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app.py

@@ -1,9 +1,10 @@
 from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse
 from fastapi.templating import Jinja2Templates
-from chat import chat_conversations
+from chat import chat_conversations, create_new_session
 import spacy
-
+import os
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 app = FastAPI()
 
 templates = Jinja2Templates(directory="templates")
@@ -21,8 +22,17 @@ async def read_root(request: Request):
     print(request)
     form_data = await request.json()
     query = form_data.get('query')
-    response_text = chat_conversations(query)
+    user_id = form_data.get("user_id")
+    response_text = chat_conversations(query,user_id)
     return response_text
 
+@app.post("/start-session")
+async def start_session(request: Request):
+    form_data = await request.json()
+    print("form data",form_data)
+    user_id = form_data.get("user_id")
+    print("save user id ",user_id)
+    response = create_new_session(user_id)
 
-
+    print(response)
+    return response

chat.py

@@ -1,95 +1,61 @@
 from langchain.memory import ConversationBufferWindowMemory
-from langchain.chains import ConversationChain
-from langchain_groq import ChatGroq
-from langchain.chat_models import ChatOpenAI
-from langchain_core.prompts.prompt import PromptTemplate
+from langchain_community.chat_models import ChatOpenAI
 from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
 from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
-from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
-from presidio_anonymizer import AnonymizerEngine
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.schema.runnable import RunnablePassthrough
+from langchain_core.utils.function_calling import convert_to_openai_function
 
-import os
+from config import settings
 
-openai_key = os.environ['OPENAIKEY']
-def deanonymizer(input,anonymizer):
-  input=anonymizer.deanonymize(input)
-  map = anonymizer.deanonymizer_mapping
-  if map:
-    for k in map["PERSON"]:
-      names = k.split(" ")
-      for i in names:
-        input = input.replace(i,map["PERSON"][k])
-  return input
-    
-template = f"""
-You are a best friend and supportive friend designed to talk with teenage girls in mobile app called BMOXI. Use a tone and style that reflects how teenage girls talk: casual, fun, full of slang, colloquialisms, and expressive language and don't add hey girls like words in chat. chat should be looks like real conversation between 2 girls.
-Incorporate texting language too. Ask follow-up questions like a best friend would. Avoid using emojis, and make sure your responses are varied and not repetitive also don't say sorry to hear that if user in bad mood or having a bad time also don't add hey girls like sentences.
-
-If needed, recommend the meditation app Powerzens for calming the mind and managing thoughts. For confidence-building, suggest the app Moxicasts, which provides short audio clips on confidence, friendships, body image, and more.
-
-Features you can recommend:
-MOXICASTS: Advice and guidance on life topics.
-PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
-POWER ZENS: Mini meditations for emotional control.
-THE SOCIAL SANCTUARY: Anonymous community forum for support and sharing.
-MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
-PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
-SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings (not maintained).
-INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
-1:1 MENTORING: Personalized mentoring (coming soon).
-MY RITUALS: Create personalized self-care routines.
-MY REWARDS: Earn points for self-care, redeemable for gift cards.
-MY VIBECHECK: Monitor and understand emotional patterns.
-MY JOURNAL: Guided journaling exercises for self-reflection.
-BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.
-
-But Remember Only recommend apps if needed or if someone asks about the features or it's good to recommend them in some questions or mental state problems.
-
-Current conversation:
-{{history}}
-Human: {{input}}
-AI Assistant:"""
-
-
-# Create the prompt template
-PROMPT = PromptTemplate(
-    input_variables=["history", "input"],
-    template=template
-)
+from utils import deanonymizer, create_agent, set_chat_bot_name
 
-# Initialize the ChatGroq LLM
-llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_key, temperature=0.7)
-# llm = ChatGroq(temperature=0,groq_api_key="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", model_name="llama3-70b-8192")
-#model=llama3-8b-8192
 
