app.py

import logging
import os
from pathlib import Path
from time import perf_counter

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

from backend.query_llm import 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('inception-mbzuai/jais-13b-chat')


def check_endpoint_status():
    # Replace with the actual API URL and headers
    api_url = os.getenv("ENDPOINT_URL")
    headers = {
        'accept': 'application/json',
        'Authorization': f'Bearer {os.getenv("BEARER")}'
        }

    try:
        response = requests.get(api_url, headers=headers)
        response.raise_for_status()  # will throw an exception for non-200 status
        data = response.json()

        # Extracting the status information
        status = data.get('status', {}).get('state', 'No status found')
        message = data.get('status', {}).get('message', 'No message found')

        return f"Status: {status}\nMessage: {message}"
    except requests.exceptions.RequestException as e:
        return f"Failed to get status: {str(e)}"

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, system_prompt=""):
    top_k = 5
    query = history[-1][0]

    logger.warning('Retrieving documents...')
    # Retrieve documents relevant to query
    document_start = perf_counter()
    documents = retriever(query, top_k=top_k)
    document_time = document_start - perf_counter()
    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] = ""
    for character in generate(prompt):
        history[-1][1] = character
        yield history, prompt_html


with gr.Blocks() as demo:
    with gr.Tab("Application"):
        output = gr.Textbox(check_endpoint_status, label="Endpoint Status (send chat to wake up)", every=1)
        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)

        prompt_html = gr.HTML()
        # Turn off interactivity while generating if you hit enter
        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)

    gr.Examples(['What is the capital of China, I think its Shanghai?',
                 'Why is the sky blue?',
                 'Who won the mens world cup in 2014?',], txt)

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