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import gradio as gr
from langchain.document_loaders import OnlinePDFLoader
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)
from langchain.llms import HuggingFaceHub
flan_ul2 = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":300})
from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings()
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
def pdf_changes(pdf_doc):
loader = OnlinePDFLoader(pdf_doc.name)
documents = loader.load()
texts = text_splitter.split_documents(documents)
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
global qa
qa = RetrievalQA.from_chain_type(llm=flan_ul2, 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() as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Row():
with gr.Column():
pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
langchain_status = gr.Textbox()
load_pdf = gr.Button("Load pdf to langchain")
with gr.Column():
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
with gr.Row():
question = gr.Textbox(label="Question")
clear = gr.Button("Clear")
load_pdf.click(pdf_changes, pdf_doc, langchain_status, queue=False)
question.submit(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch() |