app.py

from datasets import load_dataset
from datasets import Dataset
from langchain.docstore.document import Document as LangchainDocument
from sentence_transformers import SentenceTransformer
from langchain_community.document_loaders import WebBaseLoader


from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from sentence_transformers import SentenceTransformer
from huggingface_hub import Repository, upload_file
from datasets import Dataset
import os

HF_TOKEN = os.getenv('HF_Token')

url = "https://oxyjon.com/blog/"
loader = WebBaseLoader(url)
document = loader.load()

def create_vector_db():

    # split the document into chunks
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=50)
    texts = text_splitter.split_documents(document)
    print(texts[1])
    print(texts[3])
    print(texts[17])

    df = pd.DataFrame(texts)
    
    column_headers = list(df.columns.values)
    print(column_headers)
    pd.options.display.max_colwidth = 300
    print(df.iloc[[3]])
    dataset = Dataset.from_pandas(df)
    print("check2b")
    print(dataset[3])
    dataset.push_to_hub("Namitg02/Test",token = HF_TOKEN)


if __name__ == "__main__":
    print("check31")
    create_vector_db()