from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
from transformers import AutoTokenizer
import pickle
import os
import shutil

with open('shakespeare.pkl', 'rb') as fp:
    data = pickle.load(fp)

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()

persist_directory = "vector_db"

vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory)

vectordb.persist()
vectordb = None

vectordb_persist = Chroma(persist_directory=persist_directory, embedding_function=embeddings)

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

doc_retriever = vectordb_persist.as_retriever()

shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)

def make_inference(query):
    inference = shakespeare_qa.run(query)
    return inference

if __name__ == "__main__":
    # make a gradio interface
    import gradio as gr

    gr.Interface(
        make_inference,
        gr.inputs.Textbox(lines=2, label="Query"),
        gr.outputs.Textbox(label="Response"),
        title="Ask_Shakespeare",
        description="️building_w_llms_qa_Shakespeare allows you to inquire about the Shakespeare's plays.",
    ).launch()