUN THIS PART SECOND#####################################


def process_data(json_data, output_path):

    print(f"Processing: {output_path.stem}")

    cols = ["transcript", "confidence"]

    dfdata = [[t[cols[0]], t[cols[1]]] for r in json_data.get('results') for t in r.get("alternatives")]

    df0 = pd.DataFrame(data = dfdata, columns = cols)

    df1 = pd.DataFrame(json_data.get("speaker_labels")).drop(["final", "confidence"], axis=1)


    # test3 = pd.concat([df0, df1], axis=1)
    test3 = pd.merge(df0, df1, left_index = True, right_index = True)


    # sentiment
    print(f"Getting sentiment for: {output_path.stem}")
    transcript = test3["transcript"]
    transcript.dropna(inplace=True)

    analyzer = SentimentIntensityAnalyzer()
    text = transcript
    scores = [analyzer.polarity_scores(txt) for txt in text]

    # data = pd.DataFrame(text, columns = ["Text"])
    data = transcript.to_frame(name="Text")
    data2 = pd.DataFrame(scores)


    # final_dataset= pd.concat([data, data2], axis=1)
    final_dataset = pd.merge(data, data2, left_index = True, right_index = True)

    # test4 = pd.concat([test3, final_dataset], axis=1)
    test4 = pd.merge(test3, final_dataset, left_index = True, right_index = True)

    test4.drop("Text", axis=1, inplace=True)

    test4.rename(columns = {
            "neg": "Negative",
            "pos": "Positive",
            "neu": "Neutral",
            }, inplace=True)

    # This is the name of the output csv file
    test4.to_csv(output_path, index = False)


def process_audio_file(filename, output_type = "csv"):

    audio_file_path = directory.joinpath(filename)

    # Update output path to consider `output_type` parameter.
    out_path = directory.joinpath(f"{audio_file_path.stem}.{output_type}")

    print(f"Current file: '{filename}'")

    with open(audio_file_path, "rb") as audio_file:
        data = service.recognize(
                audio = audio_file,
                speaker_labels = True,
                content_type = "audio/wav",
                inactivity_timeout = -1,
                model = "en-US_NarrowbandModel",
                continuous = True,
            ).get_result()

    print(f"Speech-to-text complete for: '{filename}'")

    # Return data and output path as collection.
    return [data, out_path]


def main():
    print("Running main()...")

    # Default num. workers == min(32, os.cpu_count() + 4)
    n_workers = os.cpu_count() + 2

    # Create generator for all .wav files in folder (and subfolders).
    file_gen = directory.glob("**/*.wav")

    with concurrent.futures.ThreadPoolExecutor(max_workers = n_workers) as executor:
        futures = {executor.submit(process_audio_file, f) for f in file_gen}
        for future in concurrent.futures.as_completed(futures):
            pkg = future.result()
            process_data(*pkg)


if __name__ == "__main__":

    print(f"Program to process audio files has started.")

    t_start = time.perf_counter()

    main()

    t_stop = time.perf_counter()
    print(f"Done! Processing completed in {t_stop - t_start} seconds.")

在Rstudio中，我尝试-

In Rstudio, I tried -

R.UI文件

library(shiny)
library(reticulate) # for reading Python code
library(dplyr)
library(stringr) 
library(formattable) # for adding color to tables
library(shinybusy) # for busy bar
library(DT) # for dataTableOutput

use_python("/usr/lib/python3")

ui <- fluidPage(
  add_busy_bar(color = "#5d98ff"),
  fileInput("wavFile", "SELECT .WAV FILE", accept = ".wav"),
  uiOutput("downloadData"),
  dataTableOutput("transcript"),
  
)

R.Server文件

server <- function(input, output) {
  
  # .WAV File Selector ------------------------------------------------------
  
  file <- reactive({
    file <- input$wavFile # Get file from user input
    gsub("\\\\","/",file$datapath) # Access the file path. Convert back slashes to forward slashes.
  })
  
  
  # Transcribe and Clean ----------------------------------------------------
  
  transcript <- reactive({
    
    req(input$wavFile) # Require a file before proceeding
    
    source_python('transcribe.py') # Load the Python function           # COMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
    transcript <- data.frame(transcribe(file())) # Transcribe the file  # COMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
    # load('transcript.rdata') # Loads a dummy transcript               # UNCOMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
    
    transcript$transcript <- unlist(transcript$transcript) # Transcript field comes in as a list. Unlist it.
    transcript <- transcript[which(!(is.na(transcript$confidence))),] # Remove empty lines
    names(transcript) <- str_to_title(names(transcript)) # Capitalize column headers
    
    transcript # Return the transcript
    
  })
  
