|
import cfg |
|
import gradio as gr |
|
import pandas as pd |
|
from cfg import setup_buster |
|
|
|
buster = setup_buster(cfg.buster_cfg) |
|
|
|
|
|
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 |
|
) |
|
|
|
|
|
matched_documents["page"] = matched_documents.apply( |
|
lambda x: x.url.split("/")[-1], axis=1 |
|
) |
|
|
|
documents_answer_template: str = "📝 Here are the sources I used to answer your question:\n\n{documents}\n\n{footnote}" |
|
document_template: str = "[🔗 {document.page}]({document.url}), 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." |
|
|
|
return documents_answer_template.format(documents=documents, footnote=footnote) |
|
|
|
|
|
def add_sources(history, completion): |
|
if completion.answer_relevant: |
|
formatted_sources = format_sources(completion.matched_documents) |
|
history.append([None, formatted_sources]) |
|
|
|
return history |
|
|
|
|
|
def user(user_input, history): |
|
"""Adds user's question immediately to the chat.""" |
|
return "", history + [[user_input, None]] |
|
|
|
|
|
def chat(history): |
|
user_input = history[-1][0] |
|
|
|
completion = buster.process_input(user_input) |
|
print(completion) |
|
|
|
history[-1][1] = "" |
|
|
|
for token in completion.answer_generator: |
|
history[-1][1] += token |
|
|
|
yield history, completion |
|
|
|
|
|
block = gr.Blocks() |
|
|
|
with block: |
|
gr.Markdown( |
|
"""<h1><center>Buster 🤖: A Question-Answering Bot for your documentation</center></h1>""" |
|
) |
|
gr.Markdown( |
|
""" |
|
## Welcome to Buster! |
|
This chatbot is designed to answer any questions related to the [huggingface transformers](https://huggingface.co/docs/transformers/index) library. |
|
It uses ChatGPT + embeddings to search the docs for relevant sections and uses them to answer questions. It can then cite its sources back to you to verify the information. |
|
Note that LLMs are prone to hallucination, so all outputs should always be vetted by users. |
|
|
|
#### The Code is open-sourced and available on [Github](www.github.com/jerpint/buster) |
|
""" |
|
) |
|
|
|
chatbot = gr.Chatbot() |
|
|
|
with gr.Row(): |
|
with gr.Column(scale=4): |
|
question = gr.Textbox( |
|
label="What's your question?", |
|
placeholder="Ask a question to AI stackoverflow here...", |
|
lines=1, |
|
) |
|
submit = gr.Button(value="Send", variant="secondary") |
|
|
|
examples = gr.Examples( |
|
examples=[ |
|
"What kind of models should I use for images and text?", |
|
"When should I finetune a model vs. training it form scratch?", |
|
"Can you give me some python code to quickly finetune a model on my sentiment analysis dataset?", |
|
], |
|
inputs=question, |
|
) |
|
|
|
gr.HTML("️<center> Created with ❤️ by @jerpint and @hadrienbertrand.") |
|
|
|
response = gr.State() |
|
|
|
submit.click(user, [question, chatbot], [question, chatbot], queue=False).then( |
|
chat, inputs=[chatbot], outputs=[chatbot, response] |
|
).then(add_sources, inputs=[chatbot, response], outputs=[chatbot]) |
|
question.submit(user, [question, chatbot], [question, chatbot], queue=False).then( |
|
chat, inputs=[chatbot], outputs=[chatbot, response] |
|
).then(add_sources, inputs=[chatbot, response], outputs=[chatbot]) |
|
|
|
|
|
block.queue(concurrency_count=16) |
|
block.launch(debug=True, share=False) |
|
|
