def bottrain(name):
message = "Success"
code = 200
try:
projectmgr.ValidateServiceExists(name, constants.ServiceTypes.ChatBot)
id = projectmgr.StartJob(name, constants.ServiceTypes.ChatBot, 0)
rjson = request.json
data = rjson["data"]
corpus = []
if "corpus" in rjson:
corpus = rjson["corpus"]
if corpus == 'all':
chatbot.corpustrain(name, '')
elif corpus != '':
chatbot.corpustrain(name, corpus)
chatbot.train(name, data)
projectmgr.EndJob(id, "Completed", "Completed",
json.dumps({
"corpus": corpus,
"data": data
}))
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def execute(name):
message = "Success"
code = 200
taskid = ""
try:
service = projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
data = json.loads(request.data)
epoches = 32
batch_size = 32
taskid = ""
servicejson = json.loads(service.servicedata)
if "epoches" in data:
epoches = data['epoches']
if "batch_size" in data:
batch_size = data['batch_size']
trainingstatus = app.trainingstatus
if trainingstatus == 1:
message = "Training in progress! Please try after the current training is completed."
code = 500
else:
if servicejson["model_type"] == "mlp":
taskid = backgroundproc.StartTrainThread(
name, epoches, batch_size)
elif servicejson["model_type"] == "general":
taskid = backgroundproc.StartValidateThread(name)
message = "Job started! Please check status for id: " + taskid
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message, "jobid": taskid})
def modelinfo(name, modelname):
message = "Success"
code = 200
result = None
model_json = None
try:
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
modelRec = projectmgr.GetDeepModel(
name, constants.ServiceTypes.MachineLearning, modelname)
if modelRec is None:
raise Exception("No Model Found!")
result = json.loads(modelRec.modeldata)
model_obj = kerasfactory.createModel(result)
model_json = json.loads(model_obj.to_json())
except Exception as e:
code = 500
message = str(e)
return jsonify({
"statuscode": code,
"message": message,
"result": result,
"model_json": model_json
})
def modelflow(name, modelname):
message = "Success"
code = 200
try:
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
projectmgr.UpdateModelFlow(name, "ml", modelname, request.json)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def savepipelineinfo(name):
message = "Success"
code = 200
try:
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
projectmgr.UpsertPipeline(name, "ml", request.json)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def botupdate(name):
message = "Success"
code = 200
try:
projectmgr.ValidateServiceExists(name, constants.ServiceTypes.ChatBot)
projectmgr.UpsertService(name, constants.ServiceTypes.ChatBot,
request.json)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def update(name):
message = "Success"
code = 200
try:
directory = "./data/" + name
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
projectmgr.UpsertService(name, "ml", request.json)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def delete(name):
message = "Success"
code = 200
try:
directory = "./data/" + name
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
projectmgr.DeleteService(name, "ml")
if os.path.exists(directory):
shutil.rmtree(directory)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def delfile(name):
message = "Success"
code = 200
try:
dataset_folder = "./data/" + name + "/dataset/"
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
if not os.path.exists(dataset_folder):
os.makedirs(dataset_folder)
filename = request.json["filename"]
os.remove(dataset_folder + filename)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def botdelete(name):
message = "Success"
code = 200
try:
projectmgr.ValidateServiceExists(name, constants.ServiceTypes.ChatBot)
botfolder = "./data/__chatbot/" + name
if os.path.exists(botfolder):
shutil.rmtree(botfolder)
projectmgr.DeleteService(name, constants.ServiceTypes.ChatBot)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
def pipelineflowinfo(name):
message = "Success"
code = 200
result = None
try:
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
pipelineRec = projectmgr.GetPipeline(name, "ml")
if pipelineRec is None:
raise Exception("No Pipeline Found!")
result = json.loads(pipelineRec.pipelineflow)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message, "result": result})
def modelflowinfo(name, modelname):
message = "Success"
code = 200
result = None
try:
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
modelRec = projectmgr.GetDeepModel(
name, constants.ServiceTypes.MachineLearning, modelname)
if modelRec is None:
raise Exception("No Model Found!")
result = json.loads(modelRec.modelflow)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message, "result": result})
def botpredict(name):
message = "Success"
code = 200
start = datetime.utcnow()
result = []
try:
projectmgr.ValidateServiceExists(name, constants.ServiceTypes.ChatBot)
rjson = request.json
data = rjson["data"]
result = chatbot.predict(name, data)
logmgr.LogPredSuccess(name, constants.ServiceTypes.ChatBot, start)
except Exception as e:
code = 500
message = str(e)
logmgr.LogPredError(name, constants.ServiceTypes.ChatBot, start,
message)
return jsonify({"statuscode": code, "message": message, "result": result})
def getfiles(name):
message = "Success"
code = 200
result = []
try:
dataset_folder = "./data/" + name + "/dataset/"
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
if not os.path.exists(dataset_folder):
os.makedirs(dataset_folder)
files = os.listdir(dataset_folder)
for f in files:
result.append(f)
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message, "result": result})
def upload(name):
message = "Success"
code = 200
try:
datasetFolder = "./data/" + name + "/dataset/"
projectmgr.ValidateServiceExists(
name, constants.ServiceTypes.MachineLearning)
if not os.path.exists(datasetFolder):
os.makedirs(datasetFolder)
if len(request.files) == 0:
code = 1002
message = "No file found"
return jsonify({"statuscode": code, "message": message})
postedfile = request.files.items(0)[0][1]
postedfile.save(
os.path.join(datasetFolder,
werkzeug.secure_filename(postedfile.filename)))
except Exception as e:
code = 500
message = str(e)
return jsonify({"statuscode": code, "message": message})
