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.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,6 @@
 ---
-title: Humanizer Pro
-emoji: 🌍
-colorFrom: indigo
-colorTo: gray
+title: Humanizer_Pro
+app_file: pipeline.py
 sdk: gradio
 sdk_version: 6.9.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

a.py

@@ -0,0 +1,239 @@
+"""
+Combined Humanizer V2 - Adversarial Model + StealthWriter Post-Processor
+Optimized for bypassing AI detectors using proven techniques.
+"""
+
+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import re
+import random
+import os
+
+# StealthWriter-style post-processor
+class StealthPostProcessor:
+    """Post-process text using StealthWriter's proven approach."""
+    
+    CONTRACTION_EXPANSIONS = {
+        "it's": "it is", "It's": "It is", "don't": "do not", "Don't": "Do not",
+        "doesn't": "does not", "Doesn't": "Does not", "didn't": "did not",
+        "won't": "will not", "wouldn't": "would not", "couldn't": "could not",
+        "shouldn't": "should not", "can't": "cannot", "Can't": "Cannot",
+        "I'm": "I am", "I've": "I have", "I'll": "I will", "I'd": "I would",
+        "you're": "you are", "You're": "You are", "you've": "you have",
+        "we're": "we are", "We're": "We are", "we've": "we have",
+        "they're": "they are", "They're": "They are", "they've": "they have",
+        "that's": "that is", "That's": "That is", "there's": "there is",
+        "what's": "what is", "who's": "who is", "let's": "let us",
+        "isn't": "is not", "aren't": "are not", "wasn't": "was not",
+        "weren't": "were not", "haven't": "have not", "hasn't": "has not",
+        "hadn't": "had not", "here's": "here is", "he's": "he is",
+        "she's": "she is", "we'll": "we will", "they'll": "they will",
+        "gotta": "got to", "gonna": "going to", "wanna": "want to",
+        "kinda": "kind of", "sorta": "sort of",
+    }
+    
+    EMPHATIC_PHRASES = [", I tell you", ", I must say", ", mind you", ", you see", ", indeed"]
+    
+    FORMAL_STARTERS = [
+        "It is almost a given that ", "One must acknowledge that ",
+        "It goes without saying that ", "It is worth noting that ",
+        "As it happens, ", "As a matter of fact, ", "In point of fact, ",
+    ]
+    
+    SYNONYM_REPLACEMENTS = {
+        "furry friend": "hairy companion", "pet": "animal companion",
+        "dog": "canine", "cat": "feline", "help": "assist", "use": "utilize",
+        "get": "obtain", "make": "create", "good": "favorable", "bad": "unfavorable",
+        "big": "substantial", "small": "modest", "very": "quite", "really": "truly",
+        "important": "significant", "need": "require", "want": "desire",
+        "think": "believe", "know": "understand", "see": "observe",
+        "find": "discover", "show": "demonstrate", "give": "provide",
+        "start": "commence", "begin": "initiate", "end": "conclude",
+        "try": "attempt", "keep": "maintain", "lot of": "numerous",
+        "a lot": "considerably", "lots of": "a great many",
+    }
+    
+    FILLERS_TO_REMOVE = [
+        "like, ", ", like,", " like ", "you know, ", ", you know,",
+        "basically, ", ", basically,", "honestly, ", "Honestly, ",
+        "I mean, ", ", I mean,", "pretty much ", "kind of ", "sort of ",
+        "actually, ", ", actually,", "literally ", "just ", "really ",
+        "so, ", "So, ", "well, ", "Well, ", "anyway, ", "Anyway, ",
+        "right? ", "Right? ", "you know? ", "I guess ", "I gotta say, ",
+    ]
+    
+    def __init__(self, intensity="high"):
+        self.change_probability = {"low": 0.3, "medium": 0.5, "high": 0.7}.get(intensity, 0.7)
+    
+    def expand_contractions(self, text):
+        for contraction, expansion in self.CONTRACTION_EXPANSIONS.items():
+            pattern = re.compile(r'\b' + re.escape(contraction) + r'\b')
+            text = pattern.sub(expansion, text)
+        return text
+    
+    def remove_casual_fillers(self, text):
+        for filler in self.FILLERS_TO_REMOVE:
+            text = text.replace(filler, " " if filler.startswith(" ") or filler.endswith(" ") else "")
+        return re.sub(r'\s+', ' ', text).strip()
+    
+    def apply_synonym_replacements(self, text):
+        for common, formal in self.SYNONYM_REPLACEMENTS.items():
+            if random.random() < self.change_probability:
+                pattern = re.compile(r'\b' + re.escape(common) + r'\b', re.IGNORECASE)
+                def replace_preserve_case(match):
+                    word = match.group(0)
+                    if word.isupper(): return formal.upper()
+                    elif word[0].isupper(): return formal.capitalize()
+                    return formal
+                text = pattern.sub(replace_preserve_case, text)
+        return text
+    
+    def add_emphatic_phrases(self, text):
+        sentences = re.split(r'(?<=[.!])\s+', text)
+        result = []
+        for sentence in sentences:
+            # Only add emphatic phrase if sentence doesn't already have one
+            has_emphatic = any(phrase.strip(", ") in sentence for phrase in self.EMPHATIC_PHRASES)
+            if sentence.endswith('.') and not has_emphatic and random.random() < self.change_probability * 0.25:
+                phrase = random.choice(self.EMPHATIC_PHRASES)
+                sentence = sentence[:-1] + phrase + "."
+            result.append(sentence)
+        return ' '.join(result)
+    
+    def add_formal_starters(self, text):
+        sentences = re.split(r'(?<=[.!?])\s+', text)
+        result = []
+        for i, sentence in enumerate(sentences):
+            # Only add formal starter if sentence doesn't already have one
+            has_starter = any(starter.strip() in sentence for starter in self.FORMAL_STARTERS)
+            if 0 < i < len(sentences) - 1 and not has_starter and random.random() < self.change_probability * 0.2:
+                starter = random.choice(self.FORMAL_STARTERS)
+                if sentence and sentence[0].isupper():
+                    sentence = starter + sentence[0].lower() + sentence[1:]
+                else:
+                    sentence = starter + sentence
+            result.append(sentence)
+        return ' '.join(result)
+    
+    def process(self, text):
+        text = self.expand_contractions(text)
+        text = self.remove_casual_fillers(text)
+        text = self.apply_synonym_replacements(text)
+        text = self.add_emphatic_phrases(text)
+        text = self.add_formal_starters(text)
+        return re.sub(r'\s+', ' ', text).strip()
+    
+    def multi_pass_process(self, text, passes=2):
+        for _ in range(passes):
+            text = self.process(text)
+        return text
+
+
+# Load model and tokenizer from HuggingFace Hub
+print("Loading humanizer V3 model from HuggingFace Hub...")
+MODEL_PATH = "harryroger798/humanizer-model-v3"
+tokenizer = T5Tokenizer.from_pretrained(MODEL_PATH)
+model = T5ForConditionalGeneration.from_pretrained(MODEL_PATH)
+print("Model loaded!")
+
+# Initialize post-processor
+processor = StealthPostProcessor(intensity="high")
+
+
+def humanize_text(text, use_post_processor=True, post_processor_passes=2):
+    """Combined humanizer: StealthWriter post-processor (primary) + model paraphrasing"""
+    if not text.strip():
+        return "", ""
+    
+    # Step 1: Run through model with better generation parameters
+    inputs = tokenizer(f"humanize: {text}", return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(
+        **inputs,
+        max_length=512,
+        num_beams=4,
+        early_stopping=True,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        repetition_penalty=2.5,
+        no_repeat_ngram_size=3,
+        length_penalty=1.0
+    )
+    model_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Check for repetitive output - if detected, use original text with post-processor only
+    words = model_output.split()
+    if len(words) > 10:
+        # Check for excessive repetition
+        word_counts = {}
+        for word in words:
+            word_counts[word] = word_counts.get(word, 0) + 1
+        max_repeat = max(word_counts.values()) if word_counts else 0
+        if max_repeat > len(words) * 0.3:  # If any word appears more than 30% of the time
+            # Fall back to using original text with post-processor
+            model_output = text
+    
+    # Step 2: Apply StealthWriter post-processor (this is the key to bypassing detection)
+    if use_post_processor:
+        final_output = processor.multi_pass_process(model_output, passes=post_processor_passes)
+    else:
+        final_output = model_output
+    
+    return model_output, final_output
+
+
+def gradio_humanize(text, use_post_processor, passes):
+    """Gradio interface function"""
+    model_out, final_out = humanize_text(text, use_post_processor, int(passes))
+    return model_out, final_out
+
+
+# Create Gradio interface
+with gr.Blocks(title="Humanizer V2 - AI Detector Bypass") as demo:
+    gr.Markdown("""
+    # 🔄 Humanizer V2 - AI Detector Bypass
+    
+    **Combined approach:** Fine-tuned T5 model (39,776 samples) + StealthWriter-style post-processor
+    
+    This humanizer uses techniques proven to bypass AI detectors:
+    - Trained on 39,776 humanizer samples (combined dataset)
+    - StealthWriter-style post-processing (expands contractions, uses formal expressions)
+    - Multi-pass processing for better results
+    - Achieved 0% AI detection on StealthWriter in testing
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Input Text (AI-generated)",
+                placeholder="Paste your AI-generated text here...",
+                lines=8
+            )
+            
+            with gr.Row():
+                use_post_processor = gr.Checkbox(label="Use StealthWriter Post-Processor", value=True)
+                passes = gr.Slider(minimum=1, maximum=3, value=2, step=1, label="Post-Processor Passes")
+            
+            submit_btn = gr.Button("Humanize", variant="primary")
+        
+        with gr.Column():
+            model_output = gr.Textbox(label="Model Output (before post-processing)", lines=6)
+            final_output = gr.Textbox(label="Final Output (after post-processing)", lines=6)
+    
+    submit_btn.click(
+        fn=gradio_humanize,
+        inputs=[input_text, use_post_processor, passes],
+        outputs=[model_output, final_output]
+    )
+    
+    gr.Markdown("""
+    ---
+    **Tips for best results:**
+    - Enable the StealthWriter post-processor for better bypass rates
+    - Use 2-3 passes for optimal results
+    - Test the output on StealthWriter or other AI detectors
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()

b.py

@@ -0,0 +1,214 @@
+# =============================================
+#        HuForm AI Mini - Gradio UI
+#   AI-generated text detection + humanisation
+#   Clean version – generation warnings removed
+#   Last updated for transformers 2025–2026
+# =============================================
+
+# ── 1. Install dependencies ───────────────────────────────────────
+# !pip install -q gradio transformers torch accelerate
+
+# ── 2. Imports ─────────────────────────────────────────────────────
+import gradio as gr
+import torch
+import re
+from transformers import (
+    pipeline,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    GenerationConfig
+)
+
+# ── 3. Configuration ───────────────────────────────────────────────
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {DEVICE.upper()}")
+
+# Detection model – good open-source choice
+DETECTION_MODEL = "Hello-SimpleAI/chatgpt-detector-roberta"
+
+# Humanisation model – fast and decent quality
+HUMANISATION_MODEL = "Qwen/Qwen2.5-1.5B-Instruct"
+
+# ── 4. Lazy model loading ──────────────────────────────────────────
+_detection_pipe = None
+def get_detection():
+    global _detection_pipe
+    if _detection_pipe is None:
+        print(f"Loading detector: {DETECTION_MODEL}")
+        _detection_pipe = pipeline(
+            "text-classification",
+            model=DETECTION_MODEL,
+            device=0 if DEVICE == "cuda" else -1,
+            torch_dtype=torch.float16 if DEVICE == "cuda" else None
+        )
+    return _detection_pipe
+
+_humanisation_pipe = None
+def get_humaniser():
+    global _humanisation_pipe
+    if _humanisation_pipe is None:
+        print(f"Loading humaniser: {HUMANISATION_MODEL}")
+        tokenizer = AutoTokenizer.from_pretrained(HUMANISATION_MODEL)
+        model = AutoModelForCausalLM.from_pretrained(
+            HUMANISATION_MODEL,
+            torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+            device_map="auto" if DEVICE == "cuda" else None
+        )
+        _humanisation_pipe = pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer
+        )
+    return _humanisation_pipe
+
+# ── 5. Helper functions ────────────────────────────────────────────
+def split_sentences(text):
+    if not text.strip():
+        return []
+    return [s.strip() for s in re.split(r'(?<=[.!?])\s+', text.strip()) if s.strip()]
+
+def detect_ai(text):
+    if not text.strip():
+        return "No text provided.", ""
+
+    sentences = split_sentences(text)
+    pipe = get_detection()
+
+    results = []
+    total_ai = 0.0
+
+    preds = pipe(sentences, truncation=True, max_length=512)
+
+    for sent, pred in zip(sentences, preds):
+        label = pred['label'].lower()
+        score = pred['score']
+
+        # Normalize to AI probability (model-specific)
+        ai_prob = score * 100 if any(x in label for x in ["fake", "ai", "generated"]) else (1 - score) * 100
+        total_ai += ai_prob
+
+        tag = "Very likely AI" if ai_prob > 85 else "Likely AI" if ai_prob > 60 else "Likely Human"
+        color = "#dc2626" if ai_prob > 85 else "#d97706" if ai_prob > 60 else "#16a34a"
+
+        results.append(
+            f"<div style='padding:8px; margin:4px 0; border-left:4px solid {color};'>"
+            f"<strong>{tag} ({ai_prob:.1f}%)</strong><br>{sent}</div>"
+        )
+
+    avg = total_ai / len(sentences) if sentences else 0
+    summary = f"<h3>Overall AI probability: {avg:.1f}%</h3>"
+
+    return summary + "".join(results), f"Overall: {avg:.1f}% AI"
+
+def humanise(text, style="Natural", intensity=0.7):
+    if not text.strip():
+        return "Please enter some text."
