JSONオブジェクトをファイルから読み込むのではなく、リクエストボディから取得するフラスコアプリケーションを作成しました。
このコードは、エンティティとsklearn-crfsuiteのためにspaCyを使用して既存のLUIS jsonをインテント認識のために変換します。
from flask import Flask, jsonify, request
from flask_cors import CORS
import json, os, msvcrt, psutil, subprocess, datetime
app = Flask(__name__)
CORS(app)
with app.app_context():
with app.test_request_context():
#region REST based RASA API
serverExecutablePID = 0
hasAPIStarted = False
configFileDirectory = "C:\\Code\\RasaAPI\\RASAResources\\config"
chitChatModel = "ChitChat"
assetsDirectory = "C:\\Code\\RasaAPI\\RASAResources"
def createSchema(SchemaPath, dataToBeWritten):
try:
#write LUIS or RASA JSON Schema in json file locking the file to avoid race condition using Python's Windows msvcrt binaries
with open(SchemaPath, "w") as SchemaCreationHandle:
msvcrt.locking(SchemaCreationHandle.fileno(), msvcrt.LK_LOCK, os.path.getsize(SchemaPath))
json.dump(dataToBeWritten, SchemaCreationHandle, indent = 4, sort_keys=False)
SchemaCreationHandle.close()
#Check if written file actually exists on disk or not
doesFileExist = os.path.exists(SchemaPath)
return doesFileExist
except Exception as ex:
return str(ex.args)
def appendTimeStampToModel(ModelName):
return ModelName + '_{:%Y%m%d-%H%M%S}.json'.format(datetime.datetime.now())
def appendTimeStampToConfigSpacy(ModelName):
return ModelName + '_config_spacy_{:%Y%m%d-%H%M%S}.json'.format(datetime.datetime.now())
def createConfigSpacy(ModelName, DataPath, ConfigSpacyPath, TrainedModelsPath, LogDataPath):
try:
with open(ConfigSpacyPath, "w") as configSpacyFileHandle:
msvcrt.locking(configSpacyFileHandle.fileno(), msvcrt.LK_LOCK, os.path.getsize(ConfigSpacyPath))
configDataToBeWritten = dict({
"project": ModelName,
"data": DataPath,
"path": TrainedModelsPath,
"response_log": LogDataPath,
"log_level": "INFO",
"max_training_processes": 1,
"pipeline": "spacy_sklearn",
"language": "en",
"emulate": "luis",
"cors_origins": ["*"],
"aws_endpoint_url": None,
"token": None,
"num_threads": 2,
"port": 5000
})
json.dump(configDataToBeWritten, configSpacyFileHandle, indent = 4, sort_keys=False)
return os.path.getsize(ConfigSpacyPath) > 0
except Exception as ex:
return str(ex.args)
def TrainRASA(configFilePath):
try:
trainingString = 'start /wait python -m rasa_nlu.train -c ' + '\"' + os.path.normpath(configFilePath) + '\"'
returnCode = subprocess.call(trainingString, shell = True)
return returnCode
except Exception as ex:
return str(ex.args)
def StartRASAServer(configFileDirectory, ModelName):
#region Server starting logic
try:
global hasAPIStarted
global serverExecutablePID
#1) for finding which is the most recent config_spacy
root, dirs, files = next(os.walk(os.path.normpath(configFileDirectory)))
configFiles = [configFile for configFile in files if ModelName in configFile]
configFiles.sort(key = str.lower, reverse = True)
mostRecentConfigSpacy = os.path.join(configFileDirectory, configFiles[0])
serverStartingString = 'start /wait python -m rasa_nlu.server -c ' + '\"' + os.path.normpath(mostRecentConfigSpacy) + '\"'
serverProcess = subprocess.Popen(serverStartingString, shell = True)
serverExecutablePID = serverProcess.pid
pingReturnCode = 1
while(pingReturnCode):
pingReturnCode = os.system("netstat -na | findstr /i 5000")
if(pingReturnCode == 0):
hasAPIStarted = True
return pingReturnCode
except Exception as ex:
return jsonify({"message": "Failed because: " + str(ex.args) , "success": False})
#endregion
def KillProcessWindow(hasAPIStarted, serverExecutablePID):
if(hasAPIStarted == True and serverExecutablePID != 0):
me = psutil.Process(serverExecutablePID)
for child in me.children():
child.kill()
@app.route('/api/TrainRASA', methods = ['POST'])
def TrainRASAServer():
try:
#get request body of POST request
postedJSONData = json.loads(request.data, strict = False)
if postedJSONData["data"] is not None:
print("Valid data")
#region JSON file building logic
modelName = postedJSONData["modelName"]
modelNameWithExtension = appendTimeStampToModel(modelName)
schemaPath = os.path.join(assetsDirectory, "data", modelNameWithExtension)
print(createSchema(schemaPath, postedJSONData["data"]))
#endregion
#region config file creation logic
configFilePath = os.path.join(assetsDirectory, "config", appendTimeStampToConfigSpacy(modelName))
logsDirectory = os.path.join(assetsDirectory, "logs")
trainedModelDirectory = os.path.join(assetsDirectory, "models")
configFileCreated = createConfigSpacy(modelName, schemaPath, configFilePath, trainedModelDirectory, logsDirectory)
#endregion
if(configFileCreated == True):
#region Training RASA NLU with schema
TrainingReturnCode = TrainRASA(configFilePath)
#endregion
if(TrainingReturnCode == 0):
return jsonify({"message": "Successfully trained RASA NLU with modelname: " + modelName, "success": True})
# KillProcessWindow(hasAPIStarted, serverExecutablePID)
# serverStartingReturnCode = StartRASAServer(configFileDirectory, modelName)
# #endregion
# if serverStartingReturnCode == 0:
# return jsonify({"message": "Successfully started RASA server on port 5000", "success": True})
# elif serverStartingReturnCode is None:
# return jsonify({"message": "Could not start RASA server, request timed out", "success": False})
else:
return jsonify({"message": "Soemthing wrong happened while training RASA NLU!", "success": False})
else:
return jsonify({"message": "Could not create config file for RASA NLU", "success": False})
#throw exception if request body is empty
return jsonify({"message": "Please enter some JSON, JSON seems to be empty", "success": False})
except Exception as ex:
return jsonify({"Reason": "Failed because" + str(ex.args), "success": False})
@app.route('/api/StopRASAServer', methods = ['GET'])
def StopRASAServer():
try:
global serverExecutablePID
if(serverExecutablePID != 0 or serverExecutablePID != None):
me = psutil.Process(serverExecutablePID)
for child in me.children():
child.kill()
return jsonify({"message": "Server stopped....", "success": True})
except Exception as ex:
return jsonify({"message": "Something went wrong while shutting down the server because: " + str(ex.args), "success": True})
if __name__ == "__main__":
StartRASAServer(configFileDirectory, chitChatModel)
app.run(debug=False, threaded = True, host='0.0.0.0', port = 5050)
私はこれがこの質問の続きだと思います。 https://stackoverflow.com/questions/47313046/how-to-create-training-data-for-rasa-nlu-through-program-nodejs/47313236 –