Update app.py

app.py

@@ -1,118 +1,118 @@
-# app.py — TYF Sadik AI Studio v2.0
-# Professional Paraphrasing • Grammar Correction • AI Detection
-# 🔗 https://tyfsadik.org | 💼 MD. Taki Yasir Faraji Sadik | 📍 North York, ON, Canada
-
-import os, re, string, random, time, json
+import os
 import gradio as gr
-import nltk
+from transformers import pipeline
 import spacy
 import subprocess
-from nltk.corpus import wordnet, stopwords
-from nltk.tokenize import word_tokenize, sent_tokenize
+import nltk
+from nltk.corpus import wordnet
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
 from spellchecker import SpellChecker
-
-# ─────────────────────────────────────────────────────────────
-# 🎨 BRANDING & METADATA — MAKE TYF SADIK FAMOUS
-# ─────────────────────────────────────────────────────────────
-BRANDING = {
-    "name": "TYF Sadik",
-    "full_name": "MD. Taki Yasir Faraji Sadik",
-    "title": "Cybersecurity & IT Professional",
-    "roles": ["SOC Analyst", "Network Analyst", "Cloud Infrastructure"],
-    "location": "North York, ON, Canada",
-    "education": "Diploma in Computer Networking & Cybersecurity — Seneca Polytechnic",
-    "certs": ["CompTIA A+", "Azure Fundamentals", "AWS Cloud Practitioner", "ISC2 CC"],
-    "website": "https://tyfsadik.org",
-    "github": "https://github.com/TYFSADIK",
-    "linkedin": "https://linkedin.com/in/tyfsadik",
-    "version": "v2.0-PRO",
-    "tagline": "Empowering Secure, Human-Centric AI Solutions"
-}
-
-# ─────────────────────────────────────────────────────────────
-# 📦 DEPENDENCIES & SETUP
-# ─────────────────────────────────────────────────────────────
-nltk.download('punkt', quiet=True)
-nltk.download('stopwords', quiet=True)
-nltk.download('averaged_perceptron_tagger', quiet=True)
-nltk.download('wordnet', quiet=True)
-nltk.download('omw-1.4', quiet=True)
-nltk.download('punkt_tab', quiet=True)
-
+import re
+import string
+import random
+
+# Download necessary NLTK data
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('averaged_perceptron_tagger_eng')
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+nltk.download('punkt_tab')
+
+# Initialize stopwords
 stop_words = set(stopwords.words("english"))
+
+# Words we don't want to replace
 exclude_tags = {'PRP', 'PRP$', 'MD', 'VBZ', 'VBP', 'VBD', 'VBG', 'VBN', 'TO', 'IN', 'DT', 'CC'}
 exclude_words = {'is', 'am', 'are', 'was', 'were', 'have', 'has', 'do', 'does', 'did', 'will', 'shall', 'should', 'would', 'could', 'can', 'may', 'might'}
 
-# Spell checker
+# 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()
 
-# SpaCy model
+# 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"], capture_output=True)
+    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
     nlp = spacy.load("en_core_web_sm")
 
-# ─────────────────────────────────────────────────────────────
-# ✨ CORE FUNCTIONS — ENHANCED & OPTIMIZED
-# ─────────────────────────────────────────────────────────────
 def plagiarism_removal(text):
-    """Replace words with synonyms while preserving grammar and style"""
-    def replace_word(word):
+    def plagiarism_remover(word):
         if word.lower() in stop_words or word.lower() in exclude_words or word in string.punctuation:
             return word
         
+        # Find synonyms
         synonyms = set()
         for syn in wordnet.synsets(word):
             for lemma in syn.lemmas():
-                name = lemma.name()
-                if "_" not in name and name.isalpha() and name.lower() != word.lower():
-                    synonyms.add(name)
-        
-        pos = nltk.pos_tag([word])[0][1]
-        if pos in exclude_tags:
+                if "_" not in lemma.name() and lemma.name().isalpha() and lemma.name().lower() != word.lower():
+                    synonyms.add(lemma.name())
+
+        pos_tag_word = nltk.pos_tag([word])[0]
+
+        if pos_tag_word[1] in exclude_tags:
             return word
         