+
+    pipe = get_humaniser()
+
+    style_prompts = {
+        "Natural": "Rewrite this to sound completely natural, human-written — vary sentence length, use contractions, slight imperfections.",
+        "Casual": "Rewrite this in a relaxed, friendly, conversational tone like a real person chatting.",
+        "Academic": "Rewrite this in clear, formal academic style with precise and sophisticated language.",
+        "Professional": "Rewrite this in a crisp, professional business tone — confident and authoritative."
+    }
+
+    tone = style_prompts.get(style, style_prompts["Natural"])
+
+    prompt = f"""<|im_start|>system
+You are an expert editor that removes AI stiffness and makes text feel authentically human.
+Keep original meaning 100%. Improve flow, rhythm, vocabulary variety. Output ONLY the rewritten text.<|im_end|>
+<|im_start|>user
+{tone}
+Text:
+{text}<|im_end|>
+<|im_start|>assistant
+"""
+
+    try:
+        # ── Explicit GenerationConfig – removes both warnings ──
+        gen_config = GenerationConfig(
+            max_new_tokens=600,
+            temperature=0.4 + float(intensity) * 0.5,
+            top_p=0.92,
+            repetition_penalty=1.08,
+            do_sample=True,
+            pad_token_id=pipe.tokenizer.eos_token_id,
+            eos_token_id=pipe.tokenizer.eos_token_id
+        )
+        gen_config.max_length = None  # ← disables conflicting default max_length
+
+        output = pipe(
+            prompt,
+            generation_config=gen_config,
+            num_return_sequences=1
+        )[0]["generated_text"]
+
+        # Extract after assistant tag
+        if "assistant" in output:
+            rewritten = output.split("assistant", 1)[-1].strip()
+        else:
+            rewritten = output[len(prompt):].strip()
+
+        return rewritten.strip()
+    except Exception as e:
+        return f"Error during generation: {str(e)}"
+
+# ── 6. Gradio Interface ────────────────────────────────────────────
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# HuForm AI Mini\n**Sentence-level AI detection + style-controlled humanisation**")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_text = gr.Textbox(
+                label="Input Text (paragraph)",
+                placeholder="Paste or type text here...",
+                lines=8,
+                max_lines=20
+            )
+
+            style_dropdown = gr.Dropdown(
+                choices=["Natural", "Casual", "Academic", "Professional"],
+                value="Natural",
+                label="Humanisation Style"
+            )
+
+            intensity_slider = gr.Slider(
+                minimum=0.1, maximum=1.0, value=0.7, step=0.05,
+                label="Rewrite Intensity (higher = more creative change)"
+            )
+
+            with gr.Row():
+                detect_btn = gr.Button("Analyze (Detect AI)")
+                humanise_btn = gr.Button("Rewrite / Humanise")
+
+        with gr.Column(scale=1):
+            detection_output = gr.HTML(label="Detection Result")
+            humanised_output = gr.Textbox(label="Rewritten Text", lines=10)
+
+    # ── Event handlers ─────────────────────────────────────────────
+    detect_btn.click(
+        fn=detect_ai,
+        inputs=input_text,
+        outputs=[detection_output, gr.Textbox(visible=False)]
+    )
+
+    humanise_btn.click(
+        fn=humanise,
+        inputs=[input_text, style_dropdown, intensity_slider],
+        outputs=humanised_output
+    )
+
+    # Example texts
+    gr.Examples(
+        examples=[
+            ["The rapid advancement of artificial intelligence technologies has significantly transformed numerous industries and daily life."],
+            ["Yo this new AI stuff is actually kinda wild, like it's everywhere now lol."],
+            ["Machine learning algorithms demonstrate superior performance in pattern recognition tasks across diverse datasets."]
+        ],
+        inputs=input_text,
+        label="Quick examples"
+    )
+
+# ── Launch ─────────────────────────────────────────────────────────
+demo.launch(debug=False, share=True)

c.py

@@ -0,0 +1,1142 @@
+Hugging Face's logo
+Hugging Face
+
+Please check your email address for a confirmation link
+Spaces:
+conversantech
+/
+humanizer-ai 
+
+like
+47
+App
+Files
+Community
+2
+humanizer-ai
+/
+app.py
+
+conversantech's picture
+conversantech
+Update app.py
+600da25
+verified
+raw
+
+Copy download link
+history
+blame
+contribute
+delete
+50.9 kB
+import os
+import gradio as gr
+import random
+import re
+import nltk
+import numpy as np
+import torch
+from collections import defaultdict, Counter
+import string
+import math
+from typing import List, Dict, Tuple, Optional
+
+# Core NLP imports with fallback handling
+try:
+    import spacy
+    SPACY_AVAILABLE = True
+except ImportError:
+    SPACY_AVAILABLE = False
+
+try:
+    from transformers import (
+        AutoTokenizer, AutoModelForSequenceClassification,
+        T5Tokenizer, T5ForConditionalGeneration,
+        pipeline, BertTokenizer, BertModel
+    )
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+
+try:
+    from sentence_transformers import SentenceTransformer
+    SENTENCE_TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    SENTENCE_TRANSFORMERS_AVAILABLE = False
+
+try:
+    from textblob import TextBlob
+    TEXTBLOB_AVAILABLE = True
+except ImportError:
+    TEXTBLOB_AVAILABLE = False
+
+try:
+    from sklearn.metrics.pairwise import cosine_similarity
+    SKLEARN_AVAILABLE = True
+except ImportError:
+    SKLEARN_AVAILABLE = False
+
+from textstat import flesch_reading_ease, flesch_kincaid_grade
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.corpus import wordnet, stopwords
+from nltk.tag import pos_tag
+
+# Setup environment
+os.environ['NLTK_DATA'] = '/tmp/nltk_data'
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+def download_dependencies():
+    """Download all required dependencies with error handling"""
+    try:
+        # NLTK data
+        os.makedirs('/tmp/nltk_data', exist_ok=True)
+        nltk.data.path.append('/tmp/nltk_data')
+        
+        required_nltk = ['punkt', 'punkt_tab', 'averaged_perceptron_tagger', 
+                        'stopwords', 'wordnet', 'omw-1.4', 'vader_lexicon']
+        
+        for data in required_nltk:
+            try:
+                nltk.download(data, download_dir='/tmp/nltk_data', quiet=True)
+            except Exception as e:
+                print(f"Failed to download {data}: {e}")
+        
+        print("✅ NLTK dependencies loaded")
+        
+    except Exception as e:
+        print(f"❌ Dependency setup error: {e}")
+
+download_dependencies()
+
+class AdvancedAIHumanizer:
+    def __init__(self):
+        self.setup_models()
+        self.setup_humanization_patterns()
+        self.load_linguistic_resources()
+        self.setup_fallback_embeddings()
+        
+    def setup_models(self):
+        """Initialize advanced NLP models with fallback handling"""
+        try:
+            print("🔄 Loading advanced models...")
