import glob
import os
from langchain.text_splitter import RecursiveCharacterTextSplitter, SentenceTransformersTokenTextSplitter
from transformers import AutoTokenizer
from torch import cuda
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import Qdrant
from auditqa.reports import files, report_list
device = 'cuda' if cuda.is_available() else 'cpu'
#from dotenv import load_dotenv
#load_dotenv()

#HF_token = os.environ["HF_TOKEN"]
path_to_data = "./data/pdf/"

def process_pdf():
    docs = {}
    for file in report_list:
        try:
            docs[file] = PyMuPDFLoader(path_to_data + file + '.pdf').load()
        except Exception as e:
            print("Exception: ", e)

    # text splitter based on the tokenizer of a model of your choosing
    # to make texts fit exactly a transformer's context window size
    # langchain text splitters: https://python.langchain.com/docs/modules/data_connection/document_transformers/
    chunk_size = 256
    text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
            AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5"),
            chunk_size=chunk_size,
            chunk_overlap=10,
            add_start_index=True,
            strip_whitespace=True,
            separators=["\n\n", "\n"],
    )
    all_documents = {}
    categories = list(files.keys())
    for category in categories:
        all_documents[category] = {}

    print(all_documents)