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Update app.py
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app.py
CHANGED
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@@ -2,13 +2,13 @@ import os
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import gradio as gr
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from transformers import pipeline
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import spacy
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import subprocess
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import nltk
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from nltk.corpus import wordnet
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from spellchecker import SpellChecker
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import re
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import inflect
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try:
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nlp = spacy.load("en_core_web_sm")
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except OSError:
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@@ -16,8 +16,6 @@ except OSError:
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spacy.cli.download("en_core_web_sm")
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nlp = spacy.load("en_core_web_sm")
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# Initialize the English text classification pipeline for AI detection
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pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
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@@ -31,9 +29,6 @@ inflect_engine = inflect.engine()
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nltk.download('wordnet')
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nltk.download('omw-1.4')
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# Load the SpaCy model
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nlp = spacy.load("en_core_web_sm")
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# Function to predict the label and score for English text (AI Detection)
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def predict_en(text):
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res = pipeline_en(text)[0]
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@@ -172,22 +167,67 @@ def ensure_subject_verb_agreement(text):
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corrected_text.append(token.text)
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return ' '.join(corrected_text)
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#
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def
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words = text.split()
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corrected_words = []
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for word in words:
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return ' '.join(corrected_words)
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#
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def
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text = re.sub(r'\s+([?.!,";:])', r'\1', text)
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return text
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# Function to
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def handle_possessives(text):
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text = re.sub(r"\b(\w+)'s\b", r"\1's", text)
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return text
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@@ -231,53 +271,40 @@ def rephrase_with_synonyms(text):
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return ' '.join(rephrased_text)
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# Function to paraphrase and correct grammar with enhanced accuracy
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def paraphrase_and_correct(text):
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# Remove meaningless or redundant words first
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cleaned_text = remove_redundant_words(text)
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# Capitalize sentences and proper nouns
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cleaned_text = capitalize_sentences_and_nouns(cleaned_text)
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# Correct tense errors
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cleaned_text = correct_tense_errors(cleaned_text)
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# Correct singular/plural errors
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cleaned_text = correct_singular_plural_errors(cleaned_text)
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# Correct article errors
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cleaned_text = correct_article_errors(cleaned_text)
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# Correct spelling
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cleaned_text = correct_spelling(cleaned_text)
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# Correct punctuation issues
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cleaned_text = correct_punctuation(cleaned_text)
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# Handle possessives
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cleaned_text = handle_possessives(cleaned_text)
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# Replace words with synonyms
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cleaned_text = rephrase_with_synonyms(cleaned_text)
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# Correct double negatives
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cleaned_text = correct_double_negatives(cleaned_text)
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# Ensure subject-verb agreement
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cleaned_text = ensure_subject_verb_agreement(cleaned_text)
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return cleaned_text
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# Function to detect AI-generated content
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def detect_ai(text):
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label, score = predict_en(text)
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return label, score
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def gradio_interface(text):
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label, score = detect_ai(text)
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corrected_text = paraphrase_and_correct(text)
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return {label: score}, corrected_text
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#
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iface = gr.Interface(
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fn=gradio_interface,
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inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
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@@ -290,4 +317,4 @@ iface = gr.Interface(
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)
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# Launch the app
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iface.launch()
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import gradio as gr
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from transformers import pipeline
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import spacy
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import nltk
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from nltk.corpus import wordnet
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from spellchecker import SpellChecker
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import re
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import inflect
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# Initialize components
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try:
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nlp = spacy.load("en_core_web_sm")
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except OSError:
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spacy.cli.download("en_core_web_sm")
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nlp = spacy.load("en_core_web_sm")
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# Initialize the English text classification pipeline for AI detection
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pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
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nltk.download('wordnet')
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nltk.download('omw-1.4')
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# Function to predict the label and score for English text (AI Detection)
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def predict_en(text):
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res = pipeline_en(text)[0]
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corrected_text.append(token.text)
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return ' '.join(corrected_text)
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# Enhance the spell checker function
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def enhanced_spell_check(text):
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words = text.split()
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corrected_words = []
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for word in words:
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if '_' in word: # Handle cases like 'animate_being'
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sub_words = word.split('_')
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corrected_sub_words = [spell.correction(w) for w in sub_words]
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corrected_words.append('_'.join(corrected_sub_words))
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else:
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corrected_word = spell.correction(word)
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corrected_words.append(corrected_word if corrected_word else word)
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return ' '.join(corrected_words)
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# Function to correct common semantic errors
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def correct_semantic_errors(text):
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semantic_corrections = {
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"animate_being": "animal",
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"little": "smallest",
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"big": "largest",
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"mammalian": "mammals",
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"universe": "world",
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"manner": "ways",
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"continue": "preserve",
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"dirt": "soil",
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"wellness": "health",
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"modulate": "regulate",
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"clime": "climate",
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"function": "role",
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"keeping": "maintaining",
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"lend": "contribute",
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"better": "improve",
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"is": "s",
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"wite": "write",
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"alos": "also",
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"ads": "as",
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"dictuionatr": "dictionary",
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"wors": "words"
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}
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words = text.split()
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corrected_words = [semantic_corrections.get(word.lower(), word) for word in words]
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return ' '.join(corrected_words)
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# Enhance the punctuation correction function
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def enhance_punctuation(text):
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# Remove extra spaces before punctuation
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text = re.sub(r'\s+([?.!,";:])', r'\1', text)
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# Add space after punctuation if it's missing
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text = re.sub(r'([?.!,";:])(\S)', r'\1 \2', text)
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# Correct spacing for quotes
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text = re.sub(r'\s*"\s*', '" ', text).strip()
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# Ensure proper capitalization after sentence-ending punctuation
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text = re.sub(r'([.!?])\s*([a-z])', lambda m: m.group(1) + ' ' + m.group(2).upper(), text)
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return text
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# Function to handle possessives
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def handle_possessives(text):
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text = re.sub(r"\b(\w+)'s\b", r"\1's", text)
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return text
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return ' '.join(rephrased_text)
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# Function to detect AI-generated content
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def detect_ai(text):
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label, score = predict_en(text)
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return label, score
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# Enhance the paraphrase_and_correct function
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def paraphrase_and_correct(text):
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# Apply enhanced spell checking
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text = enhanced_spell_check(text)
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# Correct semantic errors
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text = correct_semantic_errors(text)
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# Apply existing corrections
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text = remove_redundant_words(text)
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text = capitalize_sentences_and_nouns(text)
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text = correct_tense_errors(text)
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text = correct_singular_plural_errors(text)
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text = correct_article_errors(text)
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text = enhance_punctuation(text)
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text = handle_possessives(text)
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text = rephrase_with_synonyms(text)
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text = correct_double_negatives(text)
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text = ensure_subject_verb_agreement(text)
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return text
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# Gradio interface setup
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def gradio_interface(text):
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label, score = detect_ai(text)
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corrected_text = paraphrase_and_correct(text)
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return {label: score}, corrected_text
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# Create Gradio interface
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iface = gr.Interface(
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fn=gradio_interface,
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inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
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)
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# Launch the app
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iface.launch()
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