+            
+            # Sentence transformer for semantic similarity
+            if SENTENCE_TRANSFORMERS_AVAILABLE:
+                try:
+                    self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+                    print("✅ Sentence transformer loaded")
+                except:
+                    self.sentence_model = None
+                    print("⚠️ Sentence transformer not available")
+            else:
+                self.sentence_model = None
+                print("⚠️ sentence-transformers not installed")
+            
+            # Paraphrasing model
+            if TRANSFORMERS_AVAILABLE:
+                try:
+                    self.paraphrase_tokenizer = T5Tokenizer.from_pretrained('t5-small')
+                    self.paraphrase_model = T5ForConditionalGeneration.from_pretrained('t5-small')
+                    print("✅ T5 paraphrasing model loaded")
+                except:
+                    self.paraphrase_tokenizer = None
+                    self.paraphrase_model = None
+                    print("⚠️ T5 paraphrasing model not available")
+            else:
+                self.paraphrase_tokenizer = None
+                self.paraphrase_model = None
+                print("⚠️ transformers not installed")
+            
+            # SpaCy model
+            if SPACY_AVAILABLE:
+                try:
+                    self.nlp = spacy.load("en_core_web_sm")
+                    print("✅ SpaCy model loaded")
+                except:
+                    try:
+                        os.system("python -m spacy download en_core_web_sm")
+                        self.nlp = spacy.load("en_core_web_sm")
+                        print("✅ SpaCy model downloaded and loaded")
+                    except:
+                        self.nlp = None
+                        print("⚠️ SpaCy model not available")
+            else:
+                self.nlp = None
+                print("⚠️ spaCy not installed")
+                
+        except Exception as e:
+            print(f"❌ Model setup error: {e}")
+
+    def setup_fallback_embeddings(self):
+        """Setup fallback word similarity using simple patterns"""
+        # Common word groups for similarity
+        self.word_groups = {
+            'analyze': ['examine', 'study', 'investigate', 'explore', 'review', 'assess'],
+            'important': ['crucial', 'vital', 'significant', 'essential', 'key', 'critical'],
+            'shows': ['demonstrates', 'reveals', 'indicates', 'displays', 'exhibits'],
+            'understand': ['comprehend', 'grasp', 'realize', 'recognize', 'appreciate'],
+            'develop': ['create', 'build', 'establish', 'form', 'generate', 'produce'],
+            'improve': ['enhance', 'better', 'upgrade', 'refine', 'advance', 'boost'],
+            'consider': ['think about', 'examine', 'evaluate', 'contemplate', 'ponder'],
+            'different': ['various', 'diverse', 'distinct', 'separate', 'alternative'],
+            'effective': ['successful', 'efficient', 'productive', 'powerful', 'useful'],
+            'significant': ['important', 'substantial', 'considerable', 'notable', 'major'],
+            'implement': ['apply', 'execute', 'carry out', 'put into practice', 'deploy'],
+            'utilize': ['use', 'employ', 'apply', 'harness', 'leverage', 'exploit'],
+            'comprehensive': ['complete', 'thorough', 'extensive', 'detailed', 'full'],
+            'fundamental': ['basic', 'essential', 'core', 'primary', 'key', 'central'],
+            'substantial': ['significant', 'considerable', 'large', 'major', 'extensive']
+        }
+        
+        # Reverse mapping for quick lookup
+        self.synonym_map = {}
+        for base_word, synonyms in self.word_groups.items():
+            for synonym in synonyms:
+                if synonym not in self.synonym_map:
+                    self.synonym_map[synonym] = []
+                self.synonym_map[synonym].extend([base_word] + [s for s in synonyms if s != synonym])
+
+    def setup_humanization_patterns(self):
+        """Setup comprehensive humanization patterns"""
+        
+        # Expanded AI-flagged terms with more variations
+        self.ai_indicators = {
+            # Academic/Formal terms
+            r'\bdelve into\b': ["explore", "examine", "investigate", "look into", "study", "dig into", "analyze"],
+            r'\bembark upon?\b': ["begin", "start", "initiate", "launch", "set out", "commence", "kick off"],
+            r'\ba testament to\b': ["proof of", "evidence of", "shows", "demonstrates", "reflects", "indicates"],
+            r'\blandscape of\b': ["world of", "field of", "area of", "context of", "environment of", "space of"],
+            r'\bnavigating\b': ["handling", "managing", "dealing with", "working through", "tackling", "addressing"],
+            r'\bmeticulous\b': ["careful", "thorough", "detailed", "precise", "systematic", "methodical"],
+            r'\bintricate\b': ["complex", "detailed", "sophisticated", "elaborate", "complicated", "involved"],
+            r'\bmyriad\b': ["many", "numerous", "countless", "various", "multiple", "lots of"],
+            r'\bplethora\b': ["abundance", "wealth", "variety", "range", "loads", "tons"],
+            r'\bparadigm\b': ["model", "framework", "approach", "system", "way", "method"],
+            r'\bsynergy\b': ["teamwork", "cooperation", "collaboration", "working together", "unity"],
+            r'\bleverage\b': ["use", "utilize", "employ", "apply", "tap into", "make use of"],
+            r'\bfacilitate\b': ["help", "assist", "enable", "support", "aid", "make easier"],
+            r'\boptimize\b': ["improve", "enhance", "refine", "perfect", "boost", "maximize"],
+            r'\bstreamline\b': ["simplify", "improve", "refine", "smooth out", "make efficient"],
+            r'\brobust\b': ["strong", "reliable", "solid", "sturdy", "effective", "powerful"],
+            r'\bseamless\b': ["smooth", "fluid", "effortless", "easy", "integrated", "unified"],
+            r'\binnovative\b': ["creative", "original", "new", "fresh", "groundbreaking", "inventive"],
+            r'\bcutting-edge\b': ["advanced", "modern", "latest", "new", "state-of-the-art", "leading"],
+            r'\bstate-of-the-art\b': ["advanced", "modern", "latest", "top-notch", "cutting-edge"],
+            
+            # Transition phrases - more natural alternatives
+            r'\bfurthermore\b': ["also", "plus", "what's more", "on top of that", "besides", "additionally"],
+            r'\bmoreover\b': ["also", "plus", "what's more", "on top of that", "besides", "furthermore"],
+            r'\bhowever\b': ["but", "yet", "though", "still", "although", "that said"],
+            r'\bnevertheless\b': ["still", "yet", "even so", "but", "however", "all the same"],
+            r'\btherefore\b': ["so", "thus", "that's why", "as a result", "because of this", "for this reason"],
+            r'\bconsequently\b': ["so", "therefore", "as a result", "because of this", "thus", "that's why"],
+            r'\bin conclusion\b': ["finally", "to wrap up", "in the end", "ultimately", "lastly", "to finish"],
+            r'\bto summarize\b': ["in short", "briefly", "to sum up", "basically", "in essence", "overall"],
+            r'\bin summary\b': ["briefly", "in short", "basically", "to sum up", "overall", "in essence"],
+            
+            # Academic connectors - more casual
+            r'\bin order to\b': ["to", "so I can", "so we can", "with the goal of", "aiming to"],
+            r'\bdue to the fact that\b': ["because", "since", "as", "given that", "seeing that"],
+            r'\bfor the purpose of\b': ["to", "in order to", "for", "aiming to", "with the goal of"],
+            r'\bwith regard to\b': ["about", "concerning", "regarding", "when it comes to", "as for"],
+            r'\bin terms of\b': ["regarding", "when it comes to", "as for", "concerning", "about"],
+            r'\bby means of\b': ["through", "using", "via", "by way of", "with"],
+            r'\bas a result of\b': ["because of", "due to", "from", "owing to", "thanks to"],
+            r'\bin the event that\b': ["if", "should", "in case", "when", "if it happens that"],
+            r'\bprior to\b': ["before", "ahead of", "earlier than", "in advance of"],
+            r'\bsubsequent to\b': ["after", "following", "later than", "once"],
+            
+            # Additional formal patterns
+            r'\bcomprehensive\b': ["complete", "thorough", "detailed", "full", "extensive", "in-depth"],
+            r'\bfundamental\b': ["basic", "essential", "core", "key", "primary", "main"],
+            r'\bsubstantial\b': ["significant", "considerable", "large", "major", "big", "huge"],
+            r'\bsignificant\b': ["important", "major", "considerable", "substantial", "notable", "big"],
+            r'\bimplement\b': ["put in place", "carry out", "apply", "execute", "use", "deploy"],
+            r'\butilize\b': ["use", "employ", "apply", "make use of", "tap into", "leverage"],
+            r'\bdemonstrate\b': ["show", "prove", "illustrate", "reveal", "display", "exhibit"],
+            r'\bestablish\b': ["set up", "create", "build", "form", "start", "found"],
+            r'\bmaintain\b': ["keep", "preserve", "sustain", "continue", "uphold", "retain"],
+            r'\bobtain\b': ["get", "acquire", "gain", "secure", "achieve", "attain"],
+        }
+        
+        # More natural sentence starters
+        self.human_starters = [
+            "Actually,", "Honestly,", "Basically,", "Really,", "Generally,", "Usually,",
+            "Often,", "Sometimes,", "Clearly,", "Obviously,", "Naturally,", "Certainly,",
+            "Definitely,", "Interestingly,", "Surprisingly,", "Notably,", "Importantly,",
+            "What's more,", "Plus,", "Also,", "Besides,", "On top of that,", "In fact,",
+            "Indeed,", "Of course,", "No doubt,", "Without question,", "Frankly,",
+            "To be honest,", "Truth is,", "The thing is,", "Here's the deal,", "Look,"
+        ]
+        
+        # Professional but natural contractions
+        self.contractions = {
+            r'\bit is\b': "it's", r'\bthat is\b': "that's", r'\bthere is\b': "there's",
+            r'\bwho is\b': "who's", r'\bwhat is\b': "what's", r'\bwhere is\b': "where's",
+            r'\bthey are\b': "they're", r'\bwe are\b': "we're", r'\byou are\b': "you're",
+            r'\bI am\b': "I'm", r'\bhe is\b': "he's", r'\bshe is\b': "she's",
+            r'\bcannot\b': "can't", r'\bdo not\b': "don't", r'\bdoes not\b': "doesn't",
+            r'\bwill not\b': "won't", r'\bwould not\b': "wouldn't", r'\bshould not\b': "shouldn't",
+            r'\bcould not\b': "couldn't", r'\bhave not\b': "haven't", r'\bhas not\b': "hasn't",
+            r'\bhad not\b': "hadn't", r'\bis not\b': "isn't", r'\bare not\b': "aren't",
+            r'\bwas not\b': "wasn't", r'\bwere not\b': "weren't", r'\blet us\b': "let's",
+            r'\bI will\b': "I'll", r'\byou will\b': "you'll", r'\bwe will\b': "we'll",
+            r'\bthey will\b': "they'll", r'\bI would\b': "I'd", r'\byou would\b': "you'd"
+        }
+
+    def load_linguistic_resources(self):
+        """Load additional linguistic resources"""
+        try:
+            # Stop words
+            self.stop_words = set(stopwords.words('english'))
+            
+            # Common filler words and phrases for natural flow
+            self.fillers = [
+                "you know", "I mean", "sort of", "kind of", "basically", "actually",
+                "really", "quite", "pretty much", "more or less", "essentially"
+            ]
+            
+            # Natural transition phrases
+            self.natural_transitions = [
+                "And here's the thing:", "But here's what's interesting:", "Now, here's where it gets good:",
+                "So, what does this mean?", "Here's why this matters:", "Think about it this way:",
+                "Let me put it this way:", "Here's the bottom line:", "The reality is:",
+                "What we're seeing is:", "The truth is:", "At the end of the day:"
+            ]
+            
+            print("✅ Linguistic resources loaded")
+            
+        except Exception as e:
+            print(f"❌ Linguistic resource error: {e}")
+
+    def calculate_perplexity(self, text: str) -> float:
+        """Calculate text perplexity to measure predictability"""
+        try:
+            words = word_tokenize(text.lower())
+            if len(words) < 2:
+                return 50.0
+                
+            word_freq = Counter(words)
+            total_words = len(words)
+            
+            # Calculate entropy
+            entropy = 0
+            for word in words:
+                prob = word_freq[word] / total_words
+                if prob > 0:
+                    entropy -= prob * math.log2(prob)
+            
+            perplexity = 2 ** entropy
+            
+            # Normalize to human-like range (40-80)
+            if perplexity < 20:
+                perplexity += random.uniform(20, 30)
+            elif perplexity > 100:
+                perplexity = random.uniform(60, 80)
+                
+            return perplexity
+            
+        except:
+            return random.uniform(45, 75)  # Human-like default
+
+    def calculate_burstiness(self, text: str) -> float:
+        """Calculate burstiness (variation in sentence length)"""
+        try:
+            sentences = sent_tokenize(text)
+            if len(sentences) < 2:
+                return 1.2
+                
+            lengths = [len(word_tokenize(sent)) for sent in sentences]
+            
+            if len(lengths) < 2:
+                return 1.2
+                
+            mean_length = np.mean(lengths)
+            variance = np.var(lengths)
+            
+            if mean_length == 0:
+                return 1.2
+                
+            burstiness = variance / mean_length
+            
+            # Ensure human-like burstiness (>0.5)
+            if burstiness < 0.5:
+                burstiness = random.uniform(0.7, 1.5)
+                
+            return burstiness
+            
+        except:
+            return random.uniform(0.8, 1.4)  # Human-like default
+
+    def get_semantic_similarity(self, text1: str, text2: str) -> float:
+        """Calculate semantic similarity between texts"""
+        try:
+            if self.sentence_model and SKLEARN_AVAILABLE:
+                embeddings = self.sentence_model.encode([text1, text2])
+                similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]
+                return float(similarity)
+            else:
+                # Fallback: simple word overlap similarity
+                words1 = set(word_tokenize(text1.lower()))
+                words2 = set(word_tokenize(text2.lower()))
+                
+                if not words1 or not words2:
+                    return 0.8
+                
+                intersection = len(words1.intersection(words2))
+                union = len(words1.union(words2))
+                
+                if union == 0:
+                    return 0.8
+                    
+                jaccard_sim = intersection / union
+                return max(0.7, jaccard_sim)  # Minimum baseline
+                
+        except Exception as e:
+            print(f"Similarity calculation error: {e}")
+            return 0.8
+
+    def advanced_paraphrase(self, text: str, max_length: int = 256) -> str:
+        """Advanced paraphrasing using T5 or fallback methods"""
+        try:
+            if self.paraphrase_model and self.paraphrase_tokenizer:
+                # Use T5 for paraphrasing
+                input_text = f"paraphrase: {text}"
+                inputs = self.paraphrase_tokenizer.encode(
+                    input_text, 
+                    return_tensors='pt', 
+                    max_length=max_length, 
+                    truncation=True
+                )
+                
+                with torch.no_grad():
+                    outputs = self.paraphrase_model.generate(
+                        inputs, 
+                        max_length=max_length,
+                        num_return_sequences=1,
+                        temperature=0.8,
+                        do_sample=True,
+                        top_p=0.9,
+                        repetition_penalty=1.1
+                    )
+                
+                paraphrased = self.paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
+                
+                # Check semantic similarity
+                similarity = self.get_semantic_similarity(text, paraphrased)
+                if similarity > 0.7:
+                    return paraphrased
+                    
+            # Fallback: manual paraphrasing
+            return self.manual_paraphrase(text)
+            
+        except Exception as e:
+            print(f"Paraphrase error: {e}")
+            return self.manual_paraphrase(text)
+
+    def manual_paraphrase(self, text: str) -> str:
+        """Manual paraphrasing as fallback"""
+        # Simple restructuring patterns
+        patterns = [
+            # Active to passive hints
+            (r'(\w+) shows that (.+)', r'It is shown by \1 that \2'),
+            (r'(\w+) demonstrates (.+)', r'This demonstrates \2 through \1'),
+            (r'We can see that (.+)', r'It becomes clear that \1'),
+            (r'This indicates (.+)', r'What this shows is \1'),
+            (r'Research shows (.+)', r'Studies reveal \1'),
+            (r'It is important to note (.+)', r'Worth noting is \1'),
+        ]
+        
+        result = text
+        for pattern, replacement in patterns:
+            if re.search(pattern, result, re.IGNORECASE):
+                result = re.sub(pattern, replacement, result, flags=re.IGNORECASE)
+                break
+                
+        return result
+
+    def get_contextual_synonym(self, word: str, context: str = "") -> str:
+        """Get contextually appropriate synonym with fallback"""
+        try:
+            # First try the predefined word groups
+            word_lower = word.lower()
+            
+            if word_lower in self.word_groups:
+                synonyms = self.word_groups[word_lower]
+                return random.choice(synonyms)
+            
+            if word_lower in self.synonym_map:
+                synonyms = self.synonym_map[word_lower]
+                return random.choice(synonyms)
+            
+            # Fallback to WordNet
+            synsets = wordnet.synsets(word.lower())
+            if synsets:
+                synonyms = []
+                for synset in synsets[:2]:
+                    for lemma in synset.lemmas():
+                        synonym = lemma.name().replace('_', ' ')
+                        if synonym != word.lower() and len(synonym) > 2:
+                            synonyms.append(synonym)
+                
+                if synonyms:
+                    # Prefer synonyms with similar length
+                    suitable = [s for s in synonyms if abs(len(s) - len(word)) <= 3]
+                    if suitable:
+                        return random.choice(suitable[:3])
+                    return random.choice(synonyms[:3])
+            
+            return word
+            
+        except:
+            return word
+
+    def advanced_sentence_restructure(self, sentence: str) -> str:
+        """Advanced sentence restructuring"""
+        try:
+            # Multiple restructuring strategies
+            strategies = [
+                self.move_adverb_clause,
+                self.split_compound_sentence,
+                self.vary_voice_advanced,
+                self.add_casual_connector,
+                self.restructure_with_emphasis
+            ]
+            
+            strategy = random.choice(strategies)
+            result = strategy(sentence)
+            
+            # Ensure we didn't break the sentence
+            if len(result.split()) < 3 or not result.strip():
+                return sentence
+                
+            return result
+            
+        except:
+            return sentence
+
+    def move_adverb_clause(self, sentence: str) -> str:
+        """Move adverbial clauses for variation"""
+        patterns = [
+            (r'^(.*?),\s*(because|since|when|if|although|while|as)\s+(.*?)([.!?])$', 
+             r'\2 \3, \1\4'),
+            (r'^(.*?)\s+(because|since|when|if|although|while|as)\s+(.*?)([.!?])$', 
+             r'\2 \3, \1\4'),
+            (r'^(Although|While|Since|Because|When|If)\s+(.*?),\s*(.*?)([.!?])$',
+             r'\3, \1 \2\4')
+        ]
+        
+        for pattern, replacement in patterns:
+            if re.search(pattern, sentence, re.IGNORECASE):
+                result = re.sub(pattern, replacement, sentence, flags=re.IGNORECASE)
+                if result != sentence and len(result.split()) >= 3:
+                    return result.strip()
+        
+        return sentence
+
+    def split_compound_sentence(self, sentence: str) -> str:
+        """Split overly long compound sentences"""
+        conjunctions = [', and ', ', but ', ', so ', ', yet ', ', or ', '; however,', '; moreover,']
+        
+        for conj in conjunctions:
+            if conj in sentence and len(sentence.split()) > 15:
+                parts = sentence.split(conj, 1)
+                if len(parts) == 2:
+                    first = parts[0].strip()
+                    second = parts[1].strip()
+                    
+                    # Ensure both parts are substantial
+                    if len(first.split()) > 3 and len(second.split()) > 3:
+                        # Add period to first part if needed
+                        if not first.endswith(('.', '!', '?')):
+                            first += '.'
+                        
+                        # Capitalize second part
+                        if second and second[0].islower():
+                            second = second[0].upper() + second[1:]
+                        
+                        # Add natural connector
+                        connectors = ["Also,", "Plus,", "Additionally,", "What's more,", "On top of that,"]
+                        connector = random.choice(connectors)
+                        
+                        return f"{first} {connector} {second.lower()}"
+        
+        return sentence
+
+    def vary_voice_advanced(self, sentence: str) -> str:
+        """Advanced voice variation"""
+        # Passive to active patterns
+        passive_patterns = [
+            (r'(\w+)\s+(?:is|are|was|were)\s+(\w+ed|shown|seen|made|used|done|taken|given|found)\s+by\s+(.+)',
+             r'\3 \2 \1'),
+            (r'(\w+)\s+(?:has|have)\s+been\s+(\w+ed|shown|seen|made|used|done|taken|given|found)\s+by\s+(.+)',
+             r'\3 \2 \1'),
+            (r'It\s+(?:is|was)\s+(\w+ed|shown|found|discovered)\s+that\s+(.+)',
+             r'Research \1 that \2'),
+            (r'(\w+)\s+(?:is|are)\s+considered\s+(.+)',
+             r'Experts consider \1 \2')
+        ]
+        
+        for pattern, replacement in passive_patterns:
+            if re.search(pattern, sentence, re.IGNORECASE):
+                result = re.sub(pattern, replacement, sentence, flags=re.IGNORECASE)
+                if result != sentence:
+                    return result
+        
+        return sentence
+
+    def add_casual_connector(self, sentence: str) -> str:
+        """Add casual connectors for natural flow"""
+        if len(sentence.split()) > 8:
+            # Insert casual phrases
+            casual_insertions = [
+                ", you know,", ", I mean,", ", basically,", ", actually,",
+                ", really,", ", essentially,", ", fundamentally,"
+            ]
+            
+            # Find a good insertion point (after a comma)
+            if ',' in sentence:
+                parts = sentence.split(',', 1)
+                if len(parts) == 2 and random.random() < 0.3:
+                    insertion = random.choice(casual_insertions)
+                    return f"{parts[0]}{insertion}{parts[1]}"
+        
+        return sentence
+
+    def restructure_with_emphasis(self, sentence: str) -> str:
+        """Restructure with natural emphasis"""
+        emphasis_patterns = [
+            (r'^The fact that (.+) is (.+)', r'What\'s \2 is that \1'),
+            (r'^It is (.+) that (.+)', r'What\'s \1 is that \2'),
+            (r'^(.+) is very important', r'\1 really matters'),
+            (r'^This shows that (.+)', r'This proves \1'),
+            (r'^Research indicates (.+)', r'Studies show \1'),
+            (r'^It can be seen that (.+)', r'We can see that \1')
+        ]
+        
+        for pattern, replacement in emphasis_patterns:
+            if re.search(pattern, sentence, re.IGNORECASE):
+                result = re.sub(pattern, replacement, sentence, flags=re.IGNORECASE)
+                if result != sentence:
+                    return result
+        
+        return sentence
+
+    def add_human_touches(self, text: str, intensity: int = 2) -> str:
+        """Add human-like writing patterns"""
+        sentences = sent_tokenize(text)
+        humanized = []
+        
+        touch_probability = {1: 0.15, 2: 0.25, 3: 0.4}
+        prob = touch_probability.get(intensity, 0.25)
+        
+        for i, sentence in enumerate(sentences):
+            current = sentence
+            
+            # Add natural starters occasionally
+            if i > 0 and random.random() < prob and len(current.split()) > 6:
+                starter = random.choice(self.human_starters)
+                current = f"{starter} {current[0].lower() + current[1:]}"
+            
+            # Add natural transitions between sentences
+            if i > 0 and random.random() < prob * 0.3:
+                transition = random.choice(self.natural_transitions)
+                current = f"{transition} {current[0].lower() + current[1:]}"
+            
+            # Add casual fillers occasionally
+            if random.random() < prob * 0.2 and len(current.split()) > 10:
+                filler = random.choice(self.fillers)
+                words = current.split()
+                # Insert filler in middle
+                mid_point = len(words) // 2
+                words.insert(mid_point, f", {filler},")
+                current = " ".join(words)
+            
+            # Vary sentence endings for naturalness
+            if random.random() < prob * 0.2:
+                current = self.vary_sentence_ending(current)
+            
+            humanized.append(current)
+        
+        return " ".join(humanized)
+
+    def vary_sentence_ending(self, sentence: str) -> str:
+        """Add variety to sentence endings"""
+        if sentence.endswith('.'):
+            variations = [
+                (r'(\w+) is important\.', r'\1 matters.'),
+                (r'(\w+) is significant\.', r'\1 is really important.'),
+                (r'This shows (.+)\.', r'This proves \1.'),
+                (r'(\w+) demonstrates (.+)\.', r'\1 clearly shows \2.'),
+                (r'(\w+) indicates (.+)\.', r'\1 suggests \2.'),
+                (r'It is clear that (.+)\.', r'Obviously, \1.'),
+                (r'(\w+) reveals (.+)\.', r'\1 shows us \2.'),
+            ]
+            
+            for pattern, replacement in variations:
+                if re.search(pattern, sentence, re.IGNORECASE):
+                    result = re.sub(pattern, replacement, sentence, flags=re.IGNORECASE)
+                    if result != sentence:
+                        return result
+        
+        return sentence
+
+    def apply_advanced_contractions(self, text: str, intensity: int = 2) -> str:
+        """Apply natural contractions"""
+        contraction_probability = {1: 0.4, 2: 0.6, 3: 0.8}
+        prob = contraction_probability.get(intensity, 0.6)
+        
+        for pattern, contraction in self.contractions.items():
+            if re.search(pattern, text, re.IGNORECASE) and random.random() < prob:
+                text = re.sub(pattern, contraction, text, flags=re.IGNORECASE)
+        
+        return text
+
+    def enhance_vocabulary_diversity(self, text: str, intensity: int = 2) -> str:
+        """Enhanced vocabulary diversification"""
+        words = word_tokenize(text)
+        enhanced = []
+        word_usage = defaultdict(int)
+        
+        synonym_probability = {1: 0.2, 2: 0.35, 3: 0.5}
+        prob = synonym_probability.get(intensity, 0.35)
+        
+        # Track word frequency
+        for word in words:
+            if word.isalpha() and len(word) > 3:
+                word_usage[word.lower()] += 1
+        
+        for i, word in enumerate(words):
+            if (word.isalpha() and len(word) > 3 and 
+                word.lower() not in self.stop_words and
+                word_usage[word.lower()] > 1 and 
+                random.random() < prob):
+                
+                # Get context
+                context_start = max(0, i - 5)
+                context_end = min(len(words), i + 5)
+                context = " ".join(words[context_start:context_end])
+                
+                synonym = self.get_contextual_synonym(word, context)
+                enhanced.append(synonym)
+                word_usage[word.lower()] -= 1  # Reduce frequency count
+            else:
+                enhanced.append(word)
+        
+        return " ".join(enhanced)
+
+    def multiple_pass_humanization(self, text: str, intensity: int = 2) -> str:
+        """Apply multiple humanization passes"""
+        current_text = text
+        
+        passes = {1: 3, 2: 4, 3: 5}  # Increased passes for better results
+        num_passes = passes.get(intensity, 4)
+        
+        for pass_num in range(num_passes):
+            print(f"🔄 Pass {pass_num + 1}/{num_passes}")
+            
+            if pass_num == 0:
+                # Pass 1: AI pattern replacement
+                current_text = self.replace_ai_patterns(current_text, intensity)
+                
+            elif pass_num == 1:
+                # Pass 2: Sentence restructuring
+                current_text = self.restructure_sentences(current_text, intensity)
+                
+            elif pass_num == 2:
+                # Pass 3: Vocabulary enhancement
+                current_text = self.enhance_vocabulary_diversity(current_text, intensity)
+                
+            elif pass_num == 3:
+                # Pass 4: Contractions and human touches
+                current_text = self.apply_advanced_contractions(current_text, intensity)
+                current_text = self.add_human_touches(current_text, intensity)
+                
+            elif pass_num == 4:
+                # Pass 5: Final paraphrasing and polish
+                sentences = sent_tokenize(current_text)
+                final_sentences = []
+                for sent in sentences:
+                    if len(sent.split()) > 10 and random.random() < 0.3:
+                        paraphrased = self.advanced_paraphrase(sent)
+                        final_sentences.append(paraphrased)
+                    else:
+                        final_sentences.append(sent)
+                current_text = " ".join(final_sentences)
+            
+            # Check semantic preservation
+            similarity = self.get_semantic_similarity(text, current_text)
+            print(f"   Semantic similarity: {similarity:.2f}")
+            
+            if similarity < 0.7:
+                print(f"⚠️ Semantic drift detected, using previous version")
+                break
+        
+        return current_text
+
+    def replace_ai_patterns(self, text: str, intensity: int = 2) -> str:
+        """Replace AI-flagged patterns aggressively"""
+        result = text
+        replacement_probability = {1: 0.7, 2: 0.85, 3: 0.95}
+        prob = replacement_probability.get(intensity, 0.85)
+        
+        for pattern, replacements in self.ai_indicators.items():
+            matches = list(re.finditer(pattern, result, re.IGNORECASE))
+            for match in reversed(matches):  # Replace from end to preserve positions
+                if random.random() < prob:
+                    replacement = random.choice(replacements)
+                    result = result[:match.start()] + replacement + result[match.end():]
+        
+        return result
+
+    def restructure_sentences(self, text: str, intensity: int = 2) -> str:
+        """Restructure sentences for maximum variation"""
+        sentences = sent_tokenize(text)
+        restructured = []
+        
+        restructure_probability = {1: 0.3, 2: 0.5, 3: 0.7}
+        prob = restructure_probability.get(intensity, 0.5)
+        
+        for sentence in sentences:
+            if len(sentence.split()) > 8 and random.random() < prob:
+                restructured_sent = self.advanced_sentence_restructure(sentence)
+                restructured.append(restructured_sent)
+            else:
+                restructured.append(sentence)
+        
+        return " ".join(restructured)
+
+    def final_quality_check(self, original: str, processed: str) -> Tuple[str, Dict]:
+        """Final quality and coherence check"""
+        # Calculate metrics
+        metrics = {
+            'semantic_similarity': self.get_semantic_similarity(original, processed),
+            'perplexity': self.calculate_perplexity(processed),
+            'burstiness': self.calculate_burstiness(processed),
+            'readability': flesch_reading_ease(processed)
+        }
+        
+        # Ensure human-like metrics
+        if metrics['perplexity'] < 40:
+            metrics['perplexity'] = random.uniform(45, 75)
+        if metrics['burstiness'] < 0.5:
+            metrics['burstiness'] = random.uniform(0.7, 1.4)
+        
+        # Final cleanup
+        processed = re.sub(r'\s+', ' ', processed)
+        processed = re.sub(r'\s+([,.!?;:])', r'\1', processed)
+        processed = re.sub(r'([,.!?;:])\s*([A-Z])', r'\1 \2', processed)
+        
+        # Ensure proper capitalization
+        sentences = sent_tokenize(processed)
+        corrected = []
+        for sentence in sentences:
+            if sentence and sentence[0].islower():
+                sentence = sentence[0].upper() + sentence[1:]
+            corrected.append(sentence)
+        
+        processed = " ".join(corrected)
+        processed = re.sub(r'\.+', '.', processed)
+        processed = processed.strip()
+        
+        return processed, metrics
+
+    def humanize_text(self, text: str, intensity: str = "standard") -> str:
+        """Main humanization method with advanced processing"""
+        if not text or not text.strip():
+            return "Please provide text to humanize."
+        
+        try:
+            # Map intensity
+            intensity_mapping = {"light": 1, "standard": 2, "heavy": 3}
+            intensity_level = intensity_mapping.get(intensity, 2)
+            
+            print(f"🚀 Starting advanced humanization (Level {intensity_level})")
+            
+            # Pre-processing
+            text = text.strip()
+            original_text = text
+            
+            # Multi-pass humanization
+            result = self.multiple_pass_humanization(text, intensity_level)
+            
+            # Final quality check
+            result, metrics = self.final_quality_check(original_text, result)
+            
+            print(f"✅ Humanization complete")
+            print(f"📊 Final metrics - Similarity: {metrics['semantic_similarity']:.2f}, Perplexity: {metrics['perplexity']:.1f}, Burstiness: {metrics['burstiness']:.1f}")
+            
+            return result
+            
+        except Exception as e:
+            print(f"❌ Humanization error: {e}")
+            return f"Error processing text: {str(e)}"
+
+    def get_detailed_analysis(self, text: str) -> str:
+        """Get detailed analysis of humanized text"""
+        try:
+            metrics = {
+                'readability': flesch_reading_ease(text),
+                'grade_level': flesch_kincaid_grade(text),
+                'perplexity': self.calculate_perplexity(text),
+                'burstiness': self.calculate_burstiness(text),
+                'sentence_count': len(sent_tokenize(text)),
+                'word_count': len(word_tokenize(text))
+            }
+            
+            # Readability assessment
+            score = metrics['readability']
+            level = ("Very Easy" if score >= 90 else "Easy" if score >= 80 else 
+                    "Fairly Easy" if score >= 70 else "Standard" if score >= 60 else 
+                    "Fairly Difficult" if score >= 50 else "Difficult" if score >= 30 else 
+                    "Very Difficult")
+            
+            # AI detection assessment
+            perplexity_good = metrics['perplexity'] >= 40
+            burstiness_good = metrics['burstiness'] >= 0.5
+            detection_bypass = "✅ EXCELLENT" if (perplexity_good and burstiness_good) else "⚠️ GOOD" if (perplexity_good or burstiness_good) else "❌ NEEDS WORK"
+            
+            analysis = f"""📊 Advanced Content Analysis:
+📖 Readability Metrics:
+• Flesch Score: {score:.1f} ({level})
+• Grade Level: {metrics['grade_level']:.1f}
+• Sentences: {metrics['sentence_count']}
+• Words: {metrics['word_count']}
+🤖 AI Detection Bypass:
+• Perplexity: {metrics['perplexity']:.1f} {'✅' if perplexity_good else '❌'} (Target: 40-80)
+• Burstiness: {metrics['burstiness']:.1f} {'✅' if burstiness_good else '❌'} (Target: >0.5)
+• Overall Status: {detection_bypass}
+🎯 Detection Tool Results:
+• ZeroGPT: {'0% AI' if (perplexity_good and burstiness_good) else 'Low AI'}
+• Quillbot: {'Human' if (perplexity_good and burstiness_good) else 'Mostly Human'}
+• GPTZero: {'Undetectable' if (perplexity_good and burstiness_good) else 'Low Detection'}"""
+            
+            return analysis
+            
+        except Exception as e:
+            return f"Analysis error: {str(e)}"
+
+# Create enhanced interface
+def create_enhanced_interface():
+    """Create the enhanced Gradio interface"""
+    humanizer = AdvancedAIHumanizer()
+    
+    def process_text_advanced(input_text, intensity):
+        if not input_text or len(input_text.strip()) < 10:
+            return "Please enter at least 10 characters of text to humanize.", "No analysis available."
+        
+        try:
+            result = humanizer.humanize_text(input_text, intensity)
+            analysis = humanizer.get_detailed_analysis(result)
+            return result, analysis
+        except Exception as e:
+            return f"Error: {str(e)}", "Processing failed."
+
+    # Enhanced CSS styling
+    enhanced_css = """
+    .gradio-container {
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        min-height: 100vh;
+    }
+    .main-header {
+        text-align: center;
+        color: white;
+        font-size: 2.8em;
+        font-weight: 800;
+        margin-bottom: 20px;
+        padding: 40px 20px;
+        text-shadow: 2px 2px 8px rgba(0,0,0,0.3);
+        background: rgba(255,255,255,0.1);
+        border-radius: 20px;
+        backdrop-filter: blur(10px);
+    }
+    .feature-card {
+        background: rgba(255, 255, 255, 0.95);
+        border-radius: 20px;
+        padding: 30px;
+        margin: 25px 0;
+        box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+        backdrop-filter: blur(15px);
+        border: 1px solid rgba(255,255,255,0.2);
+    }
+    .enhancement-badge {
+        background: linear-gradient(45deg, #28a745, #20c997);
+        color: white;
+        padding: 10px 18px;
+        border-radius: 25px;
+        font-weight: 700;
+        margin: 8px;
+        display: inline-block;
+        box-shadow: 0 4px 15px rgba(40,167,69,0.3);
+        transition: transform 0.2s;
+    }
+    .enhancement-badge:hover {
+        transform: translateY(-2px);
+    }
+    .status-excellent { color: #28a745; font-weight: bold; }
+    .status-good { color: #ffc107; font-weight: bold; }
+    .status-needs-work { color: #dc3545; font-weight: bold; }
+    """
+
+    with gr.Blocks(
+        title="🧠 Advanced AI Humanizer Pro - 0% Detection", 
+        theme=gr.themes.Soft(),
+        css=enhanced_css
+    ) as interface:
+        
+        gr.HTML("""
+        <div class="main-header">
+            🧠 Advanced AI Humanizer Pro
+            <div style="font-size: 0.35em; margin-top: 15px; opacity: 0.9;">
+                🎯 Guaranteed 0% AI Detection • 🔒 Meaning Preservation • ⚡ Professional Quality
+            </div>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                input_text = gr.Textbox(
+                    label="📄 AI Content Input", 
+                    lines=16, 
+                    placeholder="Paste your AI-generated content here...\n\n🚀 This advanced system uses multiple AI detection bypass techniques:\n• Multi-pass processing with 5 humanization layers\n• Perplexity optimization for unpredictability\n• Burstiness enhancement for natural variation\n• Semantic similarity preservation\n• Advanced paraphrasing with T5 models\n• Contextual synonym replacement\n\n💡 Minimum 50 words recommended for optimal results.",
+                    info="✨ Optimized for all AI detectors: ZeroGPT, Quillbot, GPTZero, Originality.ai",
+                    show_copy_button=True
+                )
+                
+                intensity = gr.Radio(
+                    choices=[
+                        ("🟢 Light (Conservative, 70% changes)", "light"),
+                        ("🟡 Standard (Balanced, 85% changes)", "standard"), 
+                        ("🔴 Heavy (Maximum, 95% changes)", "heavy")
+                    ], 
+                    value="standard", 
+                    label="🎛️ Humanization Intensity",
+                    info="⚡ Standard recommended for most content • Heavy for highly detectable AI text"
+                )
+                
+                btn = gr.Button(
+                    "🚀 Advanced Humanize (0% AI Detection)", 
+                    variant="primary", 
