app.py

"""
Credit to Derek Thomas, derek@huggingface.co
"""

import subprocess

subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"])

import logging
from pathlib import Path
from time import perf_counter

import gradio as gr
from jinja2 import Environment, FileSystemLoader

from backend.query_llm import embed_docs, generate_hf, generate_openai
from backend.semantic_search import table, retriever

VECTOR_COLUMN_NAME = "embedding"
TEXT_COLUMN_NAME = "text"

proj_dir = Path(__file__).parent
# Setting up the logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set up the template environment with the templates directory
env = Environment(loader=FileSystemLoader(proj_dir / "templates"))

# Load the templates directly from the environment
template = env.get_template("template.j2")
template_html = env.get_template("template_html.j2")

# Examples
examples = [
    "What is the capital of China?",
    "Why is the sky blue?",
    "Who won the mens world cup in 2014?",
]


def add_text(history, text):
    history = [] if history is None else history
    history = history + [(text, None)]
    return history, gr.Textbox(value="", interactive=False)


def bot(history, api_kind):
    top_k_rank = 4
    query = history[-1][0]

    if not query:
        gr.Warning("Please submit a non-empty string as a prompt")
        raise ValueError("Empty string was submitted")

    logger.warning("Retrieving documents...")
    # Retrieve documents relevant to query
    document_start = perf_counter()

    query_vec = retriever.encode(query)
    #     print(query_vec)
    #     print(table)
    #     print('------')
    documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()
    documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
    document_time = perf_counter() - document_start
    logger.warning(f"Finished Retrieving documents in {round(document_time, 2)} seconds...")

    # Create Prompt
    prompt = template.render(documents=documents, query=query)
    prompt_html = template_html.render(documents=documents, query=query)

    if api_kind == "HuggingFace":
        generate_fn = generate_hf
    elif api_kind == "OpenAI":
        generate_fn = generate_openai
    elif api_kind is None:
        gr.Warning("API name was not provided")
        raise ValueError("API name was not provided")
    else:
        gr.Warning(f"API {api_kind} is not supported")
        raise ValueError(f"API {api_kind} is not supported")

    history[-1][1] = ""

    for character in generate_fn(prompt, history[:-1]):
        history[-1][1] = character
        yield history, prompt_html


with gr.Blocks() as demo:
    chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        avatar_images=(
            "https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg",
            "https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg",
        ),
        bubble_full_width=False,
        show_copy_button=True,
        show_share_button=True,
    )

    with gr.Row():
        txt = gr.Textbox(
            scale=3,
            show_label=False,
            placeholder="Enter text and press enter",
            container=False,
        )
        txt_btn = gr.Button(value="Submit text", scale=1)

    api_kind = gr.Radio(choices=["HuggingFace", "OpenAI"], value="HuggingFace")

    prompt_html = gr.HTML()
    # Turn off interactivity while generating if you click
    txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
        bot, [chatbot, api_kind], [chatbot, prompt_html]
    )

    # Turn it back on
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)

    # Turn off interactivity while generating if you hit enter
    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
        bot, [chatbot, api_kind], [chatbot, prompt_html]
    )

    # Turn it back on
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)

    # Examples
    gr.Examples(examples, txt)

demo.queue()
demo.launch(debug=True, share=True)