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Update app.py
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app.py
CHANGED
@@ -1,58 +1,52 @@
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import gradio as gr
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from transformers import pipeline
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from newspaper import Article
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import language_tool_python
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import nltk
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import
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from nltk.tokenize import sent_tokenize
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# Download punkt tokenizer
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nltk.download("punkt")
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#
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grammar_tool = language_tool_python.LanguageToolPublicAPI(
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#
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summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")
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toxicity_classifier = pipeline("text-classification", model="Hate-speech-CNERG/bert-base-uncased-hatexplain")
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def extract_text(input_type, text_input, url_input):
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if input_type == "URL" and url_input:
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return article.text
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except Exception as e:
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return f"Error fetching URL: {e}"
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return text_input
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def check_grammar(text):
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]
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except Exception as e:
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return [{"text": "", "error": f"Grammar check failed: {str(e)}", "suggestions": [], "offset": 0, "length": 0}]
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def detect_sensitive_content(text):
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sentences = sent_tokenize(text)
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sensitive_issues = []
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for i, sentence in enumerate(sentences):
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result = toxicity_classifier(sentence)
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label
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if any(term in label for term in ['toxic', 'hate', 'offensive']):
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sensitive_issues.append({
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"sentence": sentence,
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"score": result[0]['score'],
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"label": label,
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"index": i
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})
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return sensitive_issues
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@@ -77,27 +71,22 @@ def highlight_text(text, grammar_issues, sensitive_issues):
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span = f"<span style='background-color: yellow'>{error_text}</span>"
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highlighted = highlighted[:start] + span + highlighted[end:]
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offset_adjust += len(span) - len(error_text)
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for issue in sensitive_issues:
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return highlighted
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def review_blog(input_type, text_input, url_input):
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if not text_input and not url_input:
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return "Please provide text or a URL.", "", []
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text = extract_text(input_type, text_input, url_input)
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if text.startswith("Error"):
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return text, "", []
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grammar_issues = check_grammar(text)
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sensitive_issues = detect_sensitive_content(text)
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suggestions = generate_suggestions(text, grammar_issues, sensitive_issues)
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highlighted_text = highlight_text(text, grammar_issues, sensitive_issues)
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suggestions_text = "\n".join([f"{i+1}. {
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return highlighted_text, suggestions_text, suggestions
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def apply_changes(text, suggestions, approved_indices):
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orig_sentence = orig_match.group(1)
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text = text.replace(orig_sentence, new_text)
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else:
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orig_match = re.search(r"Replace '([^']+)'
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if orig_match:
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text = text.replace(orig_text, new_text)
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except ValueError:
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continue
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return text
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# Gradio UI
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("
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gr.Markdown("
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input_type = gr.Radio(["Text", "URL"], label="Input Type", value="Text")
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text_input = gr.Textbox(label="Blog Text", lines=10, visible=True)
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input_type.change(fn=toggle_input, inputs=input_type, outputs=[text_input, url_input])
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review_btn = gr.Button("
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apply_btn = gr.Button("
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suggestions_state = gr.State()
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review_btn.click(fn=review_blog,
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apply_btn.click(fn=apply_changes,
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inputs=[text_input, suggestions_state, approve_indices],
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outputs=final_output)
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demo.launch()
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import gradio as gr
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import language_tool_python
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import nltk
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from transformers import pipeline
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from newspaper import Article
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from nltk.tokenize import sent_tokenize
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import re
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nltk.download("punkt")
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# Use local LanguageTool server (must be running on port 8081)
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grammar_tool = language_tool_python.LanguageToolPublicAPI(
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language='en-US',
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endpoint='http://localhost:8081/v2/'
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)
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# Hugging Face pipelines
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toxicity_classifier = pipeline("text-classification", model="unitary/toxic-bert")
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summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")
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def extract_text(input_type, text_input, url_input):
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if input_type == "URL" and url_input:
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article = Article(url_input)
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article.download()
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article.parse()
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return article.text
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return text_input
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def check_grammar(text):
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matches = grammar_tool.check(text)
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return [
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{
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"text": match.context,
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"error": match.message,
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"suggestions": match.replacements,
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"offset": match.offset,
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"length": match.errorLength
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} for match in matches
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]
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def detect_sensitive_content(text):
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sentences = sent_tokenize(text)
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sensitive_issues = []
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for i, sentence in enumerate(sentences):
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result = toxicity_classifier(sentence)
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if result[0]['label'] == 'toxic' and result[0]['score'] > 0.7:
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sensitive_issues.append({
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"sentence": sentence,
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"score": result[0]['score'],
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"index": i
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})
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return sensitive_issues
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span = f"<span style='background-color: yellow'>{error_text}</span>"
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highlighted = highlighted[:start] + span + highlighted[end:]
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offset_adjust += len(span) - len(error_text)
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for issue in sensitive_issues:
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highlighted = highlighted.replace(
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issue['sentence'],
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f"<span style='background-color: red'>{issue['sentence']}</span>"
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)
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return highlighted
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def review_blog(input_type, text_input, url_input):
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if not text_input and not url_input:
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return "Please provide text or a URL.", "", []
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text = extract_text(input_type, text_input, url_input)
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grammar_issues = check_grammar(text)
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sensitive_issues = detect_sensitive_content(text)
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suggestions = generate_suggestions(text, grammar_issues, sensitive_issues)
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highlighted_text = highlight_text(text, grammar_issues, sensitive_issues)
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suggestions_text = "\n".join([f"{i+1}. {s}" for i, s in enumerate(suggestions)])
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return highlighted_text, suggestions_text, suggestions
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def apply_changes(text, suggestions, approved_indices):
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orig_sentence = orig_match.group(1)
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text = text.replace(orig_sentence, new_text)
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else:
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orig_match = re.search(r"Replace '([^']+)' with '([^']+)'", suggestion)
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if orig_match:
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text = text.replace(orig_match.group(1), orig_match.group(2))
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except ValueError:
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continue
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return text
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("# ✨ AI Blog Reviewer")
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gr.Markdown("Highlight grammar & sensitive issues. Rephrase toxic content. Approve and apply changes.")
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input_type = gr.Radio(["Text", "URL"], label="Input Type", value="Text")
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text_input = gr.Textbox(label="Blog Text", lines=10, visible=True)
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input_type.change(fn=toggle_input, inputs=input_type, outputs=[text_input, url_input])
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review_btn = gr.Button("Review Content")
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highlighted = gr.HTML()
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suggestions = gr.Textbox(label="Suggestions", lines=10)
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approved = gr.Textbox(label="Approve Suggestions (e.g., 1,3)")
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apply_btn = gr.Button("Apply Changes")
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final = gr.Textbox(label="Final Text", lines=10)
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suggestions_state = gr.State()
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review_btn.click(fn=review_blog, inputs=[input_type, text_input, url_input],
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outputs=[highlighted, suggestions, suggestions_state])
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apply_btn.click(fn=apply_changes, inputs=[text_input, suggestions_state, approved], outputs=final)
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demo.launch()
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