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, get_max_length from backend.semantic_search import retrieve, reranking DOCS_AMOUNT = int(os.getenv("DOCS_AMOUNT", 30)) TOP_K = int(os.getenv("TOP_K", 4)) 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: str): history = [] if history is None else history history = history + [(text, None)] return history, gr.Textbox(value="", interactive=False) def bot(history, api_kind): query = history[-1][0] if not query: raise Warning("Please submit a non-empty string as a prompt") logger.info('Retrieving documents...') # Retrieve documents relevant to query document_start = perf_counter() raw_documents = retrieve(query, DOCS_AMOUNT) print('MAX LENGTH', get_max_length(raw_documents)) documents = reranking(query, raw_documents, TOP_K) document_time = perf_counter() - document_start logger.info(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 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") 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) demo.queue() demo.launch(debug=True)