app.py

"""
Credit to Derek Thomas, [email protected]
"""
import os
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 generate_hf, generate_openai
from backend.semantic_search import retrieve

from dotenv import load_dotenv
load_dotenv()

TOP_K = int(os.getenv("TOP_K", 4))
HF_TOKEN = os.getenv("HF_TOKEN")

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')


def add_text(history, text):
    logger.info(f'Adding text: {text}')
    history = [] if history is None else history
    history = history + [(text, None)]
    logger.info(f'Updated history: {history}')
    return history, gr.Textbox(value="", interactive=False)


def bot(history, api_kind):
    logger.info(f'Bot function called with history: {history} and api_kind: {api_kind}')
    query = history[-1][0]
    logger.info(f'Query: {query}')

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

    logger.info('Retrieving documents...')
    # Retrieve documents relevant to query
    document_start = perf_counter()

    documents = retrieve(query, TOP_K)

    document_time = perf_counter() - document_start
    logger.info(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)
    logger.info(f'Prompt created: {prompt}')

    if api_kind == "HuggingFace":
        generate_fn = generate_hf
    elif api_kind == "OpenAI":
        generate_fn = generate_openai
    else:
        raise gr.Error(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(
            fn=add_text,
            inputs=[chatbot, txt],
            outputs=[chatbot, txt]
        ).then(
            fn=bot,
            inputs=[chatbot, api_kind],
            outputs=[chatbot, prompt_html]
        )
    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(
        fn=add_text,
        inputs=[chatbot, txt],
        outputs=[chatbot, txt]
    ).then(
        fn=bot,
        inputs=[chatbot, api_kind],
        outputs=[chatbot, prompt_html]
    )
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)

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