m

app.py

@@ -1,107 +0,0 @@
-"""
-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
-
-
-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):
-    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 gr.Warning("Please submit a non-empty string as a prompt")
-
-    logger.info('Retrieving documents...')
-    # Retrieve documents relevant to query
-    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...')
-
-    # 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)