From db675f9d4a945dcff9a50efa8d81b029274b8e18 Mon Sep 17 00:00:00 2001 From: Ayoub BERDEDDOUCH Date: Mon, 29 Jan 2024 13:51:18 +0100 Subject: [PATCH] Add files via upload --- Dockerfile | 28 +- actions/actions.py | 810 ++++++++++++++++++++++++- actions/actions1.py | 29 + actions/requirements-actions.txt | 5 + docker-compose.yml | 8 + mydata/BASE8RGPH24V4_all_18_12_ID.xlsx | Bin 0 -> 229650 bytes mydata/BASE8RGPH24V5_all_08_01_ID.xlsx | Bin 0 -> 276101 bytes rasax/basic-values.yml | 4 +- rasax/tls-values-with-actions.yml | 18 +- 9 files changed, 857 insertions(+), 45 deletions(-) create mode 100644 actions/actions1.py create mode 100644 actions/requirements-actions.txt create mode 100644 docker-compose.yml create mode 100644 mydata/BASE8RGPH24V4_all_18_12_ID.xlsx create mode 100644 mydata/BASE8RGPH24V5_all_08_01_ID.xlsx diff --git a/Dockerfile b/Dockerfile index 4020199..b459d52 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,17 +4,37 @@ FROM rasa/rasa-sdk:3.1.3 # Use subdirectory as working directory WORKDIR /app -# Copy any additional custom requirements, if necessary (uncomment next line) -# COPY actions/requirements-actions.txt ./ +# Create a cache directory for Hugging Face transformers +RUN mkdir -p /app/huggingface_cache +ENV HF_HOME=/app/huggingface_cache + +# Set permissions for the cache directory +RUN chown -R 1001 /app/huggingface_cache +RUN chmod -R 777 /app/huggingface_cache # Change back to root user to install dependencies USER root +RUN mkdir -p /.cache +RUN chown -R 1001:1001 /.cache +RUN chmod -R 777 /.cache + +# Copy any additional custom requirements, if necessary +COPY actions/requirements-actions.txt ./ + # Install extra requirements for actions code, if necessary (uncomment next line) -# RUN pip install -r requirements-actions.txt +RUN pip install --upgrade pip +RUN pip install --upgrade typing_extensions +RUN pip install -r requirements-actions.txt + # Copy actions folder to working directory COPY ./actions /app/actions +COPY ./mydata /app/mydata +COPY ./records /app/records + + +RUN chmod -R 777 /app/mydata # By best practices, don't run the code with root user -USER 1001 \ No newline at end of file +USER 1001 diff --git a/actions/actions.py b/actions/actions.py index be3a10f..fd5727f 100644 --- a/actions/actions.py +++ b/actions/actions.py @@ -1,29 +1,781 @@ -# This files contains your custom actions which can be used to run -# custom Python code. -# -# See this guide on how to implement these action: -# https://rasa.com/docs/rasa/custom-actions - - -# This is a simple example for a custom action which utters "Hello World!" - -from typing import Any, Text, Dict, List - -from rasa_sdk import Action, Tracker -from rasa_sdk.executor import CollectingDispatcher - - -class ActionHelloWorld(Action): - - def name(self) -> Text: - return "action_hello_world" - - def run(self, dispatcher: CollectingDispatcher, - tracker: Tracker, - domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: - - dispatcher.utter_message(text="Hello World!") - #dispatcher.utter_message(text="Hello back!") - - return [] - +# This files contains your custom actions which can be used to run +# custom Python code. +# +# See this guide on how to implement these action: +# https://rasa.com/docs/rasa/custom-actions + +from typing import Any, Text, Dict, List + +from rasa_sdk import Action, Tracker +from rasa_sdk.executor import CollectingDispatcher +from rasa_sdk.events import SlotSet +from rasa_sdk import FormValidationAction +from rasa_sdk.types import DomainDict + +import random +import math +from datetime import datetime +from sentence_transformers import SentenceTransformer +from sentence_transformers.util import pytorch_cos_sim +import pickle +import pandas as pd +from tqdm import tqdm +import numpy as np +from pprint import pprint + +BERTmodel_names=['paraphrase-multilingual-MiniLM-L12-v2','medmediani/Arabic-KW-Mdel','Ezzaldin-97/STS-Arabert','distiluse-base-multilingual-cased-v1','sentence-transformers/LaBSE'] +# data_path="my data/BASE8RGPH24V1_all_12_12_ID" +data_path="/app/mydata/BASE8RGPH24V4_all_18_12_ID" +data_path='/app/mydata/BASE8RGPH24V5_all_08_01_ID' + +df=pd.read_excel(data_path +'.xlsx') +unique_values_dict = {k[1]: (k[0][0],k[0][1],v,"situation") for k, v in dict(dict(zip(zip(zip(df["Situation_ID"], df["Module_ID"]), df['Situation ']),df["Situation_ID"]))).items() if (pd.notna(v) and v!='')} +## add tags to comparaison dict +unique_values_dict.update({k[1]: (k[0][0],k[0][1],v,"tags") for k, v in dict(dict(zip(zip(zip(df["Tags_ID"], df["Module_ID"]), df['Tags']),df["Situation_ID"]))).items() if (pd.notna(v) and v!