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 have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast.
    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))

    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