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""" |
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Credit to Derek Thomas, [email protected] |
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""" |
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import os |
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import logging |
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from pathlib import Path |
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from time import perf_counter |
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import gradio as gr |
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from jinja2 import Environment, FileSystemLoader |
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from backend.query_llm import generate_hf, generate_openai |
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from backend.semantic_search import retrieve |
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TOP_K = int(os.getenv("TOP_K", 4)) |
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proj_dir = Path(__file__).parent |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) |
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template = env.get_template('template.j2') |
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template_html = env.get_template('template_html.j2') |
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def add_text(history, text): |
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history = [] if history is None else history |
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history = history + [(text, None)] |
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return history, gr.Textbox(value="", interactive=False) |
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def bot(history, api_kind): |
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query = history[-1][0] |
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if not query: |
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raise gr.Warning("Please submit a non-empty string as a prompt") |
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logger.info('Retrieving documents...') |
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document_start = perf_counter() |
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documents = retrieve(query, TOP_K) |
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document_time = perf_counter() - document_start |
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logger.info(f'Finished Retrieving documents in {round(document_time, 2)} seconds...') |
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prompt = template.render(documents=documents, query=query) |
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prompt_html = template_html.render(documents=documents, query=query) |
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if api_kind == "HuggingFace": |
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generate_fn = generate_hf |
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elif api_kind == "OpenAI": |
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generate_fn = generate_openai |
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else: |
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raise gr.Error(f"API {api_kind} is not supported") |
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history[-1][1] = "" |
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for character in generate_fn(prompt, history[:-1]): |
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history[-1][1] = character |
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yield history, prompt_html |
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with gr.Blocks() as demo: |
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chatbot = gr.Chatbot( |
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[], |
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elem_id="chatbot", |
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avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg', |
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'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'), |
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bubble_full_width=False, |
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show_copy_button=True, |
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show_share_button=True, |
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) |
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with gr.Row(): |
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txt = gr.Textbox( |
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scale=3, |
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show_label=False, |
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placeholder="Enter text and press enter", |
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container=False, |
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) |
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txt_btn = gr.Button(value="Submit text", scale=1) |
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api_kind = gr.Radio(choices=["HuggingFace", "OpenAI"], value="HuggingFace") |
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prompt_html = gr.HTML() |
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txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( |
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bot, [chatbot, api_kind], [chatbot, prompt_html]) |
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txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) |
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txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( |
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bot, [chatbot, api_kind], [chatbot, prompt_html]) |
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txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) |
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demo.queue() |
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demo.launch(debug=True) |
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