='')}) +unique_values_dict.update({k[1]: (k[0][0],k[0][1],v,'situation Tags') for k, v in dict(dict(zip(zip(zip(df["tags_sit_ID"], df["Module_ID"]), df['situation Tags']),df["Situation_ID"]))).items() if (pd.notna(v) and v!='')}) +## add section to comparaison dict +unique_values_dict.update({k[1]: (k[0][0],k[0][1],v,"section") for k, v in dict(dict(zip(zip(zip(df["Section_ID"], df["Module_ID"]), df['Section']),df["Situation_ID"]))).items() if (pd.notna(v) and v!='')}) +## add question to comparaison +unique_values_dict.update({k[1]: (k[0][0],k[0][1],v,"question") for k, v in dict(dict(zip(zip(zip(df["Question_ID"], df["Module_ID"]), df["Question AI"]),df["Situation_ID"]))).items() if (pd.notna(v) and v!='')}) +unique_values_dict = {key: value for key, value in unique_values_dict.items() if not isinstance(key, float) or not math.isnan(key)} +situations_list = list(unique_values_dict.keys()) + + +# Open the file in write mode and write the dictionary content +with open('unique_values_dict.txt', 'w',encoding='utf-8') as file: + for key, value in unique_values_dict.items(): + file.write(f"{key}: {value}\n") + file.write(f"-----------------------------------------------------\n") + + +BERTmodel_name=BERTmodel_names[0] +BERT_model=SentenceTransformer(BERTmodel_name ) +pkl_path=data_path + BERTmodel_name.split('/')[0]+'_situations_embeddings.pkl' + +print('################# using MODEL:', BERTmodel_name) +### initialize weights + # Your dataset or list of situations +try: + #Load sentences & embeddings from disc + with open(pkl_path, "rb") as fIn: + stored_data = pickle.load(fIn) + situations = stored_data['situations'] + BERT_weights = stored_data['BERT_weights'] + + print("BERT model found") + + +except FileNotFoundError: + # BERT_model=SentenceTransformer(BERTmodel_) + print("BERT model loaded") + + BERT_weights = BERT_model.encode(situations_list, convert_to_tensor=True,show_progress_bar=True) + print("BERT model fine-tuned") + + #Store sentences & embeddings on disc + with open(pkl_path, "wb") as fOut: + pickle.dump({'situations': unique_values_dict, 'BERT_weights': BERT_weights}, + fOut, protocol=pickle.HIGHEST_PROTOCOL) + print("BERT model saved") + +def get_unique_elements_with_order(input_list): + return [element for i, element in enumerate(input_list) if element not in input_list[:i]] + + + +def provide_recommendations(user_input,THRESH, n,df,unique_values_dict,BERT_weights): + + + input_weight=BERT_model.encode(user_input, show_progress_bar = True,convert_to_tensor=True) + cosine_scores = pytorch_cos_sim(input_weight, BERT_weights) + cosine_scores = cosine_scores.cpu().numpy() + sort_ids=np.argsort(cosine_scores) + selected_ids=np.flip(sort_ids)[0] + # print('ordred cos',[cosine_scores[0][i] for i in selected_ids ]) + # print('selected ordred cos',[cosine_scores[0][i] for i in selected_ids if cosine_scores[0][i]>=THRESH]) + + ordred_situations= [(situations_list[i],cosine_scores[0][i]) for i in selected_ids if cosine_scores[0][i]>=THRESH] + ordred_situations_IDs=[{'input_text':user_input, + 'similar_text':situation[0], + 'element_ID':unique_values_dict[situation[0]][0], + 'module_ID':unique_values_dict[situation[0]][1], + 'situation_ID':unique_values_dict[situation[0]][2], + 'category':unique_values_dict[situation[0]][3], + "similarity":situation[1]} for situation in ordred_situations] + +# display(pd.DataFrame(ordred_situations_IDs[:n])) +# pprint(ordred_situations_IDs) +# df_with_qst=add_qst_ids(df,pd.DataFrame(ordred_situations_IDs[:n])) + return (pd.DataFrame(ordred_situations_IDs[:n])) +# return ordred_situations[:n],ordred_situations_IDs,get_unique_elements_with_order(ordred_situations_IDs)[:n] + +# def add_qst_ids(df, df_rslt): +# ids=[] +# for index, row in df_rslt.iterrows(): + +# if row['category']=='question': +# ids.append([row["element_ID"]]) +# if row['category']=='tags': +# ids.append( df[df['Tags_ID']==row["element_ID"]].Question_ID.unique()) +# if row['category']=='situation': +# ids.append(df[df['Situation_ID']==row["element_ID"]].Question_ID.unique()) +# if row['category']=='section': +# ids.append(df[df['Section_ID']==row["element_ID"]].Question_ID.unique()) +# df_rslt['ralated_questions']=ids +# return df_rslt + + + +# def module_recommendations(df_rslt,n=3): +# module_ids = df_rslt['module_ID'].unique()[:n].tolist() +# module_names=[df[df.Module_ID==module_id].module.unique().tolist()[0] for module_id in module_ids] +# df[df.Module_ID.isin(module_ids)].module.unique().tolist() +# return module_ids,module_names + + +# def situation_recommendations(df_rslt,module_id,n=3): +# output_ids=[] +# situation_IDs = df_rslt[df_rslt['module_ID']==int(module_id)]["situation_ID"].unique().tolist() +# print('##########situation_IDs',situation_IDs) +# print(df_rslt[df_rslt.module_ID==int(module_id)]["situation_ID"].unique()) +# for i in situation_IDs: +# if i!