Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+import gradio as grimport os
 import gradio as gr
 from transformers import pipeline
 import spacy
@@ -18,6 +19,275 @@ spell = SpellChecker()
 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 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:
+        if token.pos_ == "NOUN" and token.text.lower() == "earth":
+            rephrased_text.append("Earth")
+            continue
+        
+        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.lemma_, 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'
+                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)
+
+    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")