import os
import pickle
import tempfile
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter


class Embedder_txt:
    def __init__(self):
        self.PATH = "embeddings"
        self.createEmbeddingsDir()

    def createEmbeddingsDir(self):
        """
        Creates a directory to store the embeddings vectors
        """
        if not os.path.exists(self.PATH):
            os.mkdir(self.PATH)

    def storeDocEmbeds(self, file, filename):
        """
        Stores document embeddings using Langchain and FAISS
        """
        # Write the uploaded file to a temporary file
        with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tmp_file:
            tmp_file.write(file)
            tmp_file_path = tmp_file.name

        # Load the data from the file using Langchain
        # loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
        documents = []
        loader = TextLoader(file_path=tmp_file_path)
        documents.extend(loader.load())
        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
        data = loader.load_and_split()
        texts = text_splitter.split_documents(documents)

        # Create an embeddings object using Langchain
        embeddings = OpenAIEmbeddings()

        # Store the embeddings vectors using FAISS
        vectors = FAISS.from_documents(texts, embeddings)
        os.remove(tmp_file_path)

        # Save the vectors to a pickle file
        with open(f"{self.PATH}/{filename}.pkl", "wb") as f:
            pickle.dump(vectors, f)

    def getDocEmbeds(self, file, filename):
        """
        Retrieves document embeddings
        """
        # Check if embeddings vectors have already been stored in a pickle file
        if not os.path.isfile(f"{self.PATH}/{filename}.pkl"):
            # If not, store the vectors using the storeDocEmbeds function
            self.storeDocEmbeds(file, filename)

        # Load the vectors from the pickle file
        with open(f"{self.PATH}/{filename}.pkl", "rb") as f:
            vectors = pickle.load(f)

        return vectors

class Embedder:
    def __init__(self):
        self.PATH = "embeddings"
        self.createEmbeddingsDir()

    def createEmbeddingsDir(self):
        """
        Creates a directory to store the embeddings vectors
        """
        if not os.path.exists(self.PATH):
            os.mkdir(self.PATH)

    def storeDocEmbeds(self, file, filename):
        """
        Stores document embeddings using Langchain and FAISS
        """
        # Write the uploaded file to a temporary file
        with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tmp_file:
            tmp_file.write(file)
            tmp_file_path = tmp_file.name

        # Load the data from the file using Langchain
        loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
        data = loader.load_and_split()

        # Create an embeddings object using Langchain
        embeddings = OpenAIEmbeddings()

        # Store the embeddings vectors using FAISS
        vectors = FAISS.from_documents(data, embeddings)
        os.remove(tmp_file_path)

        # Save the vectors to a pickle file
        with open(f"{self.PATH}/{filename}.pkl", "wb") as f:
            pickle.dump(vectors, f)

    def getDocEmbeds(self, file, filename):
        """
        Retrieves document embeddings
        """
        # Check if embeddings vectors have already been stored in a pickle file
        if not os.path.isfile(f"{self.PATH}/{filename}.pkl"):
            # If not, store the vectors using the storeDocEmbeds function
            self.storeDocEmbeds(file, filename)

        # Load the vectors from the pickle file
        with open(f"{self.PATH}/{filename}.pkl", "rb") as f:
            vectors = pickle.load(f)

        return vectors