app.py

import logging
from functools import partial
from pathlib import Path
from time import perf_counter

import gradio as gr
from jinja2 import Environment, FileSystemLoader
from transformers import AutoTokenizer

from backend.query_llm import check_endpoint_status, generate
from backend.semantic_search import retriever

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')

# Initialize tokenizer
tokenizer = AutoTokenizer.from_pretrained('derek-thomas/jais-13b-chat-hf')

# Examples
examples = ['من كان طرفي معركة اكتيوم البحرية؟',
            'لم السماء زرقاء؟',
            "من فاز بكأس العالم للرجال في عام 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, hyde=False):
    top_k = 5
    query = history[-1][0]

    logger.warning('Retrieving documents...')
    # Retrieve documents relevant to query
    document_start = perf_counter()
    if hyde:
        hyde_document = generate(f"Write a wikipedia article intro paragraph to answer this query: {query}").split('### Response: [|AI|]')[-1]

        logger.warning(hyde_document)
        documents = retriever(hyde_document, top_k=top_k)
    else:
        documents = retriever(query, top_k=top_k)
    document_time = perf_counter() - document_start
    logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')

    # Function to count tokens
    def count_tokens(text):
        return len(tokenizer.encode(text))

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

    # Check if the prompt is too long
    token_count = count_tokens(prompt)
    while token_count > 2048:
        # Shorten your documents here. This is just a placeholder for the logic you'd use.
        documents.pop()  # Remove the last document
        prompt = template.render(documents=documents, query=query)  # Re-render the prompt
        token_count = count_tokens(prompt)  # Re-count tokens

    prompt_html = template_html.render(documents=documents, query=query)

    history[-1][1] = ""
    response = generate(prompt)
    history[-1][1] = response.split('### Response: [|AI|]')[-1]
    return history, prompt_html

intro_md = """
# Arabic RAG
This is a project to demonstrate Retreiver Augmented Generation (RAG) in Arabic and English. It uses 
[Arabic Wikipedia](https://ar.wikipedia.org/wiki) as a base to answer questions you have. 
A retriever ([sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/discussions/8))
 will find the articles relevant to your query and include them in a prompt so the reader ([core42/jais-13b-chat](https://huggingface.co/core42/jais-13b-chat)) 
 can then answer your questions on it.
 
You can see the prompt clearly displayed below the chatbot to understand what is going to the LLM. 

# Read this if you get an error
I'm using Inference Endpoint's Scale to Zero to save money on GPUs. If the staus shows its not "Running" send a 
chat to wake it up. You will get a `500 error` and it will take ~7 min to wake up.
"""

with gr.Blocks() as demo:
    gr.Markdown(intro_md)
    endpoint_status = gr.Textbox(check_endpoint_status, label="Inference Endpoint Status", every=1)
    with gr.Tab("Arabic-RAG"):
        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 query in Arabic or English and press enter",
                    container=False,
                    )
            txt_btn = gr.Button(value="Submit text", scale=1)

        gr.Examples(examples, txt)
        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, [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, [chatbot, prompt_html])

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

    with gr.Tab("Arabic-RAG + HyDE"):
        hyde_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():
            hyde_txt = gr.Textbox(
                    scale=3,
                    show_label=False,
                    placeholder="Enter text and press enter",
                    container=False,
                    )
            hyde_txt_btn = gr.Button(value="Submit text", scale=1)

        gr.Examples(examples, hyde_txt)
        hyde_prompt_html = gr.HTML()
        # Turn off interactivity while generating if you click
        hyde_txt_msg = hyde_txt_btn.click(add_text, [hyde_chatbot, hyde_txt], [hyde_chatbot, hyde_txt],
                                          queue=False).then(
                partial(bot, hyde=True), [hyde_chatbot], [hyde_chatbot, hyde_prompt_html])

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

        # Turn off interactivity while generating if you hit enter
        hyde_txt_msg = hyde_txt.submit(add_text, [hyde_chatbot, hyde_txt], [hyde_chatbot, hyde_txt], queue=False).then(
                partial(bot, hyde=True), [hyde_chatbot], [hyde_chatbot, hyde_prompt_html])

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

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