app.py

import gradio as gr
from transformers import pipeline
from newspaper import Article
import nltk
from nltk.tokenize import sent_tokenize
import re

# Download punkt tokenizer
nltk.download("punkt")

# Load Hugging Face pipelines
grammar_corrector = pipeline("text2text-generation", model="pszemraj/flan-t5-base-grammar-synthesis")
toxicity_detector = pipeline("text-classification", model="unitary/toxic-bert")

# Utility: extract blog text from URL
def extract_text(input_type, text_input, url_input):
    if input_type == "Text":
        return text_input
    elif input_type == "URL":
        try:
            article = Article(url_input)
            article.download()
            article.parse()
            return article.text
        except Exception as e:
            return f"Error extracting article: {str(e)}"
    return ""

# Highlight grammar & sensitive content
def highlight_text(text, grammar_sentences, toxic_sentences):
    for s in grammar_sentences:
        text = text.replace(s, f"<span style='background-color: yellow'>{s}</span>")
    for s in toxic_sentences:
        text = text.replace(s, f"<span style='background-color: red'>{s}</span>")
    return text

# Main blog review function
def review_blog(input_type, text_input, url_input):
    text = extract_text(input_type, text_input, url_input)
    if not text or text.startswith("Error"):
        return text, "", []

    sentences = sent_tokenize(text)
    grammar_issues = []
    toxic_issues = []
    suggestions = []

    for sent in sentences:
        # Check grammar by comparing original and corrected
        corrected = grammar_corrector(sent, max_length=128, do_sample=False)[0]['generated_text']
        if corrected.strip() != sent.strip():
            grammar_issues.append(sent)
            suggestions.append(f"Grammar: Replace '{sent}' → '{corrected}'")

        # Check for toxicity
        result = toxicity_detector(sent)
        if result[0]['label'] == 'toxic' and result[0]['score'] > 0.7:
            toxic_issues.append(sent)
            suggestions.append(f"Toxicity: Rephrase '{sent}' (score: {result[0]['score']:.2f})")

    highlighted = highlight_text(text, grammar_issues, toxic_issues)
    sug_text = "\n".join(f"{i+1}. {s}" for i, s in enumerate(suggestions))
    return highlighted, sug_text, suggestions

# Apply approved suggestions
def apply_changes(original_text, suggestions, indices):
    try:
        indices = [int(i.strip()) - 1 for i in indices.split(",") if i.strip().isdigit()]
        sentences = sent_tokenize(original_text)
        for i in indices:
            if i < len(suggestions):
                match = re.search(r"'(.*?)'\s*→\s*'(.*?)'", suggestions[i])
                if match:
                    old = match.group(1)
                    new = match.group(2)
                    original_text = original_text.replace(old, new)
        return original_text
    except Exception as e:
        return f"Error applying changes: {str(e)}"

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("## ✨ Blog Content Reviewer (LLM-powered)")
    gr.Markdown("Paste blog content or a blog URL. AI will detect grammar issues & sensitive content.")

    input_type = gr.Radio(["Text", "URL"], value="Text", label="Input Type")
    text_input = gr.Textbox(label="Blog Text", lines=10, visible=True)
    url_input = gr.Textbox(label="Blog URL", visible=False)

    input_type.change(lambda t: {text_input: gr.update(visible=t=="Text"),
                                 url_input: gr.update(visible=t=="URL")},
                      input_type, [text_input, url_input])

    review_button = gr.Button("Review")
    highlighted_output = gr.HTML()
    suggestions_output = gr.Textbox(label="Suggestions", lines=10)
    approve_input = gr.Textbox(label="Approve Suggestions (e.g., 1,2)")
    apply_button = gr.Button("Apply Changes")
    final_text = gr.Textbox(label="Final Output", lines=10)

    suggestions_state = gr.State()

    review_button.click(
        fn=review_blog,
        inputs=[input_type, text_input, url_input],
        outputs=[highlighted_output, suggestions_output, suggestions_state]
    )

    apply_button.click(
        fn=apply_changes,
        inputs=[text_input, suggestions_state, approve_input],
        outputs=[final_text]
    )

demo.launch()