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"""
Credit to Derek Thomas, derek@huggingface.co
"""
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 backend.cross_encoder import rerank_with_cross_encoder
TOP_K = int(os.getenv("TOP_K", 4))
TOP_K_RERANK = int(os.getenv("TOP_K_RERANK", 40))
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):
history = [] if history is None else history
history = history + [(text, None)]
return history, gr.Textbox(value="", interactive=False)
def bot(history, api_kind, cross_enc):
query = history[-1][0]
if not query:
raise gr.Warning("Please submit a non-empty string as a prompt")
logger.info('Retrieving documents...')
# Retrieve documents relevant to query
documents = []
if cross_enc is None:
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...')
else:
document_start = perf_counter()
documents = retrieve(query, TOP_K_RERANK)
document_time = perf_counter() - document_start
logger.info(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
logger.info('Reranking documents')
document_start = perf_counter()
documents = rerank_with_cross_encoder(cross_enc, documents, query)
document_time = perf_counter() - document_start
logger.info(f'Finished Reranking 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
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", label="LLM")
cross_enc = gr.Radio(choices=[None, "cross-encoder/ms-marco-MiniLM-L-6-v2", "BAAI/bge-reranker-large"], value=None, label="Cross Encoder")
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, cross_enc], [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, cross_enc], [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
demo.queue()
demo.launch(debug=True)
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