Spaces:
Sleeping
Sleeping
import gradio as gr | |
from langchain.document_loaders import OnlinePDFLoader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.llms import HuggingFaceHub | |
from langchain.embeddings import HuggingFaceHubEmbeddings | |
from langchain.vectorstores import Chroma | |
from langchain.chains import RetrievalQA | |
def loading_pdf(): | |
return "Loading..." | |
def pdf_changes(pdf_doc, repo_id): | |
loader = OnlinePDFLoader(pdf_doc.name) | |
documents = loader.load() | |
text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0) | |
texts = text_splitter.split_documents(documents) | |
embeddings = HuggingFaceHubEmbeddings() | |
db = Chroma.from_documents(texts, embeddings) | |
retriever = db.as_retriever() | |
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":250}) | |
global qa | |
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True) | |
return "Ready" | |
def add_text(history, text): | |
history = history + [(text, None)] | |
return history, "" | |
def bot(history): | |
response = infer(history[-1][0]) | |
history[-1][1] = response['result'] | |
return history | |
def infer(question): | |
query = question | |
result = qa({"query": query}) | |
return result | |
css=""" | |
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;} | |
""" | |
title = """ | |
<div style="text-align: center;max-width: 700px;"> | |
<h1>Chat with PDF</h1> | |
<p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br /> | |
when everything is ready, you can start asking questions about the pdf ;)</p> | |
</div> | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.HTML(title) | |
with gr.Column(): | |
pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") | |
repo_id = gr.Dropdown(label="LLM", choices=["google/flan-ul2", "OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "bigscience/bloomz"], value="google/flan-ul2") | |
with gr.Row(): | |
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) | |
load_pdf = gr.Button("Load pdf to langchain") | |
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) | |
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") | |
submit_btn = gr.Button("Send message") | |
#load_pdf.click(loading_pdf, None, langchain_status, queue=False) | |
repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) | |
load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) | |
question.submit(add_text, [chatbot, question], [chatbot, question]).then( | |
bot, chatbot, chatbot | |
) | |
submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( | |
bot, chatbot, chatbot | |
) | |
demo.launch() |