  
  # Use a server-side download button ---------------------------------------
  
  # ...so that the download button only appears after transcription
  
  output$downloadData <- renderUI({
    req(transcript())
    downloadButton("handleDownload","Download CSV")
  })
  
  output$handleDownload <- downloadHandler(
    filename = function() {
      paste('Transcript ',Sys.Date(), ".csv", sep = "")
    },
    content = function(file) {
      write.csv(transcript(), file, row.names = FALSE)
    }
  )
  
  
  # Transcript table --------------------------------------------------------
  
  output$transcript <- renderDataTable({ 
    as.datatable(formattable(
      transcript() %>%
        select(Transcript,
               Confidence,
               Negative,
               Positive
        ),
      list(Confidence = color_tile('#ffffff','#a2b3c8'),
           Negative = color_tile('#ffffff', '#e74446'),
           Positive = color_tile('#ffffff', "#499650")
      )
    ), rownames = FALSE, options =list(paging = FALSE)
    )
  })
  
  
  # END ---------------------------------------------------------------------
  
}

推荐答案

在闪亮的环境中，您需要在python脚本中正确传递参数.一种简单的方法是在python脚本中定义一个函数，然后以闪亮的方式调用该函数.

In shiny, you need to pass argument properly in python script. An easy way around is to define a function in a python script and call that function in shiny.

这是修改后的python脚本(已编辑的process_data函数和已添加的run_script函数)-

Here is your modified python script (edited process_data function and added run_script function) -

import os
import json
import time
# import threading
from pathlib import Path

import concurrent.futures

# from os.path import join, dirname
from ibm_watson import SpeechToTextV1
from ibm_watson.websocket import RecognizeCallback, AudioSource
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

import pandas as pd

# Replace with your api key.
my_api_key = "api_key"

# You can add a directory path to Path() if you want to run
# the project from a different folder at some point.
directory = Path().absolute()


authenticator = IAMAuthenticator(my_api_key)

service = SpeechToTextV1(authenticator=authenticator)
service.set_service_url('https://api.us-east.speech-to-text.watson.cloud.ibm.com')
# I used this URL.
# service.set_service_url('https://stream.watsonplatform.net/speech-to-text/api') 


models = service.list_models().get_result()
#print(json.dumps(models, indent=2))

model = service.get_model('en-US_BroadbandModel').get_result()
#print(json.dumps(model, indent=2))



# get data to a csv
########################RUN THIS PART SECOND#####################################


def process_data(json_data):

    #print(f"Processing: {output_path.stem}")

    cols = ["transcript", "confidence"]

    dfdata = [[t[cols[0]], t[cols[1]]] for r in json_data.get('results') for t in r.get("alternatives")]

    df0 = pd.DataFrame(data = dfdata, columns = cols)

    df1 = pd.DataFrame(json_data.get("speaker_labels")).drop(["final", "confidence"], axis=1)


    # test3 = pd.concat([df0, df1], axis=1)
    test3 = pd.merge(df0, df1, left_index = True, right_index = True)


    # sentiment
    #print(f"Getting sentiment for: {output_path.stem}")
    transcript = test3["transcript"]
    transcript.dropna(inplace=True)

    analyzer = SentimentIntensityAnalyzer()
    text = transcript
    scores = [analyzer.polarity_scores(txt) for txt in text]

    # data = pd.DataFrame(text, columns = ["Text"])
    data = transcript.to_frame(name="Text")
    data2 = pd.DataFrame(scores)


    # final_dataset= pd.concat([data, data2], axis=1)
    final_dataset = pd.merge(data, data2, left_index = True, right_index = True)

    # test4 = pd.concat([test3, final_dataset], axis=1)
    test4 = pd.merge(test3, final_dataset, left_index = True, right_index = True)

    test4.drop("Text", axis=1, inplace=True)

    test4.rename(columns = {
            "neg": "Negative",
            "pos": "Positive",
            "neu": "Neutral",
            }, inplace=True)

