app.py

import gradio as gr
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup
import pandas as pd

"""
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("meta-llama/Llama-3.2-3B-Instruct")


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

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            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


def extract_table(url):
    global data
    try:
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        table = soup.find('table')
        if not table:
            return "<p>No table found on page</p>"
        
        data = []
        rows = table.find_all('tr')
        for i, row in enumerate(rows[1:]):
            cells = row.find_all('td')
            if len(cells) >= 2:
                data.append({
                    'Date': cells[0].text.strip()[:10],
                    'Topic': cells[1].text.strip(),
                })
        
        df = pd.DataFrame(data)
        return df.to_html(escape=False, index=False)
    except Exception as e:
        return f"<p>Error: {str(e)}</p>"


def display_table(url):
    return extract_table(url)


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            url_input = gr.Textbox(
                value="https://id2223kth.github.io/schedule/",
                label="Table URL"
            )
            table_output = gr.HTML(label="Extracted Table")
            extract_btn = gr.Button("Extract Table")
            
            extract_btn.click(
                fn=display_table,
                inputs=[url_input],
                outputs=[table_output]
            )
        
        with gr.Column(scale=2):
            
            chatbot = gr.ChatInterface(
                respond,
                additional_inputs=[
                    gr.Textbox(value="Student class preparation companion.", label="System message"),
                ],
            )
            

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