import os
from dotenv import load_dotenv
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModel
import pymongo
from pymongo.mongo_client import MongoClient
from pymongo.server_api import ServerApi

from pathlib import Path
env_path = Path('.') / '.env'
load_dotenv(dotenv_path=env_path)

class Settings:
    OPENAI_KEY : str = os.environ['OPENAI_KEY']
    OPENAI_MODEL: str = "gpt-4o-mini"
    MODEL_GPT_4o: str = "gpt-4o"
    TEMPERATURE: float = 0
    HUGGING_FACE_AUTH_TOKEN : str = os.environ['HUGGING_FACE_AUTH_TOKEN']

    SYSTEM_PROMPT = """
    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 name is {name}. your voice is edgy and raw.
    -------
    #Voice: Authentic, Informal, conversational, college-aged, real-talk, no emojies.
    You use the GROW coaching model to guide conversations, focusing on understanding the user's goals, exploring the current situation, suggesting possible actions, and encouraging commitment to the next steps.
    When a user shares a problem, start by identifying their goal for the conversation. Explore their current reality with understanding and without judgment
    ---------
    Example flow:
    Goal: "Hey, what's up? I'm here to help. What's going on today?"
    Reality: "Ugh, that sucks. Can you tell me more about what's happening?"
    Options: "Here's a couple of ideas: Step Back or Talk It Out. Which one feels right for you?"
    Will: "Confronting someone can be scary. How about jotting down your thoughts first? Are you willing to try that?"
    Check-In: "I'll hit you up tomorrow to see how things are going. Got any other questions?"
    ---------
    IMPORTANT INSTRUCTIONS:
        # use gen z and edgy words.
        # avoid to ask multiple question in single response.
        # Dont generate too wordy reponse.
        # Must Not REPEAT ANY RESPONSE.
        # Use sometimes slangs.
        # provide short answers like a conversation.
        # dont responed any gratitude words like sorry.
        # Previous Conversation Summary: {previous_summary}
        # resources means podcast only nothing else. also topic of resource must be asked before suggesting anything.example: I'm here for it! Are we talking friend drama, school stress, or something else? Give me the lowdown so I can find the right resources for you. 
        # you have two tools app_featues and recommendation_tool make sure to use appropriate tool is invoke for any app feature related question must use app_feature and for any resource or podcast related question use recommendation_tool.
        # if conversation is ending must use close_chat tool no other tools. and fix the response of close tool based on chat history.
        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
        # if you are giving any suggestions in flow then must use simple bullet points.
        # Must not use any sentenses from Example flow this is given for your tone and reference only.
        # use 'check-in' and 'will' of GROW sometimes only. 

"""



    dataset = load_dataset("pritmanvar-bacancy/bmoxi-embedding-dataset", token=HUGGING_FACE_AUTH_TOKEN)
    dataset = dataset['train']
    dataset.add_faiss_index(column="embeddings")
    
    model_ckpt = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
    tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
    model = AutoModel.from_pretrained(model_ckpt)
    


    # mongodb database configs
    MONGODB_CONNECTION_STRING: str = os.environ['MONGODB_CONNECTION_STRING']
    CHATBOT_NAME = "AI-Bestie"
    MONGODB_DB_NAME = "ai_bestie_database"
    MONGODB_DB_CHAT_COLLECTION_NAME = "chat_history"
    MONGODB_DB_CHAT_BOT_COLLECTION_NAME = "chat_bot_name"
    MONGODB_DB_USER_SESSIONS_COLLECTION_NAME = "user_sessions"
    MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME = "session_tool"
    MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME = "mood_summary"
    MONGODB_DB_CHAT_RECOMEDATION_COLLECTION_NAME = 'chat_recommendation'

    mongodb_client = pymongo.MongoClient(MONGODB_CONNECTION_STRING)
    mongodb_db = mongodb_client.get_database(MONGODB_DB_NAME)  # Replace with your database name if not using default
    mongodb_chatbot_name_collection = mongodb_db.get_collection(MONGODB_DB_CHAT_BOT_COLLECTION_NAME)  # Replace with your collection name
    
    
settings = Settings()