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Update app.py
8128f41
import os
import shutil
from glob import glob
from transformers import AutoTokenizer
from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import BSHTMLLoader, DirectoryLoader
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()
persist_directory = "vector_db"
vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory)
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
vectordb.persist()
vectordb = None
vectordb = 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.as_retriever()
shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
def make_inference(query):
# docs = docsearch.get_relevant_documents(query)
# return(chain.run(input_documents=docs, question=query))
return(shakespeare_qa.run(query))
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="πŸ—£οΈTalkToMyShakespeareπŸ“„",
description="πŸ—£οΈTalkToMyShakespeareπŸ“„ is a tool that allows you to ask questions about Shakespeare literature work.",
).launch()