Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-import gradio as grimport os
 import gradio as gr
 from transformers import pipeline
 import spacy
@@ -247,280 +246,7 @@ def paraphrase_and_correct(text):
     paraphrased_text = rephrase_with_synonyms(paraphrased_text)
 
     # Force capitalization of the first letter of each sentence
-    final_text = force_first_letter_capital(paraphrased_text)
-
-    return final_text
-
-# Gradio Interface
-def process_text(input_text):
-    ai_label, ai_score = predict_en(input_text)
-    corrected_text = paraphrase_and_correct(input_text)
-    return ai_label, ai_score, corrected_text
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=process_text,
-    inputs="text",
-    outputs=["text", "number", "text"],
-    title="AI Content Detection and Grammar Correction",
-    description="Enter text to detect AI-generated content and correct grammar."
-)
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    iface.launch()
-
-from transformers import pipeline
-import spacy
-import subprocess
-import nltk
-from nltk.corpus import wordnet
-from spellchecker import SpellChecker
-import re
-
-# Initialize the English text classification pipeline for AI detection
-pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
-
-# Initialize the spell checker
-spell = SpellChecker()
-
-# Ensure necessary NLTK data is downloaded
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-
-# Ensure the SpaCy model is installed
-try:
-    nlp = spacy.load("en_core_web_sm")
-except OSError:
-    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
-    nlp = spacy.load("en_core_web_sm")
-
-# Function to predict the label and score for English text (AI Detection)
-def predict_en(text):
-    res = pipeline_en(text)[0]
-    return res['label'], res['score']
-
-# Function to get synonyms using NLTK WordNet
-def get_synonyms_nltk(word, pos):
-    synsets = wordnet.synsets(word, pos=pos)
-    if synsets:
-        lemmas = synsets[0].lemmas()
-        return [lemma.name() for lemma in lemmas]
-    return []
-
-# Function to remove redundant and meaningless words
-def remove_redundant_words(text):
-    doc = nlp(text)
-    meaningless_words = {"actually", "basically", "literally", "really", "very", "just"}
-    filtered_text = [token.text for token in doc if token.text.lower() not in meaningless_words]
-    return ' '.join(filtered_text)
-
-# Function to capitalize the first letter of sentences and proper nouns
-def capitalize_sentences_and_nouns(text):
-    doc = nlp(text)
-    corrected_text = []
-
-    for sent in doc.sents:
-        sentence = []
-        for token in sent:
-            if token.i == sent.start:  # First word of the sentence
-                sentence.append(token.text.capitalize())
-            elif token.pos_ == "PROPN":  # Proper noun
-                sentence.append(token.text.capitalize())
-            else:
-                sentence.append(token.text)
-        corrected_text.append(' '.join(sentence))
-
-    return ' '.join(corrected_text)
-
-# Function to force capitalization of the first letter of every sentence
-def force_first_letter_capital(text):
-    sentences = text.split(". ")  # Split by period to get each sentence
-    capitalized_sentences = [sentence[0].capitalize() + sentence[1:] if sentence else "" for sentence in sentences]
-    return ". ".join(capitalized_sentences)
-
-# Function to correct tense errors in a sentence
-def correct_tense_errors(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
-            lemma = wordnet.morphy(token.text, wordnet.VERB) or token.text
-            corrected_text.append(lemma)
-        else:
-            corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to correct singular/plural errors
-def correct_singular_plural_errors(text):
-    doc = nlp(text)
-    corrected_text = []
-    
-    for token in doc:
-        if token.pos_ == "NOUN":
-            if token.tag_ == "NN":  # Singular noun
-                if any(child.text.lower() in ['many', 'several', 'few'] for child in token.head.children):
-                    corrected_text.append(token.lemma_ + 's')
-                else:
-                    corrected_text.append(token.text)
-            elif token.tag_ == "NNS":  # Plural noun
-                if any(child.text.lower() in ['a', 'one'] for child in token.head.children):
-                    corrected_text.append(token.lemma_)
-                else:
-                    corrected_text.append(token.text)
-        else:
-            corrected_text.append(token.text)
-    
-    return ' '.join(corrected_text)
-
-# Function to check and correct article errors
-def correct_article_errors(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.text in ['a', 'an']:
-            next_token = token.nbor(1)
-            if token.text == "a" and next_token.text[0].lower() in "aeiou":
-                corrected_text.append("an")
-            elif token.text == "an" and next_token.text[0].lower() not in "aeiou":
-                corrected_text.append("a")
-            else:
-                corrected_text.append(token.text)
-        else:
-            corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to get the correct synonym while maintaining verb form
-def replace_with_synonym(token):
-    pos = None
-    if token.pos_ == "VERB":
-        pos = wordnet.VERB
-    elif token.pos_ == "NOUN":
-        pos = wordnet.NOUN
-    elif token.pos_ == "ADJ":
-        pos = wordnet.ADJ
-    elif token.pos_ == "ADV":
-        pos = wordnet.ADV
-    
-    synonyms = get_synonyms_nltk(token.lemma_, pos)
-    
-    if synonyms:
-        synonym = synonyms[0]
-        if token.tag_ == "VBG":  # Present participle (e.g., running)
-            synonym = synonym + 'ing'
-        elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
-            synonym = synonym + 'ed'
-        elif token.tag_ == "VBZ":  # Third-person singular present
-            synonym = synonym + 's'
-        return synonym
-    return token.text
-
-# Function to check for and avoid double negatives
-def correct_double_negatives(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
-            corrected_text.append("always")
-        else:
-            corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to ensure subject-verb agreement
-def ensure_subject_verb_agreement(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.dep_ == "nsubj" and token.head.pos_ == "VERB":
-            if token.tag_ == "NN" and token.head.tag_ != "VBZ":  # Singular noun, should use singular verb
-                corrected_text.append(token.head.lemma_ + "s")
-            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
-                corrected_text.append(token.head.lemma_)
-        corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to correct spelling errors
-def correct_spelling(text):
-    words = text.split()
-    corrected_words = []
-    for word in words:
-        corrected_word = spell.correction(word)
-        corrected_words.append(corrected_word if corrected_word else word)  # Keep original if correction is None
-    return ' '.join(corrected_words)
-
-# Function to correct punctuation issues
-def correct_punctuation(text):
-    text = re.sub(r'\s+([?.!,";:])', r'\1', text)  # Remove space before punctuation
-    text = re.sub(r'([?.!,";:])\s+', r'\1 ', text)  # Ensure a single space after punctuation
-    return text
-
-# Function to ensure correct handling of possessive forms
-def handle_possessives(text):
-    text = re.sub(r"\b(\w+)'s\b", r"\1's", text)  # Preserve possessive forms
-    return text
-
-# Function to rephrase text and replace words with their synonyms while maintaining form
-def rephrase_with_synonyms(text):
-    doc = nlp(text)
-    rephrased_text = []
-
-    for token in doc:
-        pos_tag = None
-        if token.pos_ == "NOUN":
-            pos_tag = wordnet.NOUN
-        elif token.pos_ == "VERB":
-            pos_tag = wordnet.VERB
-        elif token.pos_ == "ADJ":
-            pos_tag = wordnet.ADJ
-        elif token.pos_ == "ADV":
-            pos_tag = wordnet.ADV
-        
-        if pos_tag:
-            synonyms = get_synonyms_nltk(token.text, pos_tag)
-            if synonyms:
-                synonym = synonyms[0]  # Just using the first synonym for simplicity
-                if token.pos_ == "VERB":
-                    if token.tag_ == "VBG":  # Present participle (e.g., running)
-                        synonym = synonym + 'ing'
-                    elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
-                        synonym = synonym + 'ed'
-                    elif token.tag_ == "VBZ":  # Third-person singular present
-                        synonym = synonym + 's'
-                elif token.pos_ == "NOUN" and token.tag_ == "NNS":  # Plural nouns
-                    synonym += 's' if not synonym.endswith('s') else ""
-                rephrased_text.append(synonym)
-            else:
-                rephrased_text.append(token.text)
-        else:
-            rephrased_text.append(token.text)
-
-    return ' '.join(rephrased_text)
-
-# Function to paraphrase and correct grammar with enhanced accuracy
-def paraphrase_and_correct(text):
-    # Remove meaningless or redundant words first
-    cleaned_text = remove_redundant_words(text)
-    
-    # Capitalize sentences and nouns
-    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
-
-    # Correct tense and singular/plural errors
-    paraphrased_text = correct_tense_errors(paraphrased_text)
-    paraphrased_text = correct_singular_plural_errors(paraphrased_text)
-    paraphrased_text = correct_article_errors(paraphrased_text)
-    paraphrased_text = correct_double_negatives(paraphrased_text)
-    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
-
-    # Correct spelling and punctuation
-    paraphrased_text = correct_spelling(paraphrased_text)
-    paraphrased_text = correct_punctuation(paraphrased_text)
-    paraphrased_text = handle_possessives(paraphrased_text)  # Handle possessives
-
-    # Rephrase with synonyms
-    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
-
-    # Force capitalization of the first letter of each sentence
-    final_text = force_first_letter_capital(paraphrased_text)
+    final_text = capitalize_sentences_and_nouns(paraphrased_text)
 
     return final_text