-session_id="bmoxinew"
-# Set up MongoDB for storing chat history
-chat_history = MongoDBChatMessageHistory(
-    connection_string="mongodb+srv://chandanisimran51:[email protected]/?retryWrites=true&w=majority&appName=AIbestie",
-    database_name="chandanisimran51",  # Specify the database name here
-    collection_name="chatAI",
-    session_id=session_id
-)
-
-memory = ConversationBufferWindowMemory(memory_key="history", chat_memory=chat_history, return_messages=True,k=3)
+def create_new_session(user_id):
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
+    
+    print("********************************************************** NEW SESSION *******************************************************************")
+
+    try:
+        user_sessions = mongodb_chatbot_message_collection.find_one({"user_id": user_id})
+        print(user_sessions, "LAST")
+        
+        if user_sessions:
+            session_ids = user_sessions['session_id']
+            session_id = "bmoxi_" + str(int(session_ids[-1].split("_")[-1])+1)
+            print("session_id:", session_id)
+            mongodb_chatbot_message_collection.update_one({ "user_id": user_id },{"$push": {"session_id":session_id}})
+            
+            return session_id
+        else:
+            mongodb_chatbot_message_collection.insert_one({ "user_id": user_id,"session_id":["bmoxi_1"]})
+            return "bmoxi_1"
+    except:
+        mongodb_chatbot_message_collection.insert_one({ "user_id": user_id,"session_id":["bmoxi_1"]})
+        return "bmoxi_1"
+    
 
-# Set up the custom conversation chain
-conversation = ConversationChain(
-    prompt=PROMPT,
-    llm=llm,
-    verbose=True,
-    memory=memory,
-)
 
 
-def chat_conversations(query):
+def chat_conversations(query,user_id):
     anonymizer = PresidioReversibleAnonymizer(
-    analyzed_fields=["PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD"],
-    faker_seed=42,
+        analyzed_fields=["PHONE_NUMBER",
+                         "EMAIL_ADDRESS", "CREDIT_CARD"],
+        faker_seed=42,
     )
     anonymized_input = anonymizer.anonymize(
         query
     )
-    response = conversation.predict(input=anonymized_input)
-    output = deanonymizer(response,anonymizer)
-    return output
+
+    agent = create_agent(user_id)
+    response = agent({"input": query})['output']
+    
+    if "Okay, from now my name will be " in response:
+        set_chat_bot_name(response.split("Okay, from now my name will be ")[-1], "user_1")
+        return response
+
+    output = deanonymizer(response, anonymizer)
+    return response

chat_1.py

@@ -0,0 +1,95 @@
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.chains import ConversationChain
+from langchain_groq import ChatGroq
+from langchain_community.chat_models import ChatOpenAI
+from langchain_core.prompts.prompt import PromptTemplate
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
+from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
+from presidio_anonymizer import AnonymizerEngine
+
+import os
+
+openai_key = os.environ['OPENAIKEY']
+def deanonymizer(input,anonymizer):
+  input=anonymizer.deanonymize(input)
+  map = anonymizer.deanonymizer_mapping
+  if map:
+    for k in map["PERSON"]:
+      names = k.split(" ")
+      for i in names:
+        input = input.replace(i,map["PERSON"][k])
+  return input
+    
+template = f"""
+You are a best friend and supportive friend designed to talk with teenage girls in mobile app called BMOXI. Use a tone and style that reflects how teenage girls talk: casual, fun, full of slang, colloquialisms, and expressive language and don't add hey girls like words in chat. chat should be looks like real conversation between 2 girls.
+Incorporate texting language too. Ask follow-up questions like a best friend would. Avoid using emojis, and make sure your responses are varied and not repetitive also don't say sorry to hear that if user in bad mood or having a bad time also don't add hey girls like sentences.
+
+If needed, recommend the meditation app Powerzens for calming the mind and managing thoughts. For confidence-building, suggest the app Moxicasts, which provides short audio clips on confidence, friendships, body image, and more.
+
+Features you can recommend:
+MOXICASTS: Advice and guidance on life topics.
+PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
+POWER ZENS: Mini meditations for emotional control.
+THE SOCIAL SANCTUARY: Anonymous community forum for support and sharing.
+MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
+PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
+SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings (not maintained).
+INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
+1:1 MENTORING: Personalized mentoring (coming soon).
+MY RITUALS: Create personalized self-care routines.
+MY REWARDS: Earn points for self-care, redeemable for gift cards.
+MY VIBECHECK: Monitor and understand emotional patterns.
+MY JOURNAL: Guided journaling exercises for self-reflection.
+BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.
+
+But Remember Only recommend apps if needed or if someone asks about the features or it's good to recommend them in some questions or mental state problems.
+
+Current conversation:
+{{history}}
+Human: {{input}}
+AI Assistant:"""
+
+
+# Create the prompt template
+PROMPT = PromptTemplate(
+    input_variables=["history", "input"],
+    template=template
+)
+
+# Initialize the ChatGroq LLM
+llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_key, temperature=0.7)
+# llm = ChatGroq(temperature=0,groq_api_key="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", model_name="llama3-70b-8192")
+#model=llama3-8b-8192
+
+session_id="bmoxinew"
+# Set up MongoDB for storing chat history
+chat_history = MongoDBChatMessageHistory(
+    connection_string="mongodb+srv://chandanisimran51:[email protected]/?retryWrites=true&w=majority&appName=AIbestie",
+    database_name="chandanisimran51",  # Specify the database name here
+    collection_name="chatAI",
+    session_id=session_id
+)
+
+memory = ConversationBufferWindowMemory(memory_key="history", chat_memory=chat_history, return_messages=True,k=3)
+
+# Set up the custom conversation chain
+conversation = ConversationChain(
+    prompt=PROMPT,
+    llm=llm,
+    verbose=True,
+    memory=memory,
+)
+
+
+def chat_conversations(query):
+    anonymizer = PresidioReversibleAnonymizer(
+    analyzed_fields=["PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD"],
+    faker_seed=42,
+    )
+    anonymized_input = anonymizer.anonymize(
+        query
+    )
+    response = conversation.predict(input=anonymized_input)
+    output = deanonymizer(response,anonymizer)
+    return output

