File size: 3,722 Bytes
d60948f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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
)
# print the page instead of the heading, more meaningful for hf docs
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)
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(
"""
#### 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)
|