from hazm import Normalizer
import requests
import numpy as np
from dotenv import load_dotenv
import os

load_dotenv()


class TextEmbedder:
    def __init__(self, model_name="BAAI/bge-m3"):
        self.model_name = model_name
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {os.getenv('EMBEDDING_PASS')}"}
        self.normalizer = Normalizer()
    
    def preprocess_embedder(self, text:str):
        text = text.replace("\n", ".")
        text = self.normalizer.normalize(text)
        
        return text
    

    def embed_texts(self, texts:list[str])->list[list[float]]:
        """
        Embed texts using the model.
        """
        if texts == []:
            return []
            
        texts = [self.preprocess_embedder(text) for text in texts]
        
        payload = {
            "model": self.model_name,
            "input": texts
        }
        responses = requests.post("http://78.38.161.78:3094/v1/embeddings", headers=self.headers, json=payload)
        embeddings = [np.array(response["embedding"], dtype=np.float32) for response in responses.json()["data"]]
        
        return embeddings