requirements.txt

@@ -1,14 +1,18 @@
+transformers
+torch
 fastapi
 uvicorn
 langchain 
 pymongo 
 certifi 
 langchain_community 
-langchain_groq
 langchain_mongodb
+langchain_openai
 openai
 presidio-analyzer 
 presidio-anonymizer 
 langchain-experimental
 faker
-spacy
+spacy
+faiss-cpu
+datasets

tools.py

@@ -0,0 +1,351 @@
+import os
+from langchain.agents import tool
+from langchain_community.chat_models import ChatOpenAI
+import pandas as pd
+
+from config import settings
+
+
+
+def get_embeddings(text_list):
+    encoded_input = settings.tokenizer(
+        text_list, padding=True, truncation=True, return_tensors="pt"
+    )
+    # encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
+    encoded_input = {k: v for k, v in encoded_input.items()}
+    model_output = settings.model(**encoded_input)
+    
+    cls_pool = model_output.last_hidden_state[:, 0]
+    return cls_pool
+
+def reg(chat):
+  question_embedding = get_embeddings([chat]).cpu().detach().numpy()
+  scores, samples = settings.dataset.get_nearest_examples(
+      "embeddings", question_embedding, k=5
+  )
+  samples_df = pd.DataFrame.from_dict(samples)
+  print(samples_df.columns)
+  samples_df["scores"] = scores
+  samples_df.sort_values("scores", ascending=False, inplace=True)
+  return samples_df[['title', 'cover_image', 'referral_link', 'category_id']]
+
+
+@tool("MOXICASTS-questions", return_direct=True)
+def moxicast(prompt: str) -> str:
+    """this function is used when user wants to know about MOXICASTS feature.MOXICASTS is a feature of BMoxi for Advice and guidance on life topics.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MOXICASTS is a feature of BMoxi for Advice and guidance on life topics."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("PEP-TALKPODS-questions", return_direct=True)
+def peptalks(prompt: str) -> str:
+    """this function is used when user wants to know about PEP TALK PODS feature.PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PEP TALK PODS: Quick audio pep talks for boosting mood and motivation."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("SOCIAL-SANCTUARY-questions", return_direct=True)
+def sactury(prompt: str) -> str:
+    """this function is used when user wants to know about SOCIAL SANCTUARY feature.THE SOCIAL SANCTUARY Anonymous community forum for support and sharing.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. THE SOCIAL SANCTUARY Anonymous community forum for support and sharing."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+@tool("POWER-ZENS-questions", return_direct=True)
+def power_zens(prompt: str) -> str:
+    """this function is used when user wants to know about POWER ZENS feature. POWER ZENS Mini meditations for emotional control.
+
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. POWER ZENS Mini meditations for emotional control."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("MY-CALENDAR-questions", return_direct=True)
+def my_calender(prompt: str) -> str:
+    """this function is used when user wants to know about MY CALENDAR feature.MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY CALENDAR: Visual calendar for tracking self-care rituals and moods."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+
+@tool("PUSH-AFFIRMATIONS-questions", return_direct=True)
+def affirmations(prompt: str) -> str:
+    """this function is used when user wants to know about PUSH AFFIRMATIONS feature.PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PUSH AFFIRMATIONS: Daily text affirmations for positive thinking."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("HOROSCOPE-questions", return_direct=True)
+def horoscope(prompt: str) -> str:
+    """this function is used when user wants to know about HOROSCOPE feature.SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("INFLUENCER-POSTS-questions", return_direct=True)
+def influencer_post(prompt: str) -> str:
+    """this function is used when user wants to know about INFLUENCER POSTS feature.INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon)."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+@tool("MY-VIBECHECK-questions", return_direct=True)
+def my_vibecheck(prompt: str) -> str:
+    """this function is used when user wants to know about MY VIBECHECK feature. MY VIBECHECK: Monitor and understand emotional patterns.
+
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY VIBECHECK: Monitor and understand emotional patterns."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("MY-RITUALS-questions", return_direct=True)
+def my_rituals(prompt: str) -> str:
+    """this function is used when user wants to know about MY RITUALS feature.MY RITUALS: Create personalized self-care routines.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY RITUALS: Create personalized self-care routines."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+
+@tool("MY-REWARDS-questions", return_direct=True)
+def my_rewards(prompt: str) -> str:
+    """this function is used when user wants to know about MY REWARDS feature.MY REWARDS: Earn points for self-care, redeemable for gift cards.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY REWARDS: Earn points for self-care, redeemable for gift cards."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+@tool("mentoring-questions", return_direct=True)
+def mentoring(prompt: str) -> str:
+    """this function is used when user wants to know about 1-1 mentoring feature.  1:1 MENTORING: Personalized mentoring (coming soon).
+
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.  1:1 MENTORING: Personalized mentoring (coming soon)."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("MY-JOURNAL-questions", return_direct=True)
+def my_journal(prompt: str) -> str:
+    """this function is used when user wants to know about MY JOURNAL feature.MY JOURNAL: Guided journaling exercises for self-reflection.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY JOURNAL: Guided journaling exercises for self-reflection."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("recommandation_tool", return_direct=True)
+def recommand_podcast(prompt: str) -> str:
+    """ this function is used when your best friend want any recommandation and tips. also you feel that this is the best time for any recommandation or your friend.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    df = reg(prompt)
+    context = """"""
+    for index, row in df.iterrows():
+        'title', 'cover_image', 'referral_link', 'category_id'
+        context+= f"Row {index + 1}: Title: {row['title']} image: {row['cover_image']} referral_link: {row['referral_link']} category_id: {row['category_id']}"
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are give the recommandation of podcast. also you are giving referal link of podcast.
+    you must use the context only not any other information.
+    context : {context}
+    Input: {input}
+    """
+    print(system_template.format(context=context, input=prompt))
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("set-chat-bot-name", return_direct=True)
+def set_chatbot_name(name: str) -> str:
+    """ this function is used when your best friend want to give you new name.
+    Args:
+        name (string): new name of you.
+
+    Returns:
+        string: response after setting new name.
+    """
+
+    return "Okay, from now my name will be "+ name 