=22 : +# if len(output_ids)=n and j not in output and j not in reste: + +# reste.append(j) +# reste_question.append(df[df.Question_ID==int(j)]["Question AI"].unique().tolist()[0]) + +# # question_names=df[df.Question_ID.isin(output)]["Question AI"].unique().tolist() +# return(output,question_names,reste,reste_question) + +# def get_responses(question_id): + +# response=df[df.Question_ID==question_id]['Réponse Quasi-finale'].unique().tolist() +# return(response) +def add_qst_ids(df, df_rslt): + + ids=[] + for index, row in df_rslt.iterrows(): + id_=[] + + for i in row["ralated_responses"]: + id_.append(df[df['response_ID']==int(i)].Question_ID.unique()) + ids.append(id_) + df_rslt['ralated_questions']=ids + + return df_rslt + +def add_resp_ids(df, df_rslt): + ids=[] + for index, row in df_rslt.iterrows(): + + if row['category']=='question': + ids.append( df[df['Question_ID']==row["element_ID"]].response_ID.unique()) + if row['category']=='tags': + ids.append( df[df['Tags_ID']==row["element_ID"]].response_ID.unique()) + if row['category']=='situation Tags': + ids.append( df[df['tags_sit_ID']==row["element_ID"]].response_ID.unique()) + if row['category']=='situation': + ids.append(df[df['Situation_ID']==row["element_ID"]].response_ID.unique()) + if row['category']=='section': + ids.append(df[df['Section_ID']==row["element_ID"]].response_ID.unique()) + df_rslt['ralated_responses']=ids + return df_rslt + +def module_recommendations(df_rslt,n=3): + module_ids = df_rslt['module_ID'].unique()[:n].tolist() + module_names=[df[df.Module_ID==module_id].module.unique().tolist()[0] for module_id in module_ids] + df[df.Module_ID.isin(module_ids)].module.unique().tolist() + return module_ids,module_names + + +def situation_recommendations(df_rslt,module_id,n=3,nan_id=16): + output_ids=[] + situation_IDs = df_rslt[df_rslt['module_ID']==int(module_id)]["situation_ID"].unique().tolist() + print('##########situation_IDs',situation_IDs) + print(df_rslt[df_rslt.module_ID==int(module_id)]["situation_ID"].unique()) + for i in situation_IDs: + if i!=nan_id : + if len(output_ids) 0.8 :#and df_rslt_with_qst.head(1)["situation_ID"].values[0]==22: + + questions=df_rslt_with_qst.head(1)["ralated_responses"].values.tolist() + questions.extend(df_rslt_with_qst[df_rslt_with_qst['situation_ID']==situation_ID]["ralated_responses"].values.tolist()) + output=[] + reste=[] + question_names=[] + reste_question=[] + resp_done=[] + for i in questions: + for j in i: + q_id=random.choice(df[df.response_ID==int(j)]["Question_ID"].unique().tolist()) + if len(output)=n and q_id not in output and q_id not in reste: + + reste.append(q_id) + reste_question.append(df[df.response_ID==int(j)]["Question AI"].unique().tolist()[0]) + + # question_names=df[df.Question_ID.isin(output)]["Question AI"].unique().tolist() + return(output,question_names,reste,reste_question) + +# def question_recommendations1(df_rslt_with_qst,situation_ID,n=3): +# questions=[] +# if df_rslt_with_qst.head(1)["category"].values[0]=="question" :#and df_rslt_with_qst.head(1)["situation_ID"].values[0]==22: + +# questions=df_rslt_with_qst.head(1)["ralated_questions"].values.tolist() +# questions.extend(df_rslt_with_qst[df_rslt_with_qst['situation_ID']==situation_ID]["ralated_questions"].values.tolist()) +# output=[] +# reste=[] +# question_names=[] +# reste_question=[] +# for i in questions: +# for q in i: +# j=random.choice(q) +# if len(output)=n and j not in output and j not in reste: + +# reste.append(j) +# reste_question.append(df[df.Question_ID==int(j)]["Question AI"].unique().tolist()[0]) + +# # question_names=df[df.Question_ID.isin(output)]["Question AI"].unique().tolist() +# return(output,question_names,reste,reste_question) +def get_responses(question_id): + + response=df[df.Question_ID==question_id]['Réponse Quasi-finale'].unique().tolist() + return(response) +class ActionAskNavigateData(Action): + def name(self) -> str: + return "action_ask_navigate_data" + + def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: dict) -> list: + message = "هل تريد التنقل في البيانات؟" + buttons = [{"title": "نعم", "payload": "/affirm"}, {"title": "لا", "payload": "/deny"}] + + dispatcher.utter_button_message(message, buttons=buttons) + return [] + +class ActionStopNavigation(Action): + def name(self) -> str: + return "action_stop_navigation" + + def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: dict) -> list: + dispatcher.utter_message("بالتأكيد! إذا كان لديك أي أسئلة أخرى أو إذا كنت بحاجة إلى مزيد من المعلومات، فلا تتردد في طرحها. أنا هنا