    # This is the name of the output csv file
    # test4.to_csv(output_path, index = False)
    return(test4)


def process_audio_file(filename, output_type = "csv"):

    audio_file_path = directory.joinpath(filename)

    # Update output path to consider `output_type` parameter.
    out_path = directory.joinpath(f"{audio_file_path.stem}.{output_type}")

    print(f"Current file: '{filename}'")

    with open(audio_file_path, "rb") as audio_file:
        data = service.recognize(
                audio = audio_file,
                speaker_labels = True,
                content_type = "audio/wav",
                inactivity_timeout = -1,
                model = "en-US_NarrowbandModel",
                continuous = True,
            ).get_result()

    print(f"Speech-to-text complete for: '{filename}'")

    # Return data and output path as collection.
    return [data, out_path]


def main():
    print("Running main()...")

    # Default num. workers == min(32, os.cpu_count() + 4)
    n_workers = os.cpu_count() + 2

    # Create generator for all .wav files in folder (and subfolders).
    file_gen = directory.glob("**/*.wav")

    with concurrent.futures.ThreadPoolExecutor(max_workers = n_workers) as executor:
        futures = {executor.submit(process_audio_file, f) for f in file_gen}
        for future in concurrent.futures.as_completed(futures):
            pkg = future.result()
            process_data(*pkg)


def run_script (filename):
    return(process_data(process_audio_file(filename)[0]))

发光的代码

在服务器文件中调用run_script函数而不是转录.确保transcribe.py文件位于工作目录中.纠正了output $ transcript中的一些错字

Shiny code

In server file call run_script function rather than transcribe. Make sure that transcribe.py file is in working directory. Corrected some typo in output$transcript

library(shiny)
library(reticulate) # for reading Python code
library(dplyr)
library(stringr) 
library(formattable) # for adding color to tables
library(shinybusy) # for busy bar
library(DT) # for dataTableOutput

use_python("C:/Users/ap396/Anaconda3/python")

ui <- fluidPage(
  add_busy_bar(color = "#5d98ff"),
  fileInput("wavFile", "SELECT .WAV FILE", accept = ".wav",multiple = T),
  uiOutput("downloadData"),
  dataTableOutput("transcript")
  
)


server <- function(input, output) {
  
  # .WAV File Selector ------------------------------------------------------
  
  file <- reactive({
    
    req(input$wavFile) # Require a file before proceeding
    
    files <- input$wavFile # Get file from user input
    file = NULL
    for (i in 1:nrow(files)){
      print(file)
      file = c(file,gsub("\\\\","/",files$datapath[i])) # Access the file path. Convert back slashes to forward slashes.  
    }
    return(file)
  })
  

  # Transcribe and Clean ----------------------------------------------------
  source_python('transcribe.py')
  
  transcript <- reactive({

    dft= data.frame(NULL)
    
    for(j in 1:length(file())){
    t0 = Sys.time()
    transcript <- run_script(file()[j])   #  Transcribe the file  # COMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
    t1 = Sys.time() - t0
    
    transcript$File = j; transcript$Time = t1
    
    dft = rbind(dft,transcript)
    }

    return(dft) # Return the transcript
    
  })
  
  
  # Use a server-side download button ---------------------------------------
  # ...so that the download button only appears after transcription
  
  output$downloadData <- renderUI({
    req(transcript())
    downloadButton("handleDownload","Download CSV")
  })
  
  output$handleDownload <- downloadHandler(
    filename = function() {
      paste('Transcript ',Sys.Date(), ".csv", sep = "")
    },
    content = function(file) {
      write.csv(transcript(), file, row.names = FALSE)
    }
  )
  
  
  # Transcript table --------------------------------------------------------
  
  output$transcript <- renderDataTable({ 
    as.datatable(formattable(
      transcript() %>%
        select(File,
               Time,
               transcript,
               confidence,
               Negative,
               Positive
        ),
      list(Confidence = color_tile('#ffffff','#a2b3c8'),
           Negative = color_tile('#ffffff', '#e74446'),
           Positive = color_tile('#ffffff', "#499650")
      )
    ), rownames = FALSE, options =list(paging = FALSE)
    )
  })
    # END ---------------------------------------------------------------------
}

# Return a Shiny app object
shinyApp(ui = ui, server = server)

请注意，闪亮的下载仅在网络浏览器中有效，因此您必须在网络浏览器中打开应用程序

Note that shiny download works only in web-browsers so you must open app in a web-browser

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