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

import gradio as gr

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)

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):
    return shakespeare_qa.run(query)

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