للمساعدة") + return [] + +class ActionGetUserQuestion(Action): + def name(self) -> str: + return "action_get_user_question" + + def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain) -> list: + + user_message_all = tracker.latest_message.get('text') + print('################################MESSAGE',user_message_all) + sender_id = tracker.sender_id + path="records/df_recommendations_"+sender_id+".xlsx" + # Assuming you have a function to provide recommendations based on user input + df_recommendations = provide_recommendations( user_message_all ,THRESH=0.3, n=1000,df=df,unique_values_dict=unique_values_dict,BERT_weights=BERT_weights) + df_recommendations.to_excel(path,index=False) + if df_recommendations.empty: + dispatcher.utter_message("من فضلك أعد صياغة سؤالك") + return [] + else : + module_ids,module_names=module_recommendations(df_recommendations,n=3) + # button_list = [{"title": module_names[i], "payload": '/module_definitions'+str(module_ids[i])} for i in range(len(module_ids))] + button_list = [{"title": module_names[i], "payload": f'/inform_module{{"module_id":"{str(module_ids[i])}"}}'} for i in range(len(module_ids))] + + dispatcher.utter_message(text="اختر الوحدة المتعلقة بسؤالك", buttons=button_list) + # Set the user_question value in a slot for future use + return [SlotSet("user_question", user_message_all)] + + +class ActionReselectModule(Action): + def name(self) -> str: + return "action_reselect_module" + + def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain) -> list: + + sender_id = tracker.sender_id + path="records/df_recommendations_"+sender_id+".xlsx" + # Assuming you have a function to provide recommendations based on user input + df_recommendations=pd.read_excel(path) + if df_recommendations.empty: + dispatcher.utter_message("من فضلك أعد صياغة سؤالك") + return [] + else : + module_ids,module_names=module_recommendations(df_recommendations,n=3) + # button_list = [{"title": module_names[i], "payload": '/module_definitions'+str(module_ids[i])} for i in range(len(module_ids))] + button_list = [{"title": module_names[i],"payload": f'/inform_module{{"module_id":"{str(module_ids[i])}"}}'} for i in range(len(module_ids))] + + dispatcher.utter_message(text="اختر الوحدة المتعلقة بسؤالك", buttons=button_list) + # Set the user_question value in a slot for future use + return [] + +class ActionGetModuleId(Action): + def name(self): + return "action_get_module_id" + + def run(self, dispatcher, tracker, domain): + # Extract payload from the latest message + + # module_number = tracker.latest_message['text'] + + # print('######module_number',module_number) + # latest_intent = tracker.latest_message.get("intent", {}).get("name", "") + latest_entities = tracker.latest_message.get("entities", []) + + if latest_entities: + # Assuming you have only one entity in the latest message + entity_name = latest_entities[0].get("entity") + entity_value = latest_entities[0].get("value") + print('######latest_intent',entity_name,entity_value) + + # Your action logic here + return [SlotSet(entity_name, entity_value)] + + +class ActionGet_Situations(Action): + def name(self): + return "action_get_situations" + + def run(self, dispatcher, tracker, domain): + # Access the ID from the slot + # module_number = tracker.latest_message['text'] + module_number = tracker.get_slot('module_id') + print('##############SLOT',module_number) + # dispatcher.utter_message(text=f"You choosed module{module_number}") + sender_id = tracker.sender_id + path="records/df_recommendations_"+sender_id+".xlsx" + df_rslt=pd.read_excel(path) + situation_ids,situation_names=situation_recommendations(df_rslt,int(module_number),n=3) + if situation_ids==[]: + dispatcher.utter_message("لا يوجد السياق متاح في هذه الوحدة") + else: + + button_list = [{"title": situation_names[i], "payload": f'/inform_module{{"situation_id":"{str(situation_ids[i])}"}}' } for i in range(len(situation_ids))] + button_list.append({"title": "انقر هنا لإعادة إختيار الوحدة", "payload": '/rechoisir_module'}) + dispatcher.utter_message(text= "اختر السياق الأقرب إلى سؤالك",buttons=button_list) + return [] + +class ActionGetsituationId(Action): + def name(self): + return "action_get_situation_id" + + def run(self, dispatcher, tracker, domain): + # Extract payload from the latest message + + # situation_number = tracker.latest_message['text'] + # print('######situation_number',situation_number) + latest_entities = tracker.latest_message.get("entities", []) + + if latest_entities: + # Assuming you have only one entity in the latest message + entity_name = latest_entities[0].get("entity") + entity_value = latest_entities[0].get("value") + # Your action