# pip install gradio transformers optimum onnxruntime onnx beautifulsoup4 langdetect deep-translator requests torch

import gradio as gr
import requests
from bs4 import BeautifulSoup
import re
from requests.sessions import Session
from langdetect import detect
from deep_translator import GoogleTranslator

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM
from optimum.pipelines import pipeline
import onnxruntime as ort
import torch

# --- ONNX CPU optimization setup ---
sess_options = ort.SessionOptions()
sess_options.intra_op_num_threads = min(4, torch.get_num_threads())
sess_options.inter_op_num_threads = 1
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL

model_name = "Rahmat82/t5-small-finetuned-summarization-xsum"
model = ORTModelForSeq2SeqLM.from_pretrained(model_name, session_options=sess_options)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)

summarizer = pipeline(
    "summarization",
    model=model,
    tokenizer=tokenizer,
    device=-1,  # CPU
    batch_size=8,
)

# --- Scraper function ---
def scrape_visible_text_from_url(url, query_selector=None, email=None, password=None, login_url=None):
    try:
        session = Session()

        if email and password and login_url:
            login_data = {'email': email, 'password': password}
            response = session.post(login_url, data=login_data)
            response.raise_for_status()
        else:
            response = session.get(url)
            response.raise_for_status()

        soup = BeautifulSoup(response.content, 'html.parser')

        for tag in soup(["script", "style", "meta", "link", "noscript", "header", "footer", "aside", "nav", "img"]):
            tag.extract()

        if query_selector:
            elements = soup.select(query_selector)
            text_content = " ".join([element.get_text() for element in elements])
        else:
            header_content = soup.find("header")
            header_text = header_content.get_text() if header_content else ""
            paragraph_content = soup.body
            paragraph_text = " ".join([p.get_text() for p in paragraph_content]) if paragraph_content else ""
            text_content = f"{header_text}\n\n{paragraph_text}"

        visible_text = re.sub(r'\s+', ' ', text_content).strip()

        translator = GoogleTranslator(source='auto', target='en')
        sentences = re.split(r'(?<=[.!?]) +', visible_text)
        translated_sentences = []
        for sentence in sentences:
            try:
                lang = detect(sentence)
                if lang != 'en':
                    translated_sentence = translator.translate(sentence)
                    translated_sentences.append(translated_sentence)
                else:
                    translated_sentences.append(sentence)
            except Exception:
                translated_sentences.append(sentence)
        translated_text = ' '.join(translated_sentences)

        return translated_text

    except Exception as e:
        return f"Error occurred while scraping: {e}"

# --- Main function for Gradio ---
def scrape_and_summarize(url, query_selector, email, password, login_url):
    scraped_text = scrape_visible_text_from_url(url, query_selector, email, password, login_url)
    if scraped_text.startswith("Error occurred"):
        return scraped_text, ""
    if not scraped_text.strip():
        return "No text found to summarize.", ""

    # Summarize scraped text
    try:
        inputs = tokenizer.encode(scraped_text, max_length=1024, truncation=True, return_tensors="pt")
        input_text = tokenizer.decode(inputs[0], skip_special_tokens=True)

        summary = summarizer(
            input_text,
            min_length=90,
            max_length=120,
            do_sample=False
        )
        return scraped_text, summary[0]["summary_text"]
    except Exception as e:
        return scraped_text, f"Error during summarization: {e}"

# --- Gradio Interface ---
with gr.Blocks() as app:
    gr.Markdown("# 🌐 Web Scraper + ⚙️ ONNX T5 Summarizer")

    with gr.Row():
        with gr.Column():
            url_input = gr.Textbox(label="Enter URL", placeholder="https://example.com", lines=1)
            query_selector_input = gr.Textbox(label="CSS Query Selector (optional)", placeholder=".article p", lines=1)
            email_input = gr.Textbox(label="Email (if login required)", lines=1)
            password_input = gr.Textbox(label="Password (if login required)", type="password", lines=1)
            login_url_input = gr.Textbox(label="Login URL (if login required)", lines=1)
            submit_btn = gr.Button("Scrape & Summarize")

        with gr.Column():
            scraped_output = gr.Textbox(label="Scraped Text", lines=15)
            summary_output = gr.Textbox(label="Summary", lines=8)

    submit_btn.click(
        fn=scrape_and_summarize,
        inputs=[url_input, query_selector_input, email_input, password_input, login_url_input],
        outputs=[scraped_output, summary_output]
    )

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