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
from langchain.retrievers import WikipediaRetriever
import pickle
import os

retriever = WikipediaRetriever(lang="en")

data = retriever.get_relevant_documents(query="Economics")

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()

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

def make_inference(query):
    inference = wikipedia_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_Wikipedia about Economics",
        description="️Building a QA application to Wikipedia",
    ).launch()