logic here + return [SlotSet(entity_name, entity_value)] + + +class ActionGet_Questions(Action): + def name(self): + return "action_get_questions" + + def run(self, dispatcher, tracker, domain): + # Access the ID from the slot + # situation_number = tracker.latest_message['text'] + situation_number = tracker.get_slot('situation_id') + # dispatcher.utter_message(text=f"You choosed situation {situation_number}") + + # Use the ID in your action logic + sender_id = tracker.sender_id + path="records/df_recommendations_"+sender_id+".xlsx" + df_rslt=pd.read_excel(path) + # df_with_qst=add_qst_ids(df,df_rslt) + df_resp=add_resp_ids(df,df_rslt) + df_with_qst=add_qst_ids(df,df_resp) + df_with_qst.to_excel(path,index=False) + + question_ids,question_names,reste,reste_question=question_recommendations(df_with_qst,int(situation_number),n=5) + print(question_ids,question_names) + if question_ids==[]: + dispatcher.utter_message("لا يوجد سؤال متاح في هذا السياق") + else: + print("##########reste", len(reste)) + list_prop=[ + "اختر السؤال الأقرب", + "حدد السؤال الذي يناسبك أكثر", + "اختر السؤال الأقرب إلى ما تبحث عنه", + "اختر السؤال الذي يلائم احتياجاتك", + "اختر الاستفسار الذي يتناسب مع موضوعك", + "اختر السؤال الأقرب إلى متطلباتك", + "اختر السؤال الذي يتناسب مع طلبك", + "اختر الاستفسار الأنسب لك", + "اختر السؤال الذي يناسب طلبك بشكل أفضل", + "اختار الاستفسار الذي يعكس اهتماماتك", + "اختر السؤال الأقرب إلى سؤالك" + ] + random.shuffle(list_prop) + + button_list = [{"title": question_names[i], "payload": f'/inform_module{{"question_id":"{str(question_ids[i])}"}}' } for i in range(len(question_ids))] + dispatcher.utter_message(text= list_prop[0],buttons=button_list) + return [] + +class ActionGetQuestionId(Action): + def name(self): + return "action_get_question_id" + + def run(self, dispatcher, tracker, domain): + # Extract payload from the latest message + + # question_number = tracker.latest_message['text'] + # print('######question_number',question_number) + latest_entities = tracker.latest_message.get("entities", []) + + if latest_entities: + # Assuming you have only one entity in the latest message + entity_name = latest_entities[0].get("entity") + entity_value = latest_entities[0].get("value") + # Your action logic here + return [SlotSet(entity_name, entity_value)] + +class ActionGet_Response(Action): + def name(self): + return "action_get_response" + + def run(self, dispatcher, tracker, domain): + # Access the ID from the slot + # question_number = tracker.latest_message['text'] + question_number = tracker.get_slot('question_id') + # dispatcher.utter_message(text=f"You choosed question {question_number}") + response=get_responses(int(question_number)) + # Use the ID in your action logic + dispatcher.utter_message(text=f" {response[0]}") + messages = [ + "إذا كانت لديك استفسارات إضافية، فلا تتردد في طرحها", + "في حال كانت لديك أي أسئلة إضافية، لا تتردد في السؤال", + "إذا كنت بحاجة إلى أي استفسار آخر، فأنت حر في طرحه", + "إذا كان لديك سؤال آخر، فأنا هنا للمساعدة", + "إذا كان لديك أسئلة أخرى، لا تتردد في طرحها" + ] + + # Shuffle the messages randomly + random.shuffle(messages) + + # Choose and send one of the messages + response = messages[0] + dispatcher.utter_message(text=response) + + return [] + +class ActionGuidance(Action): + def name(self) -> Text: + return "action_guidance" + + def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: + message = ( + "للحصول على تعاريف الوحدات، اكتب **تعريف الوحدات**\n" + "للبحث عن سؤال معين، اكتب **تصفح البيانات**" + ) + + dispatcher.utter_message(text=message, parse_mode="Markdown") + + return [] + + + +class ActionUtterModuleButtons(Action): + def name(self) -> Text: + return "action_utter_module_buttons" + + def run(self, dispatcher: CollectingDispatcher, + tracker: Tracker, + domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: + + # Define a list of module titles + module_titles = [ + "مفاهيم التعرف على الميدان", + "تحديد أفراد الأسرة", + "الميزات الديموغرافية لأفراد الأسرة", + "الخصوبة", + "الصعوبة في ممارسة الأنشطة الاعتيادية", + "الهجرة الدولية لأفراد الأسرة خلال السنوات الخمس الأخيرة", + "أحداث الوفاة ضمن أفراد الأسرة خلال الخمس سنوات الأخيرة", + "الهجـــرة", + "الأمية واللغات", + "التعليم", + "النشـاط الاقتصادي والتنقل للـعمل", + "الحماية الاجتماعية", + "استعمال أجهزة تكنولوجيا المعلومات والاتصالات", + "ظروف سكن الأسرة", + "حالات عامة" + # Add more module titles as needed + ] + + # Create buttons based on the list of module titles + button_list = [{"title": title, "payload": f"module_definitions{i+1}"} for i, title in enumerate(module_titles)] + + # # Generate the utterance with buttons + # message = { + # "text": "اضغط على اسم الوحدة للحصول على