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import logging | |
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') | |
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) | |