+                    size="lg"
+                )
+            
+            with gr.Column(scale=1):
+                output_text = gr.Textbox(
+                    label="✅ Humanized Content (0% AI Detection Guaranteed)", 
+                    lines=16, 
+                    show_copy_button=True,
+                    info="🎯 Ready for use - Bypasses all major AI detectors"
+                )
+                
+                analysis = gr.Textbox(
+                    label="📊 Advanced Detection Analysis", 
+                    lines=12,
+                    info="📈 Detailed metrics and bypass confirmation"
+                )
+        
+        gr.HTML("""
+        <div class="feature-card">
+            <h2 style="text-align: center; color: #2c3e50; margin-bottom: 25px;">🎯 Advanced AI Detection Bypass Technology</h2>
+            <div style="text-align: center; margin: 25px 0;">
+                <span class="enhancement-badge">🧠 T5 Transformer Models</span>
+                <span class="enhancement-badge">📊 Perplexity Optimization</span>
+                <span class="enhancement-badge">🔄 Multi-Pass Processing</span>
+                <span class="enhancement-badge">🎭 Semantic Preservation</span>
+                <span class="enhancement-badge">📝 Dependency Parsing</span>
+                <span class="enhancement-badge">💡 Contextual Synonyms</span>
+                <span class="enhancement-badge">🎯 Burstiness Enhancement</span>
+                <span class="enhancement-badge">🔍 Human Pattern Mimicking</span>
+            </div>
+        </div>
+        """)
+        
+        gr.HTML("""
+        <div class="feature-card">
+            <h3 style="color: #2c3e50; margin-bottom: 20px;">🛠️ Technical Specifications & Results:</h3>
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 25px; margin: 25px 0;">
+                <div style="background: linear-gradient(135deg, #e3f2fd, #bbdefb); padding: 20px; border-radius: 15px; border-left: 5px solid #2196f3;">
+                    <strong style="color: #1976d2;">🤖 AI Models & Techniques:</strong><br><br>
+                    • T5 Paraphrasing Engine<br>
+                    • BERT Contextual Analysis<br>
+                    • Sentence Transformers<br>
+                    • Advanced NLP Pipeline<br>
+                    • 5-Pass Processing System<br>
+                    • Semantic Similarity Checks
+                </div>
+                <div style="background: linear-gradient(135deg, #e8f5e8, #c8e6c9); padding: 20px; border-radius: 15px; border-left: 5px solid #4caf50;">
+                    <strong style="color: #388e3c;">📊 Quality Guarantees:</strong><br><br>
+                    • Semantic Similarity >85%<br>
+                    • Perplexity: 40-80 (Human-like)<br>
+                    • Burstiness: >0.5 (Natural)<br>
+                    • Readability Preserved<br>
+                    • Professional Tone Maintained<br>
+                    • Original Meaning Intact
+                </div>
+                <div style="background: linear-gradient(135deg, #fff3e0, #ffcc80); padding: 20px; border-radius: 15px; border-left: 5px solid #ff9800;">
+                    <strong style="color: #f57c00;">🎯 Detection Bypass Results:</strong><br><br>
+                    • ZeroGPT: <span style="color: #4caf50; font-weight: bold;">0% AI Detection</span><br>
+                    • Quillbot: <span style="color: #4caf50; font-weight: bold;">100% Human</span><br>
+                    • GPTZero: <span style="color: #4caf50; font-weight: bold;">Undetectable</span><br>
+                    • Originality.ai: <span style="color: #4caf50; font-weight: bold;">Bypassed</span><br>
+                    • Copyleaks: <span style="color: #4caf50; font-weight: bold;">Human Content</span><br>
+                    • Turnitin: <span style="color: #4caf50; font-weight: bold;">Original</span>
+                </div>
+            </div>
+        </div>
+        """)
+        
+        gr.HTML("""
+        <div class="feature-card">
+            <h3 style="color: #2c3e50; margin-bottom: 20px;">💡 How It Works - 5-Pass Humanization Process:</h3>
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 20px; margin: 20px 0;">
+                <div style="background: #f8f9fa; padding: 18px; border-radius: 12px; border-left: 4px solid #007bff; text-align: center;">
+                    <strong style="color: #007bff;">🔄 Pass 1: Pattern Elimination</strong><br>
+                    Removes AI-flagged words and phrases
+                </div>
+                <div style="background: #f8f9fa; padding: 18px; border-radius: 12px; border-left: 4px solid #28a745; text-align: center;">
+                    <strong style="color: #28a745;">🎭 Pass 2: Structure Variation</strong><br>
+                    Restructures sentences naturally
+                </div>
+                <div style="background: #f8f9fa; padding: 18px; border-radius: 12px; border-left: 4px solid #ffc107; text-align: center;">
+                    <strong style="color: #e65100;">📚 Pass 3: Vocabulary Enhancement</strong><br>
+                    Replaces with contextual synonyms
+                </div>
+                <div style="background: #f8f9fa; padding: 18px; border-radius: 12px; border-left: 4px solid #dc3545; text-align: center;">
+                    <strong style="color: #dc3545;">✨ Pass 4: Human Touches</strong><br>
+                    Adds natural contractions and flow
+                </div>
+                <div style="background: #f8f9fa; padding: 18px; border-radius: 12px; border-left: 4px solid #6f42c1; text-align: center;">
+                    <strong style="color: #6f42c1;">🎯 Pass 5: Final Polish</strong><br>
+                    Advanced paraphrasing and optimization
+                </div>
+            </div>
+        </div>
+        """)
+        
+        # Event handlers
+        btn.click(
+            fn=process_text_advanced, 
+            inputs=[input_text, intensity], 
+            outputs=[output_text, analysis]
+        )
+        
+        input_text.submit(
+            fn=process_text_advanced, 
+            inputs=[input_text, intensity], 
+            outputs=[output_text, analysis]
+        )
+    
+    return interface
+
+if __name__ == "__main__":
+    print("🚀 Starting Advanced AI Humanizer Pro...")
+    app = create_enhanced_interface()
+    app.launch(
+        server_name="0.0.0.0", 
+        server_port=7860, 
+        show_error=True,
+        share=False
+    )
+

d.py

@@ -0,0 +1,158 @@
+import streamlit as st
+import nltk
+import os
+from enhanced_text_humanizer import EnhancedTextHumanizer
+import time
+# Set NLTK data path
+nltk.data.path.append('./nltk_data')
+
+# Download required NLTK resources at startup
+@st.cache_resource
+def download_nltk_resources():
+    try:
+        # Create NLTK data directory if it doesn't exist
+        os.makedirs('nltk_data', exist_ok=True)
+        
+        # Download required resources
+        nltk.download('punkt', download_dir='./nltk_data')
+        nltk.download('punkt_tab', download_dir='./nltk_data')
+        nltk.download('averaged_perceptron_tagger', download_dir='./nltk_data')
+        nltk.download('wordnet', download_dir='./nltk_data')
+        nltk.download('omw-1.4', download_dir='./nltk_data')
+        
+        return True
+    except Exception as e:
+        st.error(f"Error downloading NLTK resources: {e}")
+        return False
+
+def initialize_humanizer():
+    # First ensure NLTK resources are downloaded
+    resources_downloaded = download_nltk_resources()
+    
+    with st.spinner('Loading language models... This may take a moment.'):
+        humanizer = EnhancedTextHumanizer()
+    return humanizer
+
+
+
+
+def main():
+    st.set_page_config(
+        page_title="Text Humanizer App",
+        page_icon="🤖",
+        layout="wide"
+    )
+
+    st.title("🤖 Enhanced Text Humanizer")
+    st.markdown("""
+    Transform formal text into more natural, human-like language with various personality styles and regional dialects.
+    """)
+
+    # Initialize the humanizer
+    if 'humanizer' not in st.session_state:
+        st.session_state.humanizer = initialize_humanizer()
+
+    # Create two columns for input and output
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.subheader("Input Text")
+        input_text = st.text_area(
+            "Enter your text here:",
+            height=200,
+            placeholder="Type or paste your text here..."
+        )
+
+        st.subheader("Customization Options")
+        
+        # Personality selection
+        personality = st.selectbox(
+            "Select Personality Style:",
+            ['casual', 'formal', 'academic', 'enthusiastic'],
+            help="Choose the personality style for the output text"
+        )
+
+        # Regional dialect selection
+        dialect = st.selectbox(
+            "Select Regional Dialect:",
+            [None, 'us_south', 'british'],
+            help="Choose a regional dialect (optional)"
+        )
+
+        # Emotional tone selection
+        emotional_tone = st.selectbox(
+            "Select Emotional Tone:",
+            [None, 'positive', 'negative', 'neutral'],
+            help="Choose the emotional tone (optional)"
+        )
+
+        # Transformation intensity
+        intensity = st.slider(
+            "Transformation Intensity:",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.7,
+            step=0.1,
+            help="Control how much the text is transformed"
+        )
+
+        # Error inclusion
+        add_errors = st.checkbox(
+            "Include Natural Speech Errors",
+            value=True,
+            help="Add realistic speech/typing errors"
+        )
+
+    with col2:
+        st.subheader("Output Text")
+        if st.button("Transform Text", type="primary"):
+            if input_text.strip():
+                try:
+                    with st.spinner('Transforming text...'):
+                        humanized_text = st.session_state.humanizer.humanize_text(
+                            input_text,
+                            intensity=intensity,
+                            personality=personality,
+                            add_errors=add_errors,
+                            regional_dialect=dialect,
+                            emotional_tone=emotional_tone
+                        )
+                        st.text_area(
+                            "Transformed Text:",
+                            value=humanized_text,
+                            height=400,
+                            disabled=True
+                        )
+                        
+                        # Show transformation details
+                        st.success("Text transformation complete!")
+                        st.markdown("### Transformation Details")
+                        st.markdown(f"""
+                        - **Personality**: {personality}
+                        - **Dialect**: {dialect if dialect else 'None'}
+                        - **Emotional Tone**: {emotional_tone if emotional_tone else 'Auto-detected'}
+                        - **Intensity**: {intensity}
+                        - **Speech Errors**: {'Enabled' if add_errors else 'Disabled'}
+                        """)
+                except Exception as e:
+                    st.error(f"An error occurred: {str(e)}")
+            else:
+                st.warning("Please enter some text to transform.")
+
+    # Add footer with information
+    st.markdown("---")
+    st.markdown("""
+    ### About This Tool
+    This text humanizer uses advanced NLP techniques to transform formal text into more natural, human-like language. 
+    It can apply different personality styles, regional dialects, and emotional tones to the text.
+    
+    **Features:**
+    - Multiple personality styles
+    - Regional dialect support
+    - Emotional tone adjustment
+    - Controllable transformation intensity
+    - Natural speech error simulation
+    """)
+
+if __name__ == "__main__":
+    main()

pipeline.py

@@ -0,0 +1,605 @@
+"""
+Unified Humanization Pipeline
+Chains three humanization approaches in optimal order for maximum AI-detection bypass.
+
+Pipeline Order:
+  Stage 1: T5 Humanizer (a.py) — fine-tuned on 39k samples, best initial paraphrase
+  Stage 2: Qwen LLM Rewrite (b.py) — deep semantic rewrite via instruction-tuned LLM
+  Stage 3: Multi-Pass Cleanup (c.py) — AI pattern removal, restructuring, contractions, human touches
+  Verify:  RoBERTa AI Detector (b.py) — sentence-level AI probability check
+"""
+
+import gradio as gr
+import torch
+import re
+import random
+import math
+import numpy as np
+import os
+from collections import defaultdict, Counter
+from typing import List, Dict, Tuple
+from transformers import (
+    pipeline as hf_pipeline,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    T5Tokenizer,
+    T5ForConditionalGeneration,
+    GenerationConfig,
+)
+
+# ── NLTK setup ───────────────────────────────────────────────────────
+import ssl
+import nltk
+
+# Fix SSL certificate issue on macOS
+try:
+    ssl._create_default_https_context = ssl._create_unverified_context
+except AttributeError:
+    pass
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Use home directory for NLTK data (already downloaded there)
+NLTK_DIR = os.path.join(os.path.expanduser("~"), "nltk_data")
+os.makedirs(NLTK_DIR, exist_ok=True)
+nltk.data.path.insert(0, NLTK_DIR)
+
+for _res in ["punkt", "punkt_tab", "averaged_perceptron_tagger",
+             "stopwords", "wordnet", "omw-1.4"]:
+    try:
+        nltk.download(_res, download_dir=NLTK_DIR, quiet=True)
+    except Exception:
+        pass
+
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.corpus import wordnet, stopwords
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+STOP_WORDS = set(stopwords.words("english"))
+
+# =====================================================================
+#  STAGE 1 — T5 Humanizer Model (from a.py)
+#  Fine-tuned on 39,776 humanization samples.  Best initial paraphrase.
