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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="π£οΈTalkToMyShakespeareπ", | |
description="π£οΈTalkToMyShakespeareπ is a tool that allows you to ask questions about Shakespeare literature work.", | |
).launch() |