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import os
from typing import Optional, Tuple
import cfg
import gradio as gr
import pandas as pd
from cfg import setup_buster
from buster.completers import Completion
from buster.utils import extract_zip
# Check if an openai key is set as an env. variable
if os.getenv("OPENAI_API_KEY") is None:
print("Warning: No openai key detected. You can set it with 'export OPENAI_API_KEY=sk-...'.")
# Typehint for chatbot history
ChatHistory = list[list[Optional[str], Optional[str]]]
extract_zip("deeplake_store.zip", "deeplake_store")
buster = setup_buster(cfg.buster_cfg)
def add_user_question(user_question: str, chat_history: Optional[ChatHistory] = None) -> ChatHistory:
"""Adds a user's question to the chat history.
If no history is provided, the first element of the history will be the user conversation.
"""
if chat_history is None:
chat_history = []
chat_history.append([user_question, None])
return chat_history
# def format_sources(matched_documents: pd.DataFrame) -> str:
# if len(matched_documents) == 0:
# return ""
# matched_documents.similarity_to_answer = matched_documents.similarity_to_answer * 100
# # drop duplicate pages (by title), keep highest ranking ones
# matched_documents = matched_documents.sort_values("similarity_to_answer", ascending=False).drop_duplicates(
# "title", keep="first"
# )
# documents_answer_template: str = (
# "📝 Here are the sources I used to answer your question:\n\n{documents}\n\n{footnote}"
# )
# document_template: str = "[🦜 {document.title}], relevance: {document.similarity_to_answer:2.1f} %"
# documents = "\n".join([document_template.format(document=document) for _, document in matched_documents.iterrows()])
# footnote: str = "I'm a bot 🤖 and not always perfect and I'm still learning."
# return documents_answer_template.format(documents=documents, footnote=footnote)
# def add_sources(history, completion):
# if completion is None:
# return history
# if completion.answer_relevant:
# formatted_sources = format_sources(completion.matched_documents)
# history.append([None, formatted_sources])
# return history
def chat(chat_history: ChatHistory) -> Tuple[ChatHistory, Completion]:
"""Answer a user's question using retrieval augmented generation."""
# We assume that the question is the user's last interaction
user_input = chat_history[-1][0]
# Do retrieval + augmented generation with buster
completion = buster.process_input(user_input)
# Stream tokens one at a time to the user
chat_history[-1][1] = ""
for token in completion.answer_generator:
chat_history[-1][1] += token
yield chat_history, completion
demo = gr.Blocks()
with demo:
with gr.Row():
gr.Markdown("<h3><center>Parrot 🦜: A Question-Answering Bot for 🫎 MOOSE documentation </center></h3>")
chatbot = gr.Chatbot()
with gr.Row():
question_textbox = gr.Textbox(
label="What's your question?",
placeholder="Type your question here...",
lines=1,
)
send_button = gr.Button(value="Send", variant="secondary")
examples = gr.Examples(
examples=[
"How can I use PorousFlow?",
"How do I use displaced mesh?",
"Why is my application not converging?",
],
inputs=question_textbox,
)
gr.Markdown("This application uses GPT to search the docs for relevant info and answer questions.")
gr.Markdown("Find the full MOOSE documentation [here](https://mooseframework.inl.gov/index.html).")
gr.HTML("️<center> Created with @aikubo")
response = gr.State()
# fmt: off
gr.on(
triggers=[send_button.click, question_textbox.submit],
fn=add_user_question,
inputs=[question_textbox],
outputs=[chatbot]
).then(
chat,
inputs=[chatbot],
outputs=[chatbot, response]
)
# fmt: on
demo.queue()
demo.launch(debug=True, share=True)
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