-        filtered = [s for s in synonyms if nltk.pos_tag([s])[0][1] == pos]
-        if not filtered:
+        filtered_synonyms = [syn for syn in synonyms if nltk.pos_tag([syn])[0][1] == pos_tag_word[1]]
+
+        if not filtered_synonyms:
             return word
-        
-        choice = random.choice(filtered)
-        return choice.title() if word.istitle() else choice
 
-    tokens = word_tokenize(text)
-    result = [replace_word(t) for t in tokens]
+        synonym_choice = random.choice(filtered_synonyms)
+
+        if word.istitle():
+            return synonym_choice.title()
+        return synonym_choice
+
+    para_split = word_tokenize(text)
+    final_text = [plagiarism_remover(word) for word in para_split]
     
-    # Fix punctuation spacing
-    cleaned = []
-    for i, token in enumerate(result):
-        if token in string.punctuation and i > 0 and cleaned:
-            cleaned[-1] += token
+    corrected_text = []
+    for i in range(len(final_text)):
+        if final_text[i] in string.punctuation and i > 0:
+            corrected_text[-1] += final_text[i]  
         else:
-            cleaned.append(token)
-    
-    return " ".join(cleaned)
+            corrected_text.append(final_text[i])
+
+    return " ".join(corrected_text)
+
+def predict_en(text):
+    res = pipeline_en(text)[0]
+    return res['label'], res['score']
 
 def remove_redundant_words(text):
-    """Remove filler words that weaken writing"""
     doc = nlp(text)
-    fillers = {"actually", "basically", "literally", "really", "very", "just", "simply", "quite"}
-    return ' '.join([t.text for t in doc if t.text.lower() not in fillers])
+    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)
 
 def fix_punctuation_spacing(text):
-    """Ensure proper spacing around punctuation"""
-    text = re.sub(r'\s+([,.!?;:\'"])', r'\1', text)
-    text = re.sub(r'([,.!?;:\'"])\s+', r'\1 ', text)
-    return text.strip()
+    words = text.split(' ')
+    cleaned_words = []
+    punctuation_marks = {',', '.', "'", '!', '?', ':'}
+
+    for word in words:
+        if cleaned_words and word and word[0] in punctuation_marks:
+            cleaned_words[-1] += word
+        else:
+            cleaned_words.append(word)
+
+    return ' '.join(cleaned_words).replace(' ,', ',').replace(' .', '.').replace(" '", "'") \
+                                    .replace(' !', '!').replace(' ?', '?').replace(' :', ':')
 
 def fix_possessives(text):
-    """Correct possessive apostrophes"""
-    return re.sub(r"(\w)\s*'\s*s", r"\1's", text)
+    text = re.sub(r'(\w)\s\'\s?s', r"\1's", text)
+    return text
 
-def capitalize_properly(text):
-    """Capitalize sentences and proper nouns"""
+def capitalize_sentences_and_nouns(text):
     doc = nlp(text)
-    result = []
+    corrected_text = []
+
     for sent in doc.sents:
         sentence = []
         for token in sent:
@@ -122,324 +122,110 @@ def capitalize_properly(text):
                 sentence.append(token.text.capitalize())
             else:
                 sentence.append(token.text)
-        result.append(' '.join(sentence))
-    return ' '.join(result)
-
-def ensure_proper_sentences(text):
-    """Ensure every sentence ends with punctuation and starts capitalized"""
-    sentences = re.split(r'(?<=[.!?])\s+', text.strip())
-    fixed = []
-    for s in sentences:
-        if s:
-            s = s[0].upper() + s[1:] if len(s) > 1 else s.upper()
-            if not re.search(r'[.!?]$', s):
-                s += '.'
-            fixed.append(s)
-    return ' '.join(fixed)
-
-def correct_articles(text):
-    """Fix a/an usage"""
+        corrected_text.append(' '.join(sentence))
+
+    return ' '.join(corrected_text)
+
+def force_first_letter_capital(text):
+    sentences = re.split(r'(?<=\w[.!?])\s+', text)
+    capitalized_sentences = []
+    
+    for sentence in sentences:
+        if sentence:
+            capitalized_sentence = sentence[0].capitalize() + sentence[1:]
+            if not re.search(r'[.!?]$', capitalized_sentence):
+                capitalized_sentence += '.'
+            capitalized_sentences.append(capitalized_sentence)
+    
+    return " ".join(capitalized_sentences)
+
+def correct_tense_errors(text):
     doc = nlp(text)
-    result = []
+    corrected_text = []
     for token in doc:
-        if token.text.lower() in ['a', 'an'] and token.nbor(1, default=None):
-            next_word = token.nbor(1).text.lower()
-            if token.text.lower() == "a" and next_word[0] in "aeiou":
-                result.append("An" if token.text[0].isupper() else "an")
-            elif token.text.lower() == "an" and next_word[0] not in "aeiou":
-                result.append("A" if token.text[0].isupper() else "a")
+        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)
+
+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:
-                result.append(token.text)
+                corrected_text.append(token.text)
         else:
-            result.append(token.text)
-    return ' '.join(result)
+            corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
+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":
+                corrected_text.append(token.head.lemma_ + "s")
+            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":
+                corrected_text.append(token.head.lemma_)
+        corrected_text.append(token.text)
+    return ' '.join(corrected_text)
 
