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import subprocess
subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"])
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
from backend.semantic_search import qd_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')
# Examples
examples = ['What is the capital of China, I think its Shanghai?',
'Why is the sky blue?',
'Who won the mens world cup in 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 = 4
query = history[-1][0]
logger.warning('Retrieving documents...')
# Retrieve documents relevant to query
document_start = perf_counter()
if hyde:
complete_output = ""
generator = generate(f"Write a wikipedia article intro paragraph to answer this query: {query}", history)
for output_chunk in generator:
complete_output += output_chunk
documents = qd_retriever.retrieve(query, top_k=top_k)
document_time = document_start - perf_counter()
logger.warning(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)
history[-1][1] = ""
for character in generate(prompt, history[:-1]):
history[-1][1] = character
yield history, prompt_html
with gr.Blocks() as demo:
with gr.Tab("RAGDemo"):
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 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)
# Examples
gr.Examples(examples, txt)
with gr.Tab("RAGDemo + 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)
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(
bot, 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(
bot, 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)
# Examples
gr.Examples(examples, hyde_txt)
demo.queue()
demo.launch(debug=True)
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