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"""
Credit to Derek Thomas, derek@huggingface.co
"""
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 embed_docs, generate_hf, generate_openai
from backend.semantic_search import table, retriever
VECTOR_COLUMN_NAME = "embedding"
TEXT_COLUMN_NAME = "text"
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?",
"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, api_kind):
top_k_rank = 4
query = history[-1][0]
if not query:
gr.Warning("Please submit a non-empty string as a prompt")
raise ValueError("Empty string was submitted")
logger.warning("Retrieving documents...")
# Retrieve documents relevant to query
document_start = perf_counter()
query_vec = retriever.encode(query)
# print(query_vec)
# print(table)
# print('------')
documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()
documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
document_time = perf_counter() - document_start
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)
if api_kind == "HuggingFace":
generate_fn = generate_hf
elif api_kind == "OpenAI":
generate_fn = generate_openai
elif api_kind is None:
gr.Warning("API name was not provided")
raise ValueError("API name was not provided")
else:
gr.Warning(f"API {api_kind} is not supported")
raise ValueError(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")
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], [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], [chatbot, prompt_html]
)
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
# Examples
gr.Examples(examples, txt)
demo.queue()
demo.launch(debug=True, share=True)
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