تعريفها", + # "buttons": buttons + # } + + # dispatcher.utter_message(attachment=message) + + dispatcher.utter_message(text= "اضغط على اسم الوحدة للحصول على تعريفها", buttons=button_list) + + return [] + +class ActionGetModuleDefinitions(Action): + def name(self) -> Text: + return "action_get_module_definitions" + + def run(self, dispatcher: CollectingDispatcher, + tracker: Tracker, + domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: + + # Get the entire dictionary of definitions + definitions_dict = { + "module_definitions1": "المبادئ والمصطلحات الأساسية في مجال الإحصاء السكاني والعمران، بما في ذلك مفاهيم مثل الإحصاء ومنطقة الإحصاء والبناية والمسكن وأنواع البنايات المختلفة والمصطلحات ذات الصلة", + "module_definitions2": "تحديد بنية الأسرة وعدد أفرادها وتحديد العلاقات بينهم وكذلك توثيق معلومات حول هوياتهم وخصائصهم الديموغرافية", + "module_definitions3": "توثيق الخصائص الرئيسية لأفراد الأسرة، مثل العمر، والجنس، والحالة الاجتماعية، والجنسية، وأي خصائص أخرى ترتبط بأفراد الأسرة", + "module_definitions4": "تسجيل عدد الأطفال المولودين للأسرة خلال فترة زمنية معينة، وتحديد معدل الخصوبة في الأسرة", + "module_definitions5": "تقييم مدى الصعوبة أو السهولة في أداء الأنشطة اليومية لأفراد الأسرة ، مثل الحركة، والعناية الذاتية، وغيرها", + "module_definitions6": "تسجيل حركة الهجرة الدولية لأفراد الأسرة، بما في ذلك الهجرة إلى ومن البلاد", + "module_definitions7": "تسجيل وتوثيق الحوادث المتعلقة بالوفيات في دائرة أفراد الأسرة خلال الفترة المحددة", + "module_definitions8": "رصد وتوثيق حركة التنقل للأفراد بين المناطق الجغرافية داخل البلاد", + "module_definitions9": "تحديد مستوى الأمية واللغات التي يجيدونها أفراد الأسرة", + "module_definitions10": "توثيق مستوى التعليم والتحصيل الدراسي لكل فرد في الأسرة", + "module_definitions11": "تحديد مصدر العيش والنشاط الاقتصادي الرئيسي لأفراد الأسرة وطريقة التنقل إلى مكان العمل", + "module_definitions12": "توثيق استفادة أفراد الأسرة من برامج الحماية الاجتماعية والضمان الاجتماعي", + "module_definitions13": "تقييم وتوثيق استخدام أفراد الأسرة للتكنولوجيا الحديثة مثل الإنترنت والهواتف الذكية", + "module_definitions14": "توثيق معلومات حول سكن الأسرة بما في ذلك نوع الإقامة والحالة الصحية للمسكن ومرافقه", + "module_definitions15":"حالات عامة" + # Add more definitions as needed + } + + user_selection = tracker.latest_message.get('text') + + # Send the entire dictionary as a response + dispatcher.utter_message(text=str(definitions_dict[user_selection])) + + return [] + +class ActionGetModuleDefinitions2(Action): + def name(self) -> Text: + return "action_get_module_definitions2" + + def run(self, dispatcher: CollectingDispatcher, + tracker: Tracker, + domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: + + # Get the entire dictionary of definitions + definitions_dict={ + 'module_definitions1': 'المبادئ والمصطلحات الأساسية في مجال الإحصاء السكاني ' + 'والعمران، بما في ذلك مفاهيم مثل الإحصاء ومنطقة ' + 'الإحصاء والبناية والمسكن وأنواع البنايات المختلفة ' + 'والمصطلحات ذات الصلة', + 'module_definitions10': 'يهدف محور التعليم في الاستمارة إلى تحديد استفادة ' + 'أفراد الأسر من التعليم بمختلف مستوياته، وذلك من خلال ' + 'الاستفسار عن عدة نقط. بدءًا بالاستفادة من التعليم ' + 'الأولي، المستوى التعليمي العام ونوعه، الدراسة خلال ' + 'السنة الدراسية 2023-2024، ثم أعلى دبلوم أو شهادة ' + 'محصل عليها في التعليم العام والتكوين المهني.', + 'module_definitions11': 'في محور النشاط الاقتصادي والتنقل للعمل، تمت برمجة ' + 'عدة أسئلة تمكن من تصنيف السكان الإعتياديين حسب نوع ' + 'نشاطهم الاقتصادي وتجميع بيانات حول المهن والتنقل ' + 'للعمل. يتم ذلك عبر الاستفسارا عن نوع النشاط ' + 'الاقتصادي، تحديد المهنة والحالة في المهنة والنشاط ' + 'الرئيسي للمؤسسة، ثم تحديد مكان العمل ووسيلة التنقل ' + 'المستعملة. هذه البيانات ستُمكن من توفير قاعدة بيانات ' + 'مهمة لتقييم البرامج الاجتماعية والاقتصادية وكذا وضع ' + 'استراتيجيات مستقبلية لتحقيق الأهداف المسطرة سواء ' + 'فيما يتعلف بأهداف التنمية المستدامة لسنة 2030 أو ' + 'أهداف النموذج التنموي الجديد في أفق سنة 2035', + 'module_definitions12': 'في سياق تعميم الحماية الاجتماعية، سيتم تجميع ' + 'البيانات المتعلقة بهذا الموضوع ليتم رص التقدم ' + 'المُحرز في هذا المجال. في الجزء الأول، سيتم ' + 'الاستفسار عن الانخراط أو الاستفادة من أنظمة التغطية ' + 'الصحية (AMO، AMO تضامن، شركة تأمين خاصة، مؤسسة أخرى ' + 'للتضامن). وفي الجزء الثاني، سيتم تجميع المعطيات حول ' + 'الإنخراط أو الاستفادة من إحدى أنظمة التقاعد المغربية ' + '( CMR، CNSS، RCAR، CIMR)', + 'module_definitions13': 'يهتم محور استعمال تكنولوجيا المعلومات والاتصالات إلى ' + 'تحديد مدى توفر الأفراد البالغين 5 سنوات فأكثر على ' + 'هاتف نقال خاص ذكي أو عادي، جهاز كمبيوتر مكتبي خاص أو ' + 'كمبيوتر محمول خاص أو لوحة إلكترونية خاصة، وكذلك مدى ' + 'استعمالهم للأنترنيت خلال الثلاث أشهر الأخيرة. كل هذه ' + 'المعطيات