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 ""