如何将我的 python 应用程序制作/转换为 Rshiny 应用程序？这是一个脑筋急转弯！无法在 R 闪亮代码

如何解决如何将我的 python 应用程序制作/转换为 Rshiny 应用程序？这是一个脑筋急转弯！无法在 R 闪亮代码

我是 R 的新手，并试图了解 Rshiny 以构建 UI。我正在尝试为我的 python 应用程序创建一个 UI，用于转录多个 wav 文件。下面有两个部分，第一个是我的 python 应用程序，第二个是我在 R 中使用 reticulate 来调用我的 transcribe.py 应用程序的闪亮应用程序。但由于某种原因，我没有收到任何输出。

我的 Python 应用程序运行良好，不需要代码审查。但是，Rshiny 应用程序没有正确执行 Python 应用程序以产生所需的结果。目的是让用户从 UI 转录文件并决定他们是否要下载 csv。

我有一个用于转录文件的 Python 应用程序，名为 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,right_index = True)

    # test4 = pd.concat([test3,final_dataset],axis=1)
    test4 = pd.merge(test3,final_dataset,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，我尝试过 -

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 ---------------------------------------------------------------------
  
}

解决方法

在闪亮中，您需要在 python 脚本中正确传递参数。一个简单的方法是在 python 脚本中定义一个函数，并以闪亮的方式调用该函数。

这是您修改后的python脚本（编辑了process_data函数并添加了run_script函数）-

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,right_index = True)

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

闪亮代码

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

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，闪亮的下载仅适用于网络浏览器，因此您必须在网络浏览器中打开应用程序