from typing import Any, Dict, List, Optional
from supabase import create_client, Client
from loguru import logger

from .utils import timing_decorator

class SupabaseClient:
    def __init__(self, url: str, key: str):
        """
        Khởi tạo SupabaseClient với url và key.
        Input: url (str), key (str)
        Output: SupabaseClient instance.
        """
        self.client: Client = create_client(url, key)

    @timing_decorator
    def get_page_token(self, page_id: str):
        """
        Lấy access token của Facebook page từ Supabase.
        Input: page_id (str)
        Output: access_token (str) hoặc None nếu không có.
        """
        try:
            response = self.client.table('page_tokens').select('access_token').eq('page_id', page_id).execute()
            if response.data and len(response.data) > 0:
                return response.data[0]['access_token']
            return None
        except Exception as e:
            logger.error(f"Error getting page token: {e}")
            return None

    @timing_decorator
    def match_documents(self, embedding: List[float], match_count: int = 5):
        """
        Truy vấn vector similarity search qua RPC match_documents.
        Input: embedding (list[float]), match_count (int)
        Output: list[dict] kết quả truy vấn.
        """
        try:
            response = self.client.rpc(
                'match_documents',
                {
                    'query_embedding': embedding,
                    'match_threshold': 0.7,
                    'match_count': match_count
                }
            ).execute()

            if response.data:
                return response.data
            return []
        except Exception as e:
            logger.error(f"Error matching documents: {e}")
            return []

    @timing_decorator
    def store_embedding(self, text: str, embedding: List[float], metadata: Dict[str, Any]):
        """
        Lưu embedding vào Supabase.
        Input: text (str), embedding (list[float]), metadata (dict)
        Output: bool (True nếu thành công, False nếu lỗi)
        """
        try:
            response = self.client.table('embeddings').insert({
                'content': text,
                'embedding': embedding,
                'metadata': metadata
            }).execute()
            
            return bool(response.data)
        except Exception as e:
            logger.error(f"Error storing embedding: {e}")
            return False