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import gradio as gr | |
import os | |
import time | |
from langchain.document_loaders import OnlinePDFLoader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.llms import OpenAI | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.vectorstores import Chroma | |
from langchain.chains import ConversationalRetrievalChain | |
def loading_pdf(): | |
return "Loading..." | |
def pdf_changes(pdf_doc, open_ai_key): | |
if openai_key is not None: | |
os.environ['OPENAI_API_KEY'] = open_ai_key | |
loader = OnlinePDFLoader(pdf_doc.name) | |
documents = loader.load() | |
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
texts = text_splitter.split_documents(documents) | |
embeddings = OpenAIEmbeddings() | |
db = Chroma.from_documents(texts, embeddings) | |
retriever = db.as_retriever() | |
global qa | |
qa = ConversationalRetrievalChain.from_llm( | |
llm=OpenAI(temperature=0.5), | |
retriever=retriever, | |
return_source_documents=False) | |
return "Ready" | |
else: | |
return "You forgot OpenAI API key" | |
def add_text(history, text): | |
history = history + [(text, None)] | |
return history, "" | |
def bot(history): | |
response = infer(history[-1][0], history) | |
history[-1][1] = "" | |
for character in response: | |
history[-1][1] += character | |
time.sleep(0.05) | |
yield history | |
def infer(question, history): | |
res = [] | |
for human, ai in history[:-1]: | |
pair = (human, ai) | |
res.append(pair) | |
chat_history = res | |
#print(chat_history) | |
query = question | |
result = qa({"question": query, "chat_history": chat_history}) | |
#print(result) | |
return result["answer"] | |
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 • OpenAI</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 ;) <br /> | |
This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key</p> | |
</div> | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.HTML(title) | |
with gr.Column(): | |
openai_key = gr.Textbox(label="You OpenAI API key", type="password") | |
pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") | |
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) | |
load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], 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() |