tools.py

import os
from langchain.agents import tool
from langchain_community.chat_models import ChatOpenAI
import pandas as pd
from langchain_core.utils.function_calling import convert_to_openai_function
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.prompts import ChatPromptTemplate, MessagesPlaceholder
from config import settings
from database_functions import set_recommendation_count,get_recommendation_count

MEMORY = None
SESSION_ID= ""

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", )
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", )
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", )
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", )
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", )
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", )
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", )
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", )
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", )
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", )
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", )
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")
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", )
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("podcast-recommendation-tool")
def recommand_podcast(prompt: str) -> str:
    """  must used when user wants to any resources only.
    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 have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast. give 3-4 podcast only.
    you must use the context only not any other information.
    context : {context}
    """
    # print(system_template.format(context=context, input=prompt))
    response = llm.invoke(system_template.format(context=context, input=prompt))
    set_recommendation_count(SESSION_ID)
    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 

@tool("clossing-chat",return_direct=True)
def close_chat(summary:str)-> str:
    """  must use this tool when user closing the conversation. must use this tool when you are ending the conversation.
    Args:
        summary (str): summary of whole chat with your friend. must use I for yourself and for user use my friend.

    Returns:
        str: closing chat statements.
    """
    
    print('close tool starts')

    global SESSION_ID
    tools = []
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    if (not get_recommendation_count(SESSION_ID)):
        tools.extend([moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post,recommand_podcast])
    
    if len(tools)!=0:
        functions = [convert_to_openai_function(f) for f in tools]
        llm = llm.bind(functions= functions)
    print('llm is created')
    
    system_template = """"""
    if len(tools)!=0:
        system_template += """ context: there is one typical conversation going on between two high school gen z girls.
        you are one of the high school gen z girl. your voice is edgy and raw. 
        now you are going to close the conversation.
        summary of conversation is given to you.
        summary: {summary}
        give recommendation of any podcast or app feature based on summary and close the conversation in short.
        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
        """
    else:
        system_template+= """ context: there is one typical conversation going on between two high school gen z girls.
        you are one of the high school gen z girl. your voice is edgy and raw. 
        now you are going to close the conversation.
        summary of conversation is given to you.
        summary: {summary}
        now just end the conversation in 1 sentense in short.
        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
        """


    prompt = ChatPromptTemplate.from_messages([("system", system_template.format(summary = summary)),MessagesPlaceholder(variable_name="agent_scratchpad")])
    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt |llm | OpenAIFunctionsAgentOutputParser()
    print('chain is rolling')
    agent = AgentExecutor(agent=chain, tools=tools, memory=MEMORY, verbose=True)
    # Define the system prompt
                      
    print('agent is created')
    # print(system_template.format(context=context, input=prompt))\

    response = agent.invoke({})['output']
    return response


@tool("App-Fetures")
def app_features(summary:str)-> str:
    """ must use For any app features details.

    Args:
        summary (str): summary of whole chat with your friend.

    Returns:
        str: closing chat statements.
    """
    
    print('app feature tool starts')
    system_template = """ you have given one summary of chat. 
    summary : {summary}.
    using this summary give appropriate features suggestions using tools. if you don't find any tool appropriate to summary ask question only.
    # make all responses short.
    """
           
    tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post]
    functions = [convert_to_openai_function(f) for f in tools]
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7).bind(functions=functions)
    print('llm is created')
    
    prompt = ChatPromptTemplate.from_messages([("system", system_template.format(summary = summary)),MessagesPlaceholder(variable_name="agent_scratchpad")])
    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt |llm | OpenAIFunctionsAgentOutputParser()
    print('chain is rolling')
    agent = AgentExecutor(agent=chain, tools=tools, memory=MEMORY, verbose=True)
    # Define the system prompt
                      
    print('agent is created')
    # print(system_template.format(context=context, input=prompt))\
    set_recommendation_count(SESSION_ID)
    response = agent.invoke({})['output']
    return response

# close_chat('Suggest a podcast or self-care tool for someone looking to unwind after a hectic day at work.')



@tool("Joke-teller", )
def joke_teller(summary: str) -> str:
    """If user needs mood boost and when you feel to lighten the environment use this tool to tell the jokes.
     Args:
        summary (str): summary of whole chat with your friend.

    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 = """  summary : {summary}.
    you are given summary of current chat. make one joke for your friend. to boost her mood.
    # make all responses short.
    """
    response = llm.invoke(system_template.format(summary=summary))

    return response.content