app.py

from glob import glob
from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.document_loaders import BSHTMLLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from transformers import AutoTokenizer

files = glob("./shakespeare/**/*.html")

import shutil
import os

os.mkdir('./data')

destination_folder = './data/'

for html_file in files:
      shutil.move(html_file, destination_folder + html_file.split("/")[-1])
bshtml_dir_loader = DirectoryLoader('./data/', loader_cls=BSHTMLLoader)

data = bshtml_dir_loader.load()

bloomz_tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-1b7")

text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(bloomz_tokenizer,
                                                   chunk_size=100,
                                                   chunk_overlap=0,
                                                   separator="\n")
documents = text_splitter.split_documents(data)

print(documents)

embeddings = HuggingFaceEmbeddings()

llm = HuggingFacePipeline.from_model_id(
        model_id="bigscience/bloomz-1b7",
        task="text-generation",
        model_kwargs={"temperature" : 0, "max_length" : 500})

vectordb = Chroma.from_documents(documents=documents, embedding=embeddings)
doc_retriever = vectordb.as_retriever()
shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)

def query(query):
    shakespeare_qa.run(query)

iface = gr.Interface(fn=query, inputs="text", outputs="text")
iface.launch()