+# =====================================================================
+
+_t5_model = None
+_t5_tokenizer = None
+
+def _load_t5():
+    global _t5_model, _t5_tokenizer
+    if _t5_model is None:
+        print("Loading Stage 1: T5 Humanizer model …")
+        MODEL_PATH = "harryroger798/humanizer-model-v3"
+        _t5_tokenizer = T5Tokenizer.from_pretrained(MODEL_PATH)
+        _t5_model = T5ForConditionalGeneration.from_pretrained(MODEL_PATH)
+        print("  Stage 1 ready.")
+    return _t5_model, _t5_tokenizer
+
+
+def stage1_t5_humanize(text: str) -> str:
+    """Initial paraphrase using the fine-tuned T5 humanizer."""
+    if not text.strip():
+        return text
+    model, tokenizer = _load_t5()
+
+    inputs = tokenizer(
+        f"humanize: {text}",
+        return_tensors="pt",
+        max_length=512,
+        truncation=True,
+    )
+    outputs = model.generate(
+        **inputs,
+        max_length=512,
+        num_beams=4,
+        early_stopping=True,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        repetition_penalty=2.5,
+        no_repeat_ngram_size=3,
+        length_penalty=1.0,
+    )
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Repetition guard — if model loops, fall back to original text
+    words = result.split()
+    if len(words) > 10:
+        counts = Counter(words)
+        if max(counts.values()) > len(words) * 0.3:
+            return text
+    return result
+
+
+# =====================================================================
+#  STAGE 2 — Qwen LLM Rewrite (from b.py)
+#  Instruction-tuned 1.5B model does a deep semantic rewrite.
+# =====================================================================
+
+_qwen_pipe = None
+
+def _load_qwen():
+    global _qwen_pipe
+    if _qwen_pipe is None:
+        print("Loading Stage 2: Qwen 2.5-1.5B-Instruct …")
+        model_id = "Qwen/Qwen2.5-1.5B-Instruct"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+            device_map="auto" if DEVICE == "cuda" else None,
+        )
+        _qwen_pipe = hf_pipeline("text-generation", model=model, tokenizer=tokenizer)
+        print("  Stage 2 ready.")
+    return _qwen_pipe
+
+
+REWRITE_PROMPTS = {
+    "Natural": "Rewrite this to sound completely natural, human-written — vary sentence length, use contractions, slight imperfections.",
+    "Casual": "Rewrite this in a relaxed, friendly, conversational tone like a real person chatting.",
+    "Academic": "Rewrite this in clear, formal academic style with precise and sophisticated language.",
+    "Professional": "Rewrite this in a crisp, professional business tone — confident and authoritative.",
+}
+
+
+def stage2_qwen_rewrite(text: str, style: str = "Natural", intensity: float = 0.7) -> str:
+    """Deep semantic rewrite using Qwen instruction-tuned LLM."""
+    if not text.strip():
+        return text
+    pipe = _load_qwen()
+
+    tone = REWRITE_PROMPTS.get(style, REWRITE_PROMPTS["Natural"])
+
+    prompt = (
+        "<|im_start|>system\n"
+        "You are an expert editor that removes AI stiffness and makes text feel authentically human.\n"
+        "Keep original meaning 100%. Improve flow, rhythm, vocabulary variety. "
+        "Output ONLY the rewritten text.<|im_end|>\n"
+        f"<|im_start|>user\n{tone}\nText:\n{text}<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+
+    gen_config = GenerationConfig(
+        max_new_tokens=600,
+        temperature=0.4 + float(intensity) * 0.5,
+        top_p=0.92,
+        repetition_penalty=1.08,
+        do_sample=True,
+        pad_token_id=pipe.tokenizer.eos_token_id,
+        eos_token_id=pipe.tokenizer.eos_token_id,
+    )
+    gen_config.max_length = None
+
+    try:
+        output = pipe(prompt, generation_config=gen_config, num_return_sequences=1)[0][
+            "generated_text"
+        ]
+        if "assistant" in output:
+            rewritten = output.split("assistant", 1)[-1].strip()
+        else:
+            rewritten = output[len(prompt) :].strip()
+        return rewritten.strip() if rewritten.strip() else text
+    except Exception as e:
+        print(f"Stage 2 error: {e}")
+        return text
+
+
+# =====================================================================
+#  STAGE 3 — Multi-Pass Cleanup (from c.py, optimized)
+#  Removes AI-flagged patterns, restructures sentences, adds
+#  contractions and human touches.  Conflicts with a.py resolved:
+#  - No contraction EXPANSION (a.py did this, we skip it)
+#  - Synonym direction is casual-ward only
+# =====================================================================
+
+# AI-flagged words/phrases → more natural replacements
+AI_PATTERNS = {
+    r"\bdelve into\b": ["explore", "examine", "look into", "dig into", "study"],
+    r"\bembark upon?\b": ["begin", "start", "kick off", "launch", "set out"],
+    r"\ba testament to\b": ["proof of", "evidence of", "shows", "reflects"],
+    r"\blandscape of\b": ["world of", "field of", "area of", "space of"],
+    r"\bnavigating\b": ["handling", "managing", "dealing with", "tackling"],
+    r"\bmeticulous\b": ["careful", "thorough", "detailed", "precise"],
+    r"\bintricate\b": ["complex", "detailed", "elaborate", "complicated"],
+    r"\bmyriad\b": ["many", "numerous", "various", "lots of"],
+    r"\bplethora\b": ["abundance", "wealth", "range", "loads"],
+    r"\bparadigm\b": ["model", "framework", "approach", "method"],
+    r"\bsynergy\b": ["teamwork", "cooperation", "collaboration"],
+    r"\bleverage\b": ["use", "employ", "tap into", "make use of"],
+    r"\bfacilitate\b": ["help", "enable", "support", "make easier"],
+    r"\boptimize\b": ["improve", "enhance", "refine", "boost"],
+    r"\bstreamline\b": ["simplify", "improve", "smooth out"],
+    r"\brobust\b": ["strong", "reliable", "solid", "effective"],
+    r"\bseamless\b": ["smooth", "easy", "fluid", "effortless"],
+    r"\binnovative\b": ["creative", "original", "new", "fresh"],
+    r"\bcutting-edge\b": ["advanced", "modern", "latest", "leading"],
+    r"\bstate-of-the-art\b": ["advanced", "modern", "top-notch"],
+    r"\bfurthermore\b": ["also", "plus", "on top of that", "besides"],
+    r"\bmoreover\b": ["also", "plus", "what's more", "besides"],
+    r"\bnevertheless\b": ["still", "yet", "even so", "all the same"],
+    r"\bconsequently\b": ["so", "as a result", "because of this"],
+    r"\bin conclusion\b": ["finally", "to wrap up", "in the end", "lastly"],
+    r"\bin order to\b": ["to", "so we can", "aiming to"],
+    r"\bdue to the fact that\b": ["because", "since", "given that"],
+    r"\bwith regard to\b": ["about", "regarding", "when it comes to"],
+    r"\bin terms of\b": ["regarding", "as for", "about"],
+    r"\bprior to\b": ["before", "ahead of", "earlier than"],
+    r"\bsubsequent to\b": ["after", "following", "once"],
+    r"\bcomprehensive\b": ["complete", "thorough", "detailed", "full"],
+    r"\bfundamental\b": ["basic", "essential", "core", "key"],
+    r"\bsubstantial\b": ["significant", "considerable", "big", "major"],
+    r"\bimplement\b": ["put in place", "carry out", "apply", "use"],
+    r"\butilize\b": ["use", "employ", "make use of", "tap into"],
+    r"\bdemonstrate\b": ["show", "prove", "reveal", "display"],
+    r"\bestablish\b": ["set up", "create", "build", "start"],
+    r"\bmaintain\b": ["keep", "preserve", "continue", "sustain"],
+    r"\bobtain\b": ["get", "gain", "secure", "pick up"],
+}
+
+# Contractions to ADD (making text sound human/casual)
+CONTRACTIONS = {
+    r"\bit is\b": "it's", r"\bthat is\b": "that's", r"\bthere is\b": "there's",
+    r"\bwho is\b": "who's", r"\bwhat is\b": "what's", r"\bwhere is\b": "where's",
+    r"\bthey are\b": "they're", r"\bwe are\b": "we're", r"\byou are\b": "you're",
+    r"\bI am\b": "I'm", r"\bhe is\b": "he's", r"\bshe is\b": "she's",
+    r"\bcannot\b": "can't", r"\bdo not\b": "don't", r"\bdoes not\b": "doesn't",
+    r"\bwill not\b": "won't", r"\bwould not\b": "wouldn't",
+    r"\bshould not\b": "shouldn't", r"\bcould not\b": "couldn't",
+    r"\bhave not\b": "haven't", r"\bhas not\b": "hasn't", r"\bhad not\b": "hadn't",
+    r"\bis not\b": "isn't", r"\bare not\b": "aren't",
+    r"\bwas not\b": "wasn't", r"\bwere not\b": "weren't",
+    r"\blet us\b": "let's", r"\bI will\b": "I'll", r"\bI would\b": "I'd",
+    r"\byou will\b": "you'll", r"\bwe will\b": "we'll", r"\bthey will\b": "they'll",
+}
+
+HUMAN_STARTERS = [
+    "Actually,", "Honestly,", "Basically,", "Really,", "Generally,",
+    "Usually,", "Often,", "Clearly,", "Naturally,", "Definitely,",
+    "Interestingly,", "What's more,", "Plus,", "Also,", "Besides,",
+    "In fact,", "Of course,", "Frankly,", "To be honest,", "The thing is,",
+]
+
+NATURAL_TRANSITIONS = [
+    "And here's the thing:", "But here's what's interesting:",
+    "So, what does this mean?", "Here's why this matters:",
+    "Think about it this way:", "The reality is:", "The truth is:",
+]
+
+WORD_GROUPS = {
+    "analyze": ["examine", "study", "investigate", "explore", "review"],
+    "important": ["crucial", "vital", "essential", "key", "critical"],
+    "shows": ["demonstrates", "reveals", "indicates", "displays"],
+    "understand": ["grasp", "realize", "recognize", "appreciate"],
+    "develop": ["create", "build", "form", "generate", "produce"],
+    "improve": ["enhance", "refine", "advance", "boost", "better"],
+    "consider": ["think about", "evaluate", "contemplate", "ponder"],
+    "different": ["various", "diverse", "distinct", "alternative"],
+    "effective": ["successful", "efficient", "productive", "useful"],
+    "significant": ["important", "notable", "considerable", "major"],
+}
+
+
+def _replace_ai_patterns(text: str, prob: float = 0.85) -> str:
+    """Replace known AI-flagged words with natural alternatives."""
+    for pattern, replacements in AI_PATTERNS.items():
+        for match in reversed(list(re.finditer(pattern, text, re.IGNORECASE))):
+            if random.random() < prob:
+                text = text[: match.start()] + random.choice(replacements) + text[match.end() :]
+    return text
+
+
+def _add_contractions(text: str, prob: float = 0.7) -> str:
+    """Add natural contractions."""
+    for pattern, contraction in CONTRACTIONS.items():
+        if re.search(pattern, text, re.IGNORECASE) and random.random() < prob:
+            text = re.sub(pattern, contraction, text, flags=re.IGNORECASE)
+    return text
+
+
+def _restructure_sentence(sentence: str) -> str:
+    """Randomly restructure a sentence for variation."""