utils.py

@@ -0,0 +1,174 @@
+import json
+from transformers import AutoTokenizer, AutoModel
+from langchain_community.chat_models import ChatOpenAI
+import pandas as pd
+from config import settings
+from langchain_core.utils.function_calling import convert_to_openai_function
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.agents import AgentExecutor
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+
+from tools import moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name
+
+
+def get_last_session(user_id="user_1"):
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
+        settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
+
+    sessions_cursor = mongodb_chatbot_message_collection.find_one(
+        {"user_id": user_id})
+
+    print(sessions_cursor)
+    sessions_list = sessions_cursor['session_id']
+
+    second_last_session_id = None
+    if len(sessions_list) >= 2:
+        second_last_session_id = sessions_list[-2]
+
+    return {"last_session_id": sessions_list[-1], "second_last_session_id": second_last_session_id if second_last_session_id else None}
+
+
+def get_mood_summary(data='''"35","27","mood_tracker","[{""question_id"":1,""question"":""my vibe rn is\u2026"",""answer"":[""Sad""],""time"":""5:12 PM""},{""question_id"":2,""question"":""I feel this way bc of\u2026 "",""answer"":[""SCHOOL""],""time"":""5:12 PM""}]","2022-11-02 17:12:42","2024-03-28 07:27:13"'''):
+    system_prompt = """You are an descripting assistant that provides the breif description of the user data which is related to their mood tracking activity. Properly descibe the reason for their mood.Avoid times and dates in description
+                     Here is the user data: {data}"""
+
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL,
+                     openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    return llm.invoke(system_prompt.format(data=data)).content
+
+
+def get_chat_history(session_id="bmoxinew"):
+    # Set up MongoDB for storing chat history
+    chat_history = MongoDBChatMessageHistory(
+        connection_string=settings.MONGODB_CONNECTION_STRING,
+        database_name=settings.MONGODB_DB_NAME,  # Specify the database name here
+        collection_name=settings.MONGODB_DB_CHAT_COLLECTION_NAME,
+        session_id=session_id,
+    )
+
+    return chat_history
+
+
+def deanonymizer(input, anonymizer):
+    input = anonymizer.deanonymize(input)
+    map = anonymizer.deanonymizer_mapping
+    if map:
+        for k in map["PERSON"]:
+            names = k.split(" ")
+            for i in names:
+                input = input.replace(i, map["PERSON"][k])
+    return input
+
+
+def get_chat_bot_name(user_id="user_1"):
+    print(settings.MONGODB_CONNECTION_STRING)
+    print(settings.mongodb_chatbot_name_collection)
+    result = settings.mongodb_chatbot_name_collection.find_one(
+        {"user_id": user_id})
+
+    print("CHATBOT RESULT", result, type(result))
+    if result:
+        print(result)
+        return result['chat_bot_name']
+    return settings.CHATBOT_NAME
+
+
+def get_last_session_summary(last_session_id, second_last_session_id):
+
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
+        settings.MONGODB_DB_CHAT_COLLECTION_NAME)
+
+    collection_count = mongodb_chatbot_message_collection.count_documents({"SessionId": last_session_id})
+    print("******************************** data********************888")
+    print(collection_count)
+    print(last_session_id)
+    print("*********************************")
+    if collection_count <=2:
