def message_actions():
# Parse the request payload
payload = json.loads(request.form["payload"])
selection_item = payload["actions"][0]["name"]
selection_value = payload["actions"][0]["value"]
reply_action = MessageManager(payload)
# get tag using trained model
if selection_item == "isQuestion":
if selection_value == "yes":
question_content = payload["original_message"]["attachments"][0][
"fallback"]
# print(question_content)
tag = prediction_model.get_Question_tags(question_content)
print(type(tag))
linklist, titlelist = stackOverFlowApi.get_Frequent_Questions_of_a_tag(
tag)
taglist = stackOverFlowApi.get_related_tag(tag)
message = reply_action.selectIsQuestion(tag, linklist, titlelist,
taglist)
response = slack_client.api_call(
"chat.postMessage",
**message,
attachments=reply_action.getSearchingBlock(question_content))
return ""
elif selection_value == 'no':
#reply_is_question = MessageActions(payload)
message = reply_action.selectIsnotQuestion()
response = slack_client.api_call("chat.update",
**message,
attachments=[])
return ""
else:
return ""
if selection_item == "search":
if selection_value == "yes":
# reply_is_selection = MessageActions(payload)
question_content = payload["original_message"]["attachments"][0][
"fallback"]
# get search link
link = stackOverFlowApi.search_question_on_stackoverflow(
question_content)
message = reply_action.selectIsSearch(link)
response = slack_client.api_call("chat.postMessage", **message)
else:
response = slack_client.api_call(
"chat.postMessage",
channel=payload["channel"]["id"],
ts=payload["message_ts"],
text="It's great to help you! :smile:")
return ""
return ""