+    strategies = [
+        # Move adverb clause
+        (r"^(.*?),\s*(because|since|when|if|although|while)\s+(.*?)([.!?])$",
+         r"\2 \3, \1\4"),
+        (r"^(Although|While|Since|Because|When|If)\s+(.*?),\s*(.*?)([.!?])$",
+         r"\3, \1 \2\4"),
+    ]
+    for pat, rep in strategies:
+        if re.search(pat, sentence, re.IGNORECASE):
+            result = re.sub(pat, rep, sentence, flags=re.IGNORECASE)
+            if len(result.split()) >= 3:
+                return result.strip()
+    return sentence
+
+
+def _split_long_sentence(sentence: str) -> str:
+    """Split overly long compound sentences."""
+    conjunctions = [", and ", ", but ", ", so ", ", yet "]
+    for conj in conjunctions:
+        if conj in sentence and len(sentence.split()) > 15:
+            parts = sentence.split(conj, 1)
+            if len(parts) == 2 and len(parts[0].split()) > 3 and len(parts[1].split()) > 3:
+                first = parts[0].strip().rstrip(".") + "."
+                second = parts[1].strip()
+                if second and second[0].islower():
+                    second = second[0].upper() + second[1:]
+                connector = random.choice(["Also,", "Plus,", "What's more,", "On top of that,"])
+                return f"{first} {connector} {second[0].lower() + second[1:]}"
+    return sentence
+
+
+def _enhance_vocabulary(text: str, prob: float = 0.3) -> str:
+    """Replace repeated words with contextual synonyms."""
+    words = word_tokenize(text)
+    usage = Counter(w.lower() for w in words if w.isalpha() and len(w) > 3)
+    enhanced = []
+    for word in words:
+        wl = word.lower()
+        if (word.isalpha() and len(word) > 3 and wl not in STOP_WORDS
+                and usage.get(wl, 0) > 1 and random.random() < prob):
+            # Check predefined groups
+            for base, syns in WORD_GROUPS.items():
+                if wl == base or wl in syns:
+                    candidates = [s for s in ([base] + syns) if s != wl]
+                    if candidates:
+                        enhanced.append(random.choice(candidates))
+                        usage[wl] -= 1
+                        break
+            else:
+                # Try WordNet
+                synsets = wordnet.synsets(wl)
+                syn_candidates = []
+                for ss in synsets[:2]:
+                    for lemma in ss.lemmas():
+                        s = lemma.name().replace("_", " ")
+                        if s != wl and len(s) > 2 and abs(len(s) - len(word)) <= 3:
+                            syn_candidates.append(s)
+                if syn_candidates:
+                    enhanced.append(random.choice(syn_candidates[:3]))
+                    usage[wl] -= 1
+                else:
+                    enhanced.append(word)
+        else:
+            enhanced.append(word)
+    return " ".join(enhanced)
+
+
+def _add_human_touches(text: str, prob: float = 0.25) -> str:
+    """Add natural sentence starters, transitions, fillers."""
+    sentences = sent_tokenize(text)
+    result = []
+    for i, sent in enumerate(sentences):
+        current = sent
+        # Natural starters on ~25% of non-first sentences
+        if i > 0 and random.random() < prob and len(current.split()) > 6:
+            starter = random.choice(HUMAN_STARTERS)
+            current = f"{starter} {current[0].lower() + current[1:]}"
+        # Natural transitions rarely
+        if i > 0 and random.random() < prob * 0.2:
+            transition = random.choice(NATURAL_TRANSITIONS)
+            current = f"{transition} {current[0].lower() + current[1:]}"
+        result.append(current)
+    return " ".join(result)
+
+
+def _final_cleanup(text: str) -> str:
+    """Fix spacing, punctuation, capitalization."""
+    text = re.sub(r"\s+", " ", text)
+    text = re.sub(r"\s+([,.!?;:])", r"\1", text)
+    text = re.sub(r"([,.!?;:])\s*([A-Z])", r"\1 \2", text)
+    text = re.sub(r"\.+", ".", text)
+    sentences = sent_tokenize(text)
+    corrected = []
+    for s in sentences:
+        if s and s[0].islower():
+            s = s[0].upper() + s[1:]
+        corrected.append(s)
+    return " ".join(corrected).strip()
+
+
+def stage3_multipass_cleanup(text: str, intensity: int = 2) -> str:
+    """Multi-pass cleanup: pattern removal → restructure → vocabulary → contractions → human touches."""
+    if not text.strip():
+        return text
+
+    prob_scale = {1: 0.5, 2: 0.75, 3: 1.0}.get(intensity, 0.75)
+    current = text
+
+    # Pass 1: Remove AI-flagged patterns
+    current = _replace_ai_patterns(current, prob=0.85 * prob_scale)
+
+    # Pass 2: Restructure sentences
+    sentences = sent_tokenize(current)
+    restructured = []
+    for sent in sentences:
+        if len(sent.split()) > 8 and random.random() < 0.5 * prob_scale:
+            sent = _restructure_sentence(sent)
+        if len(sent.split()) > 15 and random.random() < 0.4 * prob_scale:
+            sent = _split_long_sentence(sent)
+        restructured.append(sent)
+    current = " ".join(restructured)
+
+    # Pass 3: Vocabulary enhancement (replace repeated words)
+    current = _enhance_vocabulary(current, prob=0.3 * prob_scale)
+
+    # Pass 4: Add contractions + human touches
+    current = _add_contractions(current, prob=0.7 * prob_scale)
+    current = _add_human_touches(current, prob=0.25 * prob_scale)
+
+    # Final cleanup
+    current = _final_cleanup(current)
+    return current
+
+
+# =====================================================================
+#  VERIFICATION — RoBERTa AI Detector (from b.py)
+# =====================================================================
+
+_detector_pipe = None
+
+def _load_detector():
+    global _detector_pipe
+    if _detector_pipe is None:
+        print("Loading Detector: chatgpt-detector-roberta …")
+        _detector_pipe = hf_pipeline(
+            "text-classification",
+            model="Hello-SimpleAI/chatgpt-detector-roberta",
+            device=0 if DEVICE == "cuda" else -1,
+            torch_dtype=torch.float16 if DEVICE == "cuda" else None,
+        )
+        print("  Detector ready.")
+    return _detector_pipe
+
+
+def verify_detection(text: str) -> str:
+    """Run sentence-level AI detection and return an HTML report."""
+    if not text.strip():
+        return "No text to analyze."
+
+    sentences = [s.strip() for s in re.split(r"(?<=[.!?])\s+", text.strip()) if s.strip()]
+    pipe = _load_detector()
+    preds = pipe(sentences, truncation=True, max_length=512)
+
+    rows = []
+    total_ai = 0.0
+    for sent, pred in zip(sentences, preds):
+        label = pred["label"].lower()
+        score = pred["score"]
+        ai_prob = score * 100 if any(x in label for x in ["fake", "ai", "generated"]) else (1 - score) * 100
+        total_ai += ai_prob
+        tag = "Very likely AI" if ai_prob > 85 else "Likely AI" if ai_prob > 60 else "Likely Human"
+        color = "#dc2626" if ai_prob > 85 else "#d97706" if ai_prob > 60 else "#16a34a"
+        rows.append(
+            f"<div style='padding:8px;margin:4px 0;border-left:4px solid {color};'>"
+            f"<strong>{tag} ({ai_prob:.1f}%)</strong><br>{sent}</div>"
+        )
+
+    avg = total_ai / len(sentences) if sentences else 0
+    summary = f"<h3>Overall AI probability: {avg:.1f}%</h3>"
+    return summary + "".join(rows)
+
+
+# =====================================================================
+#  FULL PIPELINE
+# =====================================================================
+
+def run_pipeline(
+    text: str,
+    style: str = "Natural",
+    intensity: float = 0.7,
+    use_stage1: bool = True,
+    use_stage2: bool = True,
+    use_stage3: bool = True,
+    cleanup_intensity: int = 2,
+    progress=gr.Progress(track_tqdm=False),
+) -> Tuple[str, str, str, str]:
+    """
+    Run the full humanization pipeline.
+    Returns: (stage1_out, stage2_out, final_out, detection_html)
+    """
+    if not text.strip():
+        return "", "", "", ""
+
+    current = text
+    s1_out = s2_out = ""
+
+    # Stage 1: T5 Humanizer
+    if use_stage1:
+        progress(0.1, desc="Stage 1: T5 Humanizer …")
+        current = stage1_t5_humanize(current)
+        s1_out = current
+
+    # Stage 2: Qwen LLM Rewrite
+    if use_stage2:
+        progress(0.4, desc="Stage 2: Qwen LLM Rewrite …")
+        current = stage2_qwen_rewrite(current, style=style, intensity=intensity)
+        s2_out = current
+
+    # Stage 3: Multi-Pass Cleanup
+    if use_stage3:
+        progress(0.7, desc="Stage 3: Multi-Pass Cleanup …")
+        current = stage3_multipass_cleanup(current, intensity=cleanup_intensity)
+
+    # Verification
+    progress(0.9, desc="Verifying with AI detector …")
+    detection_html = verify_detection(current)
+
+    return s1_out, s2_out, current, detection_html
+
+
+# =====================================================================
+#  GRADIO UI
+# =====================================================================
+
+with gr.Blocks(title="Humanization Pipeline") as demo:
+    gr.Markdown(
+        "# Humanization Pipeline\n"
+        "**3-stage chain: T5 Humanizer → Qwen LLM Rewrite → Multi-Pass Cleanup → AI Detection Verify**"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_text = gr.Textbox(
+                label="Input Text (AI-generated)",
+                placeholder="Paste AI-generated text here …",
+                lines=10,
+            )
+
+            style_dropdown = gr.Dropdown(
+                choices=["Natural", "Casual", "Academic", "Professional"],
+                value="Natural",
+                label="Rewrite Style (Stage 2)",
+            )
+
+            intensity_slider = gr.Slider(
+                minimum=0.1, maximum=1.0, value=0.7, step=0.05,
+                label="LLM Rewrite Intensity (Stage 2)",
+            )
+
+            cleanup_intensity = gr.Radio(
+                choices=[("Light", 1), ("Standard", 2), ("Heavy", 3)],
+                value=2,
+                label="Cleanup Intensity (Stage 3)",
+            )
+
+            with gr.Row():
+                use_s1 = gr.Checkbox(label="Stage 1: T5 Humanizer", value=True)
+                use_s2 = gr.Checkbox(label="Stage 2: Qwen LLM", value=True)
+                use_s3 = gr.Checkbox(label="Stage 3: Multi-Pass", value=True)
+
+            run_btn = gr.Button("Run Pipeline", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            with gr.Accordion("Stage 1 Output (T5 Humanizer)", open=False):
+                s1_output = gr.Textbox(label="After Stage 1", lines=5)
+
+            with gr.Accordion("Stage 2 Output (Qwen LLM)", open=False):
+                s2_output = gr.Textbox(label="After Stage 2", lines=5)
+
+            final_output = gr.Textbox(
+                label="Final Humanized Text",
+                lines=10,
+            )
+
+            detection_result = gr.HTML(label="AI Detection Verification")
+
+    run_btn.click(
+        fn=run_pipeline,
+        inputs=[input_text, style_dropdown, intensity_slider,
+                use_s1, use_s2, use_s3, cleanup_intensity],
+        outputs=[s1_output, s2_output, final_output, detection_result],
+    )
+
+    gr.Examples(
+        examples=[
+            ["The rapid advancement of artificial intelligence technologies has significantly transformed numerous industries and daily life."],
+            ["Machine learning algorithms demonstrate superior performance in pattern recognition tasks across diverse datasets."],
+            ["In conclusion, leveraging cutting-edge methodologies facilitates the optimization of robust and seamless solutions."],
+        ],
+        inputs=input_text,
+        label="Test examples (heavily AI-flagged text)",
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=False, share=True)

requirements.txt

@@ -0,0 +1,8 @@
+gradio
+transformers<5
+torch
+sentencepiece
+protobuf
+accelerate
+nltk
+numpy<2