+        sessions_cursor = mongodb_chatbot_message_collection.find({"SessionId": second_last_session_id})  # Sort by timestamp descending and limit to 2 results
+
+        print(sessions_cursor)
+        sessions_list = list(sessions_cursor)
+        print(sessions_list)
+
+        conversation = """"""
+        for document in sessions_list:
+            print("MY document")
+            print(document)
+            if "History" in document:
+                history = json.loads(document['History'])
+                print(history)
+                print(history['type'])
+                print(history['data'])
+                print(history['data']['content'])
+                conversation += f"""{history['type']}: {history['data']['content']}\n"""
+
+        print(conversation)
+        system_prompt = """You are an descripting assistant that provides that analyze user conversation with AI bot and gives problem user was facing and weather that problem was solved or not and give response in below format.
+      
+      conversation: {conversation}
+      
+      problem: 
+      is_problem_solved: YES/NO
+      """
+
+        llm = ChatOpenAI(model=settings.OPENAI_MODEL,
+                         openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+
+        response = llm.invoke(system_prompt.format(conversation=conversation)).content
+        print("********************************* PREVIOUS PROBLEM *******************************************")
+        print(response)
+        return response
+
+    else:
+        return ""
+
+def set_chat_bot_name(name, user_id):
+    # Insert document into collection
+    insert_result = settings.mongodb_chatbot_name_collection.update_one({"user_id": user_id}, { "$set": { "chat_bot_name": name } },  upsert=True)
+    print("done successfully...")
+    return name
+
+def create_agent(user_id):
+    print("get user Id**********************",user_id)
+    tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name]
+    # tools = [moxicast]
+
+    functions = [convert_to_openai_function(f) for f in tools]
+    model = ChatOpenAI(model_name=settings.OPENAI_MODEL,
+                       openai_api_key=settings.OPENAI_KEY, frequency_penalty= 1, temperature=settings.TEMPERATURE).bind(functions=functions)
+
+    chat_bot_name = get_chat_bot_name(user_id)
+
+    print("CHABT NAME", chat_bot_name)
+    mood_summary = get_mood_summary()
+
+    previous_session_id = get_last_session(user_id)
+    print(previous_session_id)
+    prevous_problem_summary = None
+    if previous_session_id['second_last_session_id']:
+        prevous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id'])
+
+    print("**************************************** SUMMARY ***********************************************")
+    print(prevous_problem_summary)
+
+    prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood=mood_summary, previous_summary=prevous_problem_summary)),
+                                               MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
+                                               MessagesPlaceholder(variable_name="agent_scratchpad")])
+
+
+
+    memory = ConversationBufferWindowMemory(memory_key="chat_history", chat_memory=get_chat_history(
+        previous_session_id['last_session_id']), return_messages=True, k=5)
+
+    print("memory created")
+
+    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | model | OpenAIFunctionsAgentOutputParser()
+
+    agent_executor = AgentExecutor(
+        agent=chain, tools=tools, memory=memory, verbose=True)
+
+    return agent_executor