authentic_humanizer.py

import re
import random
import nltk
import numpy as np
from typing import List, Dict, Optional
import time
from collections import Counter
import statistics

# Robust NLTK data downloader that handles version differences
def ensure_nltk_data():
    """Download required NLTK data with fallbacks for different versions"""
    
    # Resources to download (try both old and new names)
    resources_to_try = [
        # Punkt tokenizer (try both versions)
        [('punkt', 'tokenizers/punkt'), ('punkt_tab', 'tokenizers/punkt_tab')],
        # Wordnet
        [('wordnet', 'corpora/wordnet')],
        