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import sqlite3

from transformers import MarianMTModel, MarianTokenizer

from langchain.embeddings import OpenAIEmbeddings  # Replace if using a different embedding model

from langchain.vectorstores import FAISS         # Replace if using a different vectorstore

class Config:

    def __init__(self):

        # 1. Load Offline Translation Models

        self.tokenizers, self.models = self._load_translation_models()

        # 2. Load Embedded Document Collection

        self.vectorstore = self._load_document_collection()

        # 3. Initialize Database Connection

        self.conn, self.cursor = self._initialize_database()

        # 4. Initialize Cache

        self.cache = {}  # Simple in-memory cache, replace with your preferred implementation

        # 5. Email Registry

        self.email_registry = ["user1@example.com", "user2@example.com"]  # Replace with your actual allowed emails

    def _load_translation_models(self):

        # Define language codes and model names (replace with your actual models)

        language_pairs = {

            'es': ('Helsinki-NLP/opus-mt-es-en', 'Helsinki-NLP/opus-mt-en-es'),

            'fr': ('Helsinki-NLP/opus-mt-fr-en', 'Helsinki-NLP/opus-mt-en-fr'),

            'de': ('Helsinki-NLP/opus-mt-de-en', 'Helsinki-NLP/opus-mt-en-de'),

            'th': ('Helsinki-NLP/opus-mt-th-en', 'Helsinki-NLP/opus-mt-en-th'),

            'ru': ('Helsinki-NLP/opus-mt-ru-en', 'Helsinki-NLP/opus-mt-en-ru'),

            'ar': ('Helsinki-NLP/opus-mt-ar-en', 'Helsinki-NLP/opus-mt-en-ar'),

            'pt': ('Helsinki-NLP/opus-mt-pt-en', 'Helsinki-NLP/opus-mt-en-pt'),

            'zh': ('Helsinki-NLP/opus-mt-zh-en', 'Helsinki-NLP/opus-mt-en-zh'),

        }

        # Load tokenizers and models

        tokenizers = {}

        models = {}

        for lang_code, (to_en_model_name, from_en_model_name) in language_pairs.items():

            tokenizers[lang_code] = {

                'to_en': MarianTokenizer.from_pretrained(to_en_model_name),

                'from_en': MarianTokenizer.from_pretrained(from_en_model_name)

            }

            models[lang_code] = {

                'to_en': MarianMTModel.from_pretrained(to_en_model_name),

                'from_en': MarianMTModel.from_pretrained(from_en_model_name)

            }

        return tokenizers, models

    def _load_document_collection(self):

        # Load your local embedded document collection (replace with your actual implementation)

        return FAISS.load_local("my_faiss_index", OpenAIEmbeddings())

    def _initialize_database(self):

        conn = sqlite3.connect('email_interactions.db')

        cursor = conn.cursor()

        # Create the email_interactions table (run this once)

        cursor.execute('''

            CREATE TABLE IF NOT EXISTS email_interactions (

                uid TEXT PRIMARY KEY,

                sender_email TEXT,

                timestamp DATETIME,

                question TEXT,

                answer TEXT,

                status TEXT,

                other_metadata TEXT

            )

        ''')

        conn.commit()

        return conn, cursor

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