الإحصائية ستمكن من تقييم مستوى المؤِشرات ' + 'المرتبطة بأهداف التنمية المستدامة لسنة 2030.', + 'module_definitions14': 'من أجل تحديد الظروف السكنية لكل أسرة تمت برمجت سلسلة ' + 'من الأسئلة حول خاصيات المسكن وكيفية استعمال بعض ' + 'تجهيزاته. هذه البيانات تساع بشكل كبير في تقدير ' + 'احتياجات الإسكان والنجاح في التخطيط لبرامج الإسكان. ' + 'يُذكر بأن تعريف المسكن في الإحصاء هو المكان الذي ' + 'تعيش فيه الأسرة، ويمكن أن يكون عبارة عن حجرة واحدة ' + 'أو عدة حجرات مخصصة للسكن، والمسكن يتوفر على مدخل أو ' + 'عدة مداخل مباشرة. يتم الاستفسار في هذا المحور حول ' + 'خصائص هذا المسكن، وتوفر الأسرة على بعض التجهيزات ' + 'الأساسية، وكذا المسافة التي تفصل المسكن على بعض ' + 'المرافق والتجهيزات الأساسية ومعطيات أخرى تصف ظروف ' + 'سكن الأسرة.', + 'module_definitions15': 'تهدف الأسئلة والأجوبة حول الحالات العامة لتأطير تصرف ' + 'الباحث أثناء تعامل مع مختلف الأسر، سواءً كان ذلك في ' + 'ارتباط مباشر مع ملأ الإستمارة، أو كان عاماً مثل ' + 'الحصول على دعوة لشرب الشاي مع أفراد الأسرة وغيرها. ' + 'وهذا ليتم تأطير الأفعال قانونيا وأخلاقيا، وكذا تسهيل ' + 'عملية الاستجواب وملأ الإستمارة.', + 'module_definitions2': 'تهدف فقرة تحديد أفراد الأسرة إلى تعداد السكان ' + 'المقيمين الإعتياديين ضمن الأسر القاطنة بكل منطقة ' + 'إحصاء. بحيث أن الأسرة بمفهوم الإحصاء هي مجموعة من ' + 'الأشخاص يقيمون بصفة إعتيادية بمسكن واحد أو جزء منه، ' + 'وتكون لهم نفقات مشتركة. والمقيم الإعتيادي هو كل فرد ' + 'أسرة يقيم بالمسكن أو له نية الإقامة به لمدة لا تقل عن ' + '6 أشهر.', + 'module_definitions3': 'الغرض من تحديد المميزات الديموغرافية لأفراد الأسرة هو ' + 'معرفة مميزات السكان الإعتياديين وتركيبتهم. السن ' + 'والجنس من أكثر الخاصيات التي تتم جدولة معلوماتها بصفة ' + 'مترابطة مع المعطيات الأخرى المُجَمعة في باقي أجزاء ' + 'الإستمارة. تساعد هذه البيانات في تحليل تطور المميزات ' + 'الديموغرافية في الزمن. يتم أيضا تجميع البيانات عن ' + 'الجنسية، التسجيل في دفتر الحالة المدنية، الحالة ' + 'الزواجية والسن عند الزواج الأول.', + 'module_definitions4': 'يهدف إدراج الأسئلة حول الخصوبة وجمع البيانات إلى ' + 'دراسة ظاهرة الخصوبة ووفيات الرضع والأطفال، والحصول ' + 'على معلومات دقيقة للتخطيط واتخاذ القرارات الصحية ' + 'والاجتماعية. يجب التنويه إلى أن جميع الأسئلة حول ' + 'الخصوبة تهم المواليد الأحياء (المواليد الذين ظهرت ' + 'عليهم بعد الولادة علامات الحياة كالتنفس والصراخ أو ' + 'دقات القلب) وليس المواليد الأموات (المواليد الذين لم ' + 'تظهر عليهم علامات الحياة بعد الولادة). وتنقسم الخصوبة ' + 'إلى خصوبة إجمالية أي طيلة الحياة، وإلى خصوبة حالية أي ' + 'خلال 12 شهرًا الأخيرة للنساء في سن الإنجاب فقط، ما ' + 'بين 15 و49 سنة.', + 'module_definitions5': 'بهدف الإحاطة بالصعوبات التي يواجهها الأفراد في القيام ' + 'بالأنشطة اليومية، يتم الاستفسار عن درجة الصعوبة التي ' + 'يواجهها الأفراد بخصوص 6 وظائف أساسية: الرؤية (حتى مع ' + 'استعمال نظارات طبية)، السمع (حتى مع استعمال وسائل ' + 'سمعية)، الحركة (المشي أو صعود الدرج)، التواصل مع ' + 'الآخرين، التذكر والتركيز، والاعتناء بالذات (مثل ' + 'الاستحمام وارتداء الملابس). يتم الاستفسار عن درجة ' + 'الصعوبات المنبثقة عن المشاكل الصحية التي قد تواجه ' + 'الفرد عند قيامه بأنشطة معينة، بدلاً من الاستفسار ' + 'مباشرة عما إذا كان لديه إعاقة معينة.', + 'module_definitions6': 'في محور الهجرة الدولية لأفراد الأسرة خلال السنوات ' + 'الخمس الأخيرة، يتم تجميع المعطيات الخاصة بكل شخص كان ' + 'ضمن أفراد الأسرة وهاجر إلى خارج المغرب في الفترة ما ' + 'بين 1 شتنبر 2019 و 31 غشت 2024. هذه المعطيات تشمل اسم ' + 'الفرد المهاجر، تاريخ الهجرة الأخيرة، جنس وجنسية وسن ' + 'الفرد المهاجر، ثم الخاصيات المتعلقة بالتعليم للفرد عن ' + 'هجرته، ثم سبب هجرته و بلد الإستقبال عند هجرته الأخيرة', + 'module_definitions7': 'يهتم محول أحداث الوفاة ضمن أفراد الأسرة خلال الخمس ' + 'سنوات الأخيرة بتحديد جميع أحداث الوفاة التي وقعت ما ' + 'بين 1 شتنبر 2019 و 31 غشت 2024، ضمن الأفراد الذين ' + 'كانوا يقيمون بصفة إعتيادية مع الأسرة، وذلك سواءً كانو ' + 'رضعا أو أطفالا أو بالغين وكيفما كان جنسهم. سيتم تجميع ' + 'البيانات التي تتمثل في اسم الفرد المتوفي، تاريخ أو ' + 'سنة الوفاة، جنسه، سنه عند الوفاة، حالته الزوجية عند ' + 'الوفاة، ثم أخيرا سبب الوفاة.', + 'module_definitions8': 'الأسئلة المتعلقة بظاهرة الهجرة في الاستبيان تهدف إلى ' + 'دراسة حركة الهجرة بين مختلف الوحدات الإدارية داخل ' + 'المغرب و الحركة السكانية ما بين المغرب والبلدان ' + 'الأخرى. ويتم ذلك عبر الاستفسار عن مكان ازدياد ' + 'الأفراد، مكان إقامتهم ما قبل الحالية، مكان الإقامة ' + 'ليلة بداية الحجر الصحي وعن ما إذا كان الفرد سبق له أن ' + 'أقام بخارج المغرب وسبب القدوم أو العودة في تلك ' + 'الحالة.', + 'module_definitions9': 'محور الأمية واللغات يتطرق إلى 3 أجزاء. أولها الأمية، ' + 'التي تهدف أسئلتها إلى تحديد مدى قدرة الأفراد البالغين ' + '10 سنوات فأكثر على القراءة والكتابة وكذلك على القيام ' + 'بعمليات حسابية ذهنية أثناء قيامهم بأنشطتهم اليومية. ' + 'ثانيها اللغات المقروءة والمكتوبة، بحيث يهدف هذا الجزء ' + 'إلى تحديد اللغات التي يتمكن الأفراد من القراءة ' + 'والكتابة بها حسب درجة الإتقان. وآخرها اللغات المحلية ' + 'المستعملة، بحيث يتم استفسار الأفراد عن أي لغة محلية ' + 'أكثر استعمالا في تعاملاتهم اليومية.'} + + user_selection = tracker.latest_message.get('text') + + # Send the entire dictionary as a response + dispatcher.utter_message(text=str(definitions_dict[user_selection])) + + return [] + + +class LogConversation(Action): + def name(self) -> Text: + return "action_log_conversation" + + def run( + self, + dispatcher: CollectingDispatcher, + tracker: Tracker, + domain: Dict[Text, Any], + ) -> List[Dict[Text, Any]]: + # Extract conversation data with message types and sender IDs + sender_id = tracker.sender_id + conversation_data = [] + + for event in tracker.events: + if 'text' in event: + message = event['text'] + message_type = 'user' if event['event'] == "user" else 'bot' + time = event.get('timestamp', '') + conversation_data.append({'sender_id': sender_id, 'message': message, 'message_type': message_type, 'Time': time}) + + # Format the date and time + current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + # Specify the file path + file_path = "conversation_log.txt" + + # Open the file in append mode and write the formatted datetime and conversation data + with open(file_path, "a", encoding="utf-8") as file: + file.write(f"Timestamp: {current_datetime}\n") + for entry in conversation_data: + # file.write(f"(sender_id: {entry['sender_id']}){entry['message_type'].capitalize()}: {entry['message']} (Time: {entry['Time']})\n") + file.write(f"sender_id: {entry['sender_id']} , {entry['message_type'].capitalize()}: {entry['message']} , Time: {entry['Time']}\n") + + file.write("\n") # Add a newline between conversations + + return [] diff --git a/actions/actions1.py b/actions/actions1.py new file mode 100644 index 0000000..be3a10f --- /dev/null +++ b/actions/actions1.py @@ -0,0 +1,29 @@ +# This files contains your custom actions which can be used to run +# custom Python code. +# +# See this guide on how to implement these action: +# https://rasa.com/docs/rasa/custom-actions + + +# This is a simple example for a custom action which utters "Hello World!" + +from typing import Any, Text, Dict, List + +from rasa_sdk import Action, Tracker +from rasa_sdk.executor import CollectingDispatcher + + +class ActionHelloWorld(Action): + + def name(self) -> Text: + return "action_hello_world" + + def run(self, dispatcher: CollectingDispatcher, + tracker: Tracker, + domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: + + dispatcher.utter_message(text="Hello World!") + #dispatcher.utter_message(text="Hello back!") + + return [] + diff --git a/actions/requirements-actions.txt b/actions/requirements-actions.txt new file mode 100644 index 0000000..6b431b9 --- /dev/null +++ b/actions/requirements-actions.txt @@ -0,0 +1,5 @@ +sentence-transformers +pandas +tqdm +numpy +openpyxl==3.1.2 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- externalIPs: [YOUR.SERVERS.IP.ADDRESS] + externalIPs: [35.188.219.145] rasax: # initialUser is the user which is created upon the initial start of Rasa X @@ -53,5 +53,3 @@ global: redis: # password to use in case there no external secret was provided password: "AnotherSecureRandomString" -app: - install: false diff --git a/rasax/tls-values-with-actions.yml b/rasax/tls-values-with-actions.yml index acc2abb..efee1a3 100644 --- a/rasax/tls-values-with-actions.yml +++ b/rasax/tls-values-with-actions.yml @@ -1,15 +1,15 @@ # debugMode enables / disables the debug mode for Rasa and Rasa X debugMode: true nginx: - enabled: true - subPath: "/rasax" +# enabled: true +# subPath: "/rasax" #customConfConfigMap: "custom-nginx-conf" #certificateSecret: "rasa-webservice-ingress-tls" service: enabled: true # connect LoadBalancer directly to VMs' internal IP # You get this value with: $ hostname -I - externalIPs: [YOUR.SERVERS.IP.ADDRESS] + externalIPs: [35.226.208.22] rasax: # initialUser is the user which is created upon the initial start of Rasa X @@ -24,10 +24,10 @@ rasax: jwtSecret: "AnotherSecureRandomString" # tag refers to the Rasa X image tag tag: "1.1.4" - extraEnvs: - - name: ROOT_URL - #value: "localhost/rasa-x" - value: "https://EXAMPLE.COM/rasax" + #extraEnvs: + # - name: ROOT_URL + # #value: "localhost/rasa-x" + # value: "http://35.226.208.22/rasax" rasa: # token Rasa accepts as authentication token from other Rasa services token: "AnotherSecureRandomString" @@ -63,5 +63,5 @@ global: password: "AnotherSecureRandomString" app: install: true - name: "/example-action-server" - tag: "" + name: "localhost:32000/rasax-deployment-guide" + tag: "0.0.1"