def runReload():
try:
kbid = json.loads(request.data)['kbid']
print(kbid)
SessionManager.deleteModelSessionByKbid(kbid)
SentenceSemanticService.define_placeholders()
SentenceSemanticService.loadTrainedDataStoreSession(kbid)
return jsonify({"status": "success"})
except Exception as e:
return jsonify({"status": "failure"})
def runReload(): # reload api for reloading model
try:
logger.info("In runReload Method reloading Model with reload api")
kbid = json.loads(request.data)['kbid']
logger.info("Request for Kd id :"+str(kbid))
if SessionManager.getModelSessionByKbid(kbid):
SessionManager.deleteModelSessionByKbid(kbid)
SentenceSemanticService.define_placeholders()
SentenceSemanticService.loadTrainedDataStoreSession(kbid)
return jsonify({"status": "success", "message": "Model Reloaded Sucessfully"}), 200
except Exception as e:
logger.error("Issue While Reloading Model ! Please Try in Sometime"+str(e))
return jsonify({"status": "failure", "message": "Issue While Reloading Model ! Please Try in Sometime"}), 500
def runPrediction(): # prediction api
try:
logger.info("In Predict Method start")
userQuery = json.loads(request.data)['question'].lower()
kbid = json.loads(request.data)['kb_id']
topN = json.loads(request.data)['top_n']
logger.info("Question : "+str(userQuery))
if not SessionManager.getModelSessionByKbid(kbid):
SentenceSemanticService.define_placeholders()
try:
SentenceSemanticService.loadTrainedDataStoreSession(kbid)
# userQuery = 'How many online surveys can I participate'
matched_statement = SentenceSemanticService.process(userQuery=userQuery, kbid=kbid, topN=topN)
logger.info("Matching Statement is :" + str(matched_statement))
result = []
for match_question, matched_score, question_id, answer in matched_statement:
result.append(
{"matched_question_id": question_id, "matched_question": match_question, "score": str(matched_score),"answer": answer})
return jsonify(result),200
except Exception as e:
return jsonify(
{"status": "failure", "message": "Issue While Reloading Model ! Please Try in Sometime"}), 500
logger.info("runPrediction: Error while reloading the model : " + str(e))
except Exception as e:
logger.info("Error : "+str(e))
return jsonify({"message": "Service Not Found ! Please Try After Sometime", "status": "failed"}), 404
def runPrediction():
try:
userQuery = json.loads(request.data)['question']
kbid = json.loads(request.data)['kb_id']
topN = json.loads(request.data)['top_n']
print(userQuery)
print(kbid)
if not SessionManager.getModelSessionByKbid(kbid):
SentenceSemanticService.define_placeholders()
SentenceSemanticService.loadTrainedDataStoreSession(kbid)
# userQuery = 'How many online surveys can I participate'
matched_statement = SentenceSemanticService.process(userQuery=userQuery, kbid=kbid, topN=topN)
print(matched_statement)
result = []
for match_question, matched_score, question_id, answer in matched_statement:
result.append(
{"matched_question_id": question_id, "matched_question": match_question, "score": str(matched_score),
"answer": answer})
return jsonify(result)
except Exception as e:
print(e)
def loadTrainedDataStoreSession(cls, kbid):
is_model_loaded = SessionManager.getModelSessionByKbid(kbid)
if not is_model_loaded:
try:
unique_data_list = DBUtil.getData('kb_qna', {'kb_id': kbid})
unique_statement_list = cls.create_only_statements_list(
unique_data_list)
customer_embed = cls.embed_customer_data(unique_statement_list)
embeding_dict = {
"unique_data_with_model": unique_data_list,
"unique_statement_list": unique_statement_list,
"customer_embed": customer_embed
}
SessionManager.setModelSessionByKbid(
kbid=kbid, model_session=embeding_dict)
print('Confidence Checker Model Restored for Customer : ' +
str(kbid))
except Exception as e:
print('Confidence Checker Model Not Restored for Customer : ' +
str(e))
def getModelSession(cls, kbid): #get model sessio n from SessionManager
customerModelSession = SessionManager.getModelSessionByKbid(kbid)
return customerModelSession
def getModelSession(cls, kbid):
customerModelSession = SessionManager.getModelSessionByKbid(kbid)
return customerModelSession