Update app.py

app.py

@@ -5,36 +5,32 @@ import spacy
 import subprocess
 import nltk
 from nltk.corpus import wordnet
-from gramformer import Gramformer
+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")
 
-# 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']
+# Initialize the spell checker
+spell = SpellChecker()
 
-# Ensure necessary NLTK data is downloaded for Humanifier
+# Ensure necessary NLTK data is downloaded
 nltk.download('wordnet')
 nltk.download('omw-1.4')
 
-# Ensure the SpaCy model is installed for Humanifier
+# 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")
 
-# Initialize Gramformer for grammar correction
-gf = Gramformer(models=1, use_gpu=False)  # You can set use_gpu=True if running on a machine with a GPU
-
-# Function to correct grammar using Gramformer
-def correct_grammar(text):
-    corrections = gf.correct(text)
-    return ' '.join(corrections)
+# 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 (Humanifier)
+# Function to get synonyms using NLTK WordNet
 def get_synonyms_nltk(word, pos):
     synsets = wordnet.synsets(word, pos=pos)
     if synsets:
@@ -42,7 +38,14 @@ def get_synonyms_nltk(word, pos):
         return [lemma.name() for lemma in lemmas]
     return []
 
-# Function to capitalize the first letter of sentences and proper nouns (Humanifier)
+# 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 = []
@@ -60,75 +63,200 @@ def capitalize_sentences_and_nouns(text):
 
     return ' '.join(corrected_text)
 
-# Paraphrasing function using SpaCy and NLTK (Humanifier)
-def paraphrase_with_spacy_nltk(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)
-    paraphrased_words = []
+    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:
-        # Map SpaCy POS tags to WordNet POS tags
-        pos = None
-        if token.pos_ in {"NOUN"}:
-            pos = wordnet.NOUN
-        elif token.pos_ in {"VERB"}:
-            pos = wordnet.VERB
-        elif token.pos_ in {"ADJ"}:
-            pos = wordnet.ADJ
-        elif token.pos_ in {"ADV"}:
-            pos = wordnet.ADV
-        
-        synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else []
-        
-        # Replace with a synonym only if it makes sense
-        if synonyms and token.pos_ in {"NOUN", "VERB", "ADJ", "ADV"} and synonyms[0] != token.text.lower():
-            paraphrased_words.append(synonyms[0])
+        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:
-            paraphrased_words.append(token.text)
+            corrected_text.append(token.text)
     
-    # Join the words back into a sentence
-    paraphrased_sentence = ' '.join(paraphrased_words)
+    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
     
-    # Capitalize sentences and proper nouns
-    corrected_text = capitalize_sentences_and_nouns(paraphrased_sentence)
+    synonyms = get_synonyms_nltk(token.lemma_, pos)
     
-    return corrected_text
+    if synonyms:
+        synonym = synonyms[0]
+        if token.tag_ == "VBG":  # Present participle
+            synonym += 'ing'
+        elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
+            synonym += 'ed'
+        elif token.tag_ == "VBZ":  # Third-person singular present
+            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)
 
-# Combined function: Paraphrase -> Capitalization (Humanifier)
+# 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)
+    return ' '.join(corrected_words)
+
+# Function to correct punctuation issues
+def correct_punctuation(text):
+    # Fix spacing before punctuation
+    text = re.sub(r'\s([?.!,";:])', r'\1', text)
+    # Add missing commas in compound sentences
+    text = re.sub(r'(\w+)\s+and\s+(\w+)', r'\1, and \2', text)
+    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
+                        synonym += 'ing'
+                    elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
+                        synonym += 'ed'
+                    elif token.tag_ == "VBZ":  # Third-person singular present
+                        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):
-    # Step 1: Paraphrase the text
-    paraphrased_text = paraphrase_with_spacy_nltk(text)
+    # Remove meaningless or redundant words first
+    cleaned_text = remove_redundant_words(text)
     
-    # Step 2: Capitalize sentences and proper nouns
-    final_text = capitalize_sentences_and_nouns(paraphrased_text)
-    
-    return final_text
+    # Capitalize sentences and nouns
+    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
 
-# Gradio app setup with three tabs
-with gr.Blocks() as demo:
-    with gr.Tab("AI Detection"):
-        t1 = gr.Textbox(lines=5, label='Text')
-        button1 = gr.Button("🤖 Predict!")
-        label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
-        score1 = gr.Textbox(lines=1, label='Prob')
+    # 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)
 
-        # Connect the prediction function to the button
-        button1.click(predict_en, inputs=[t1], outputs=[label1, score1], api_name='predict_en')
-    
-    with gr.Tab("Humanifier"):
-        text_input = gr.Textbox(lines=5, label="Input Text")
-        paraphrase_button = gr.Button("Paraphrase & Correct")
-        output_text = gr.Textbox(label="Paraphrased Text")
+    # Correct spelling and punctuation
+    paraphrased_text = correct_spelling(paraphrased_text)
+    paraphrased_text = correct_punctuation(paraphrased_text)
 
-        # Connect the paraphrasing function to the button
-        paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
-    
-    with gr.Tab("Grammar Correction"):
-        grammar_input = gr.Textbox(lines=5, label="Input Text")
-        grammar_button = gr.Button("Correct Grammar")
-        grammar_output = gr.Textbox(label="Corrected Text")
+    # 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
 
-        # Connect the grammar correction function to the button
-        grammar_button.click(correct_grammar, inputs=grammar_input, outputs=grammar_output)
+# Gradio interface
+iface = gr.Interface(
+    fn=paraphrase_and_correct,
+    inputs=gr.Textbox(label="Input Text", lines=5, placeholder="Enter text here..."),
+    outputs=gr.Textbox(label="Output Text", lines=5),
+    title="Text Paraphraser and Grammar Corrector",
+    description="This tool paraphrases the input text, corrects grammar, improves punctuation, and enhances overall clarity."
+)
 
-# Launch the app with all functionalities
-demo.launch()
+iface.launch()