import gradio as gr
import requests
from bs4 import BeautifulSoup
from transformers import pipeline

# Use faster, open-access model
llm = pipeline("text2text-generation", model="EleutherAI/gpt-neo-2.7B")

def refine_from_url(url, instruction):
    try:
        response = requests.get(url, timeout=10)
        soup = BeautifulSoup(response.text, "html.parser")
        raw_text = soup.get_text(separator="\n")

        prompt = f"""
You are a data refinement agent. Given the following webpage content, do the following:
1. Extract clear headings and structure the content.
2. Generate 5 question-answer pairs based on the content.
3. Format everything in JSONL style for GPT2 training.

Instruction: {instruction}

Content:
{raw_text[:3000]}
"""

        output = llm(prompt, max_new_tokens=512)[0]["generated_text"]
        return output
    except Exception as e:
        return f"Error: {str(e)}"

demo = gr.Interface(
    fn=refine_from_url,
    inputs=[
        gr.Textbox(label="🔗 Enter Webpage URL"),
        gr.Textbox(label="🧠 Instruction", placeholder="e.g. Clean and format this for GPT2 training")
    ],
    outputs=gr.Textbox(label="📄 Refined JSONL Output", lines=30, max_lines=60),
    title="🧠 Link-Based Data Refiner + Q&A Generator",
    description="Paste any webpage link. This app will crawl, refine, and generate question-answer pairs using Flan-T5."
)

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