 def correct_spelling(text):
-    """Basic spell correction using pyspellchecker"""
     words = word_tokenize(text)
-    corrected = []
+    corrected_words = []
+
     for word in words:
-        if word in string.punctuation or word.isdigit():
-            corrected.append(word)
+        corrected_word = spell.candidates(word)
+        if corrected_word:
+            corrected_words.append(spell.candidates(word).pop())  # Choose the first candidate as the correction
         else:
-            candidates = spell.candidates(word)
-            corrected.append(candidates.pop() if candidates else word)
-    return ' '.join(corrected)
-
-def full_pipeline(text):
-    """Execute complete paraphrasing + correction pipeline"""
-    if not text or len(text.strip()) < 10:
-        return "[!] Please enter at least 10 characters of text."
-    
-    # Process paragraph by paragraph to preserve structure
-    paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
-    results = []
-    
-    for para in paragraphs:
-        cleaned = remove_redundant_words(para)
-        synonymed = plagiarism_removal(cleaned)
-        capped = capitalize_properly(synonymed)
-        sent_fixed = ensure_proper_sentences(capped)
-        articles_fixed = correct_articles(sent_fixed)
-        spelling_fixed = correct_spelling(articles_fixed)
-        possessives_fixed = fix_possessives(spelling_fixed)
-        punct_fixed = fix_punctuation_spacing(possessives_fixed)
-        results.append(punct_fixed)
-    
-    return '\n\n'.join(results)
-
-def get_text_stats(text):
-    """Generate useful metrics about the text"""
-    if not text:
-        return {}
-    words = word_tokenize(text)
-    sents = sent_tokenize(text)
-    return {
-        "words": len([w for w in words if w.isalpha()]),
-        "sentences": len(sents),
-        "avg_sentence_length": round(len(words) / max(len(sents), 1), 1),
-        "unique_words": len(set(w.lower() for w in words if w.isalpha())),
-        "readability": "Easy" if len(words) < 100 else "Medium" if len(words) < 300 else "Advanced"
-    }
-
-# ─────────────────────────────────────────────────────────────
-# 🎨 CYBERPUNK TERMINAL UI — TYF SADIK STYLE
-# ─────────────────────────────────────────────────────────────
-TERMINAL_CSS = f"""
-@import url('https://fonts.googleapis.com/css2?family=Share+Tech+Mono&family=Orbitron:wght@700;900&display=swap');
-*, body, .gradio-container {{ 
-    font-family: 'Share Tech Mono', monospace !important; 
-    background: #030803 !important; 
-    color: #00ff41 !important; 
-}}
-.gr-button {{ 
-    font-family: 'Orbitron', monospace !important; 
-    text-transform: uppercase !important;
-    letter-spacing: 2px !important; 
-    font-weight: 700 !important; 
-    border-radius: 2px !important; 
-    transition: all 0.15s ease !important;
-}}
-.gr-button-primary {{ 
-    background: #002200 !important; 
-    border: 1px solid #00ff41 !important;
-    color: #00ff41 !important; 
-}}
-.gr-button-primary:hover {{ 
-    background: #00ff41 !important; 
-    color: #000 !important;
-    box-shadow: 0 0 22px rgba(0,255,65,0.7) !important; 
-    transform: translateY(-1px) !important;
-}}
-.gr-button-secondary {{ 
-    background: #000 !important; 
-    border: 1px solid #004400 !important; 
-    color: #00aa33 !important; 
-}}
-.gr-button-secondary:hover {{ 
-    border-color: #00ff41 !important; 
-    color: #00ff41 !important; 
-}}
-label, .gr-form-label {{ 
-    color: #00cc44 !important; 
-    text-transform: uppercase !important;
-    letter-spacing: 1px !important; 
-    font-size: 0.8em !important; 
-}}
-textarea, input, select {{ 
-    background: #000 !important; 
-    color: #00ff41 !important;
-    border: 1px solid #003300 !important; 
-    border-radius: 2px !important; 
-    font-family: inherit !important;
-}}
-textarea:focus, input:focus, select:focus {{ 
-    border-color: #00ff41 !important;
-    box-shadow: 0 0 12px rgba(0,255,65,0.25) !important; 
-}}
-.gr-panel, .gr-box, .gr-form {{ 
-    background: rgba(0,12,0,0.92) !important; 
-    border: 1px solid #001a00 !important; 
-}}
-.gr-accordion {{ 
-    background: rgba(0,8,0,0.95) !important; 
-    border: 1px solid #001a00 !important; 
-}}
-footer {{ display: none !important; }}
-::-webkit-scrollbar {{ width: 5px; background: #000; }}
-::-webkit-scrollbar-thumb {{ background: #003300; border-radius: 2px; }}
-::-webkit-scrollbar-thumb:hover {{ background: #00ff41; }}
-.brand-highlight {{ color: #00ff41 !important; font-weight: bold; }}
-.cert-badge {{ 
-    display: inline-block; 
-    background: #001a00; 
-    border: 1px solid #004400; 
-    padding: 2px 8px; 
-    margin: 2px; 
-    border-radius: 2px; 
-    font-size: 0.75em; 
-}}
-"""
-
-def create_interface():
-    with gr.Blocks(title=f"{BRANDING['name']} AI Studio • {BRANDING['version']}", css=TERMINAL_CSS) as demo:
-        
-        # ─── HEADER: PERSONAL BRANDING ───
-        gr.HTML(f"""
-        <div style="text-align:center; padding:24px 0 16px 0; border-bottom:1px solid #002200; margin-bottom:16px;">
-            <div style="font-family:'Orbitron',monospace; font-size:2.4em; font-weight:900;
-                        color:#00ff41; letter-spacing:4px; text-shadow:0 0 12px rgba(0,255,65,0.5);">
-                {BRANDING['name'].upper()}
-            </div>
-            <div style="font-family:'Orbitron',monospace; font-size:1.1em; color:#00cc44;
-                        margin-top:4px; letter-spacing:3px;">{BRANDING['version']} • {BRANDING['tagline']}</div>
-            <div style="color:#005522; font-size:0.82em; margin-top:12px; line-height:1.9;">
-                <span class="brand-highlight">{BRANDING['full_name']}</span><br>
-                {BRANDING['title']} • {' | '.join(BRANDING['roles'])}<br>
-                🎓 {BRANDING['education']}<br>
-                {' '.join(f'<span class="cert-badge">{c}</span>' for c in BRANDING['certs'])}<br>
-                📍 {BRANDING['location']}<br><br>
-                🔗 <a href="{BRANDING['website']}" target="_blank" style="color:#00aa44; text-decoration:none;">{BRANDING['website']}</a>
-                &nbsp;&nbsp;
-                💻 <a href="{BRANDING['github']}" target="_blank" style="color:#00aa44; text-decoration:none;">{BRANDING['github']}</a>
-                &nbsp;&nbsp;
-                🔗 <a href="{BRANDING['linkedin']}" target="_blank" style="color:#00aa44; text-decoration:none;">LinkedIn</a>
-            </div>
-        </div>""")
-        
-        # ─── TAB 1: PARAPHRASING & GRAMMAR CORRECTION ───
-        with gr.Tab("✍️ Paraphrase & Correct"):
-            gr.HTML("<div style='color:#005522; font-size:0.78em; letter-spacing:2px; text-transform:uppercase; margin-bottom:8px;'>[INPUT] Your Text</div>")
-            input_text = gr.Textbox(
-                label="", 
-                placeholder="Paste your text here...\n\n• Minimum 10 characters\n• Supports paragraphs\n• Academic, professional, or casual content", 
-                lines=12,
-                elem_classes="terminal-input"
-            )
-            
-            with gr.Row():
-                process_btn = gr.Button("▶ EXECUTE PARAPHRASE", variant="primary", size="lg")
-                clear_btn = gr.Button("🗑 Clear", variant="secondary", size="sm")
-            
-            gr.HTML("<div style='color:#005522; font-size:0.78em; letter-spacing:2px; text-transform:uppercase; margin:16px 0 8px 0;'>[OUTPUT] Enhanced Text</div>")
-            output_text = gr.Textbox(label="", lines=12, interactive=False, elem_classes="terminal-output")
-            
-            with gr.Row():
-                copy_btn = gr.Button("📋 Copy Result", variant="secondary", size="sm")
-                stats_btn = gr.Button("📊 View Stats", variant="secondary", size="sm")
-            
-            stats_output = gr.JSON(label="Text Statistics", visible=False)
-            
-            # Examples to showcase quality
-            with gr.Accordion("✨ Example Inputs", open=False):
-                gr.Examples(
-                    examples=[
-                        "I am excited to apply for the Support Engineer position. As a cybersecurity professional, I bring hands-on experience in troubleshooting and customer support.",
-                        "Furthermore, it is important to note that the implementation of this strategy will facilitate substantial improvements in operational efficiency.",
-                        "The company utilized advanced algorithms to leverage big data analytics, consequently enhancing their decision-making processes."
-                    ],
-                    inputs=[input_text],
-                    label="Click to load example"
-                )
-        
-        # ─── TAB 2: AI DETECTION (Placeholder for future expansion) ───
-        with gr.Tab("🤖 AI Detection"):
-            gr.HTML("""
-            <div style="text-align:center; padding:40px; color:#006633;">
-                <div style="font-size:1.2em; margin-bottom:12px;">🔒 Advanced AI Detection Module</div>
-                <div style="margin-bottom:20px;">Coming soon in v2.1 — Powered by custom ensemble models</div>
-                <div style="font-size:0.85em; color:#004422;">
-                    Follow <a href="https://github.com/TYFSADIK" target="_blank" style="color:#00aa44;">@TYFSADIK</a> 
-                    for updates on new features!
-                </div>
-            </div>""")
-            detect_input = gr.Textbox(label="Text to Analyze", lines=5, interactive=False)
-            detect_btn = gr.Button("🔍 Analyze (Coming Soon)", interactive=False)
-            detect_output = gr.Textbox(label="Result", interactive=False)
-        
-        # ─── FOOTER: CALL TO ACTION & CREDITS ───
-        gr.HTML(f"""
-        <div style="background:#000800; border:1px solid #001a00; border-radius:2px; padding:20px; margin:24px 0; text-align:center; font-family:'Share Tech Mono',monospace;">
-            <div style="color:#004422; font-family:'Orbitron',monospace; font-size:0.85em; margin-bottom:12px; letter-spacing:2px;">
-                🔗 CONNECT WITH TYF SADIK
-            </div>
-            <div style="display:flex; flex-wrap:wrap; justify-content:center; gap:12px; margin-bottom:16px;">
-                <a href="{BRANDING['website']}" target="_blank" style="background:#000; color:#00ff41; border:1px solid #004400; border-radius:2px; padding:8px 16px; text-decoration:none; font-weight:bold;">🌐 {BRANDING['website']}</a>
-                <a href="{BRANDING['github']}" target="_blank" style="background:#000; color:#00ff41; border:1px solid #004400; border-radius:2px; padding:8px 16px; text-decoration:none; font-weight:bold;">💻 GitHub</a>
-                <a href="{BRANDING['linkedin']}" target="_blank" style="background:#000; color:#00ff41; border:1px solid #004400; border-radius:2px; padding:8px 16px; text-decoration:none; font-weight:bold;">🔗 LinkedIn</a>
-            </div>
-            <div style="color:#003311; font-size:0.80em; line-height:1.8;">
-                {BRANDING['full_name']} ({BRANDING['name']}) • {BRANDING['location']}<br>
-                Cybersecurity & IT Professional • SOC/Network Analyst • Cloud Infrastructure<br>
-                <span style="color:#002208;">Educational Tool • Open Source • Use Responsibly</span><br>
-                <span style="color:#004422; font-size:0.85em;">⭐ If you found this useful, star the repo on GitHub!</span>
-            </div>
-        </div>""")
-        
-        # ─── EVENT HANDLERS ───
-        def process_with_stats(text):
-            result = full_pipeline(text)
-            stats = get_text_stats(result)
-            return result, stats, gr.update(visible=True)
-        
-        process_btn.click(
-            fn=process_with_stats,
-            inputs=[input_text],
-            outputs=[output_text, stats_output, stats_output]
-        )
-        
-        input_text.submit(
-            fn=process_with_stats,
-            inputs=[input_text],
-            outputs=[output_text, stats_output, stats_output]
-        )
-        
-        clear_btn.click(
-            fn=lambda: ("", None, gr.update(visible=False)),
-            inputs=[],
-            outputs=[output_text, stats_output, stats_output]
-        )
-        
-        copy_btn.click(
-            fn=None,
-            inputs=[output_text],
-            outputs=[],
-            js="""
-            async (text) => {
-                if (!text) return ['✗ Nothing to copy'];
-                try {
-                    await navigator.clipboard.writeText(text);
-                    return ['✓ Copied to clipboard!'];
-                } catch (err) {
-                    return ['✗ Copy failed'];
-                }
-            }
-            """
-        )
-        
-        stats_btn.click(
-            fn=lambda t: (get_text_stats(t), gr.update(visible=True)) if t else (None, gr.update(visible=False)),
-            inputs=[output_text],
-            outputs=[stats_output, stats_output]
-        )
-    
-    return demo
-
-# ─────────────────────────────────────────────────────────────
-# 🚀 LAUNCH
-# ─────────────────────────────────────────────────────────────
-if __name__ == "__main__":
-    print(f"\n{'='*60}")
-    print(f"  {BRANDING['name']} AI Studio • {BRANDING['version']}")
-    print(f"  {BRANDING['tagline']}")
-    print(f"  🔗 {BRANDING['website']}")
-    print(f"{'='*60}\n")
-    
-    app = create_interface()
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        quiet=True,
-        share=False  # Set to True for public link
-    )
+            corrected_words.append(word)  # If it's not misspelled, keep the original word
+
+    return ' '.join(corrected_words)
+
+def paraphrase_and_correct(text):
+    paragraphs = text.split("\n\n")  # Split by paragraphs
+
+    # Process each paragraph separately
+    processed_paragraphs = []
+    for paragraph in paragraphs:
+        cleaned_text = remove_redundant_words(paragraph)
+        plag_removed = plagiarism_removal(cleaned_text)
+        paraphrased_text = capitalize_sentences_and_nouns(plag_removed)
+        paraphrased_text = force_first_letter_capital(paraphrased_text)
+        paraphrased_text = correct_article_errors(paraphrased_text)
+        paraphrased_text = correct_tense_errors(paraphrased_text)
+        paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
+        paraphrased_text = fix_possessives(paraphrased_text)
+        paraphrased_text = correct_spelling(paraphrased_text)  # Spelling correction
+        paraphrased_text = fix_punctuation_spacing(paraphrased_text)
+        processed_paragraphs.append(paraphrased_text)
+
+    return "\n\n".join(processed_paragraphs)  # Reassemble the text with paragraphs
+
+# Gradio app setup
+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')
+
+        button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
+
+    with gr.Tab("Paraphrasing & Grammar Correction"):
+        t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
+        button2 = gr.Button("🔄 Paraphrase and Correct")
+        result2 = gr.Textbox(lines=5, label='Corrected Text')
+
+        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
+
+demo.launch(share=True)