app.py

import gradio as gr
from huggingface_hub import InferenceClient

import requests
from bs4 import BeautifulSoup
from bs4.element import Comment

def get_text_from_url(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    texts = soup.find_all(text=True)
    visible_texts = filter(tag_visible, texts)
    return u"\n".join(t.strip() for t in visible_texts)

def tag_visible(element):
    if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:
        return False
    if isinstance(element, Comment):
        return False
    return True

text_list = []
homepage_url = "https://sites.google.com/view/abhilashnandy/home/"
extensions = ["", "about", "curriculum-vitae", "pmrf-profile-page", "publications"]
for ext in extensions:
    url_text = get_text_from_url(homepage_url+ext)
    text_list.append(url_text)
# Repeat for sub-links if necessary

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("QuantFactory/Meta-Llama-3-8B-Instruct-GGUF")#("HuggingFaceH4/zephyr-7b-beta")

SYSTEM_MESSAGE = "You are a QA chatbot to answer queries on my homepage that has the following information -\n\n" + "\n\n".join(text_list)

def respond(
    message,
    history: list[tuple[str, str]],
    system_message=SYSTEM_MESSAGE,
    max_tokens=200,
    temperature=0.7,
    top_p=0.95,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        # if val[0]:
        if len(val)>=1:
            messages.append({"role": "user", "content": val[0]})
        # if val[1]:
        if len(val)>=2:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        # gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        # gr.Slider(minimum=1, maximum=8192, value=512, step=1, label="Max new tokens"),
        # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        # gr.Slider(
        #     minimum=0.1,
        #     maximum=1.0,
        #     value=0.95,
        #     step=0.05,
        #     label="Top-p (nucleus sampling)",
        # ),
    ],
)


if __name__ == "__main__":
    demo.launch()