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app.py

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+"""
+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 dotenv import load_dotenv
+load_dotenv()
+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)

requirements.txt

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+lancedb==0.5.3
+openai==1.11.1
+sentence-transformers==2.3.1
+tqdm==4.66.1
+torch==2.1.1
+transformers==4.37.2
+python-dotenv==1.0.1
+jinja2==3.0.1