app.py

import gradio as gr
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup
from bs4.element import Comment

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

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 "\n".join(t.strip() for t in visible_texts)

# Pre-fetch and truncate homepage text to reduce the prompt length.
text_list = []
homepage_url = "https://sites.google.com/view/abhilashnandy/home/"
extensions = ["", "pmrf-profile-page"]
for ext in extensions:
    full_text = get_text_from_url(homepage_url + ext)
    truncated_text = full_text[:1000]  # using only the first 1000 characters per extension
    text_list.append(truncated_text)

SYSTEM_MESSAGE = (
    "You are a QA chatbot to answer queries (in less than 30 words) on my homepage. "
    "Context: " + " ".join(text_list)
)

# Create a Hugging Face Inference client using a CPU-friendly model.
# Here we use 'google/flan-t5-base' as an example; you can adjust the model if needed.
client = InferenceClient(model="google/flan-t5-base")

def answer_query(query):
    # Compose a prompt using the system message, user query, and a reminder for a short answer.
    prompt = SYSTEM_MESSAGE + "\nUser: " + query + "\nAnswer in less than 30 words:"
    # Generate answer with a limit on new tokens to ensure brevity.
    result = client.text_generation(prompt, max_new_tokens=60)
    # Handle both list or direct string responses from the inference client.
    if isinstance(result, list):
        answer = result[0].get("generated_text", "")
    else:
        answer = result
    return answer.strip()

iface = gr.Interface(
    fn=answer_query,
    inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
    outputs="text",
    title="Homepage QA Chatbot",
    description="A chatbot answering queries about the homepage using pre-fetched context."
)

if __name__ == '__main__':
    iface.launch()