app.py

import logging
import os
from typing import Optional

import gradio as gr
import pandas as pd

from buster.completers import Completion

import cfg
from cfg import setup_buster

buster = setup_buster(cfg.buster_cfg)

#  suppress httpx logs they are spammy and uninformative
logging.getLogger("httpx").setLevel(logging.WARNING)

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

CONCURRENCY_COUNT = int(os.getenv("CONCURRENCY_COUNT", 64))

AVAILABLE_SOURCES_UI = [
    "Toward's AI",
    "HuggingFace",
    "Wikipedia",
    "Gen AI 360: LangChain",
    "Gen AI 360: LLMs",
]

AVAILABLE_SOURCES = [
    "towards_ai",
    "hf_transformers",
    "wikipedia",
    "langchain_course",
    "llm_course",
]


def log_likes(completion: Completion, like_data: gr.LikeData):
    # make it a str so json-parsable

    collection = "liked_data-test"

    completion_json = completion.to_json(
        columns_to_ignore=["embedding", "similarity", "similarity_to_answer"]
    )
    completion_json["liked"] = like_data.liked
    logger.info(f"User reported {like_data.liked=}")

    try:
        cfg.mongo_db[collection].insert_one(completion_json)
        logger.info("")
    except:
        logger.info("Something went wrong logging")


def log_emails(email: gr.Textbox):
    collection = "email_data-test"

    logger.info(f"User reported {email=}")
    email_document = {"email": email}

    try:
        cfg.mongo_db[collection].insert_one(email_document)
        logger.info("")
    except:
        logger.info("Something went wrong logging")

    return ""


def format_sources(matched_documents: pd.DataFrame) -> str:
    if len(matched_documents) == 0:
        return ""

    documents_answer_template: str = "📝 Here are the sources I used to answer your question:\n\n{documents}\n\n{footnote}"
    document_template: str = "[🔗 {document.source}: {document.title}]({document.url}), highest relevance: {document.similarity_to_answer:2.1f} % | # total chunks matched: {document.repetition:d}"

    matched_documents.similarity_to_answer = (
        matched_documents.similarity_to_answer * 100
    )

    matched_documents["repetition"] = matched_documents.groupby("title")[
        "title"
    ].transform("size")

    # drop duplicates, keep highest ranking ones
    matched_documents = matched_documents.sort_values(
        "similarity_to_answer", ascending=False
    ).drop_duplicates("title", keep="first")[:5]

    # Revert back to correct display
    display_source_to_ui = {
        ui: src for ui, src in zip(AVAILABLE_SOURCES, AVAILABLE_SOURCES_UI)
    }
    matched_documents["source"] = matched_documents["source"].replace(
        display_source_to_ui
    )

    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 get_empty_source_completion(user_input):
    return Completion(
        user_input=user_input,
        answer_text="You have to select at least one source from the dropdown menu.",
        matched_documents=pd.DataFrame(),
        error=False,
    )


def get_answer(history, sources: Optional[list[str]] = None):
    user_input = history[-1][0]

    if len(sources) == 0:
        completion = get_empty_source_completion(user_input)

    else:
        # Go to code names
        display_ui_to_source = {
            ui: src for ui, src in zip(AVAILABLE_SOURCES_UI, AVAILABLE_SOURCES)
        }

        sources_renamed = [display_ui_to_source[disp] for disp in sources]
        completion = buster.process_input(user_input, sources=sources_renamed)

    history[-1][1] = ""

    for token in completion.answer_generator:
        history[-1][1] += token

        yield history, completion


# CSS = """
# .contain { display: flex; flex-direction: column; }
# .gradio-container { height: 100vh !important; }
# #component-0 { height: 100%; }
# #chatbot { flex-grow: 1; overflow: auto;}
# """
theme = gr.themes.Base()
demo = gr.Blocks(theme=theme)
with demo:
    with gr.Row():
        gr.Markdown(
            "<h3><center>Toward's AI x Buster 🤖: A Question-Answering Bot for anything AI-related</center></h3>"
        )

    latest_completion = gr.State()

    source_selection = gr.Dropdown(
        choices=AVAILABLE_SOURCES_UI,
        label="Select Sources",
        value=AVAILABLE_SOURCES_UI,
        multiselect=True,
    )

    chatbot = gr.Chatbot(elem_id="chatbot")

    with gr.Row():
        question = gr.Textbox(
            label="What's your question?",
            placeholder="Ask a question to our AI tutor here...",
            lines=1,
        )
        submit = gr.Button(value="Send", variant="secondary")

    with gr.Row():
        examples = gr.Examples(
            examples=cfg.example_questions,
            inputs=question,
        )
        with gr.Row():
            email = gr.Textbox(
                label="Want to receive updates about our AI tutor?",
                placeholder="Enter your email here...",
                lines=1,
                scale=3,
            )
            submit_email = gr.Button(value="Submit", variant="secondary", scale=0)

    gr.Markdown(
        "This application uses ChatGPT to search the docs for relevant info and answer questions. "
        "\n\n### Powered by [Buster 🤖](www.github.com/jerpint/buster)"
    )

    completion = gr.State()

    submit.click(user, [question, chatbot], [question, chatbot], queue=False).then(
        get_answer, inputs=[chatbot, source_selection], outputs=[chatbot, completion]
    ).then(add_sources, inputs=[chatbot, completion], outputs=[chatbot])
    question.submit(user, [question, chatbot], [question, chatbot], queue=False).then(
        get_answer, inputs=[chatbot, source_selection], outputs=[chatbot, completion]
    ).then(add_sources, inputs=[chatbot, completion], outputs=[chatbot])

    chatbot.like(log_likes, completion)

    submit_email.click(log_emails, email, email)
    email.submit(log_emails, email, email)

demo.queue(concurrency_count=CONCURRENCY_COUNT)
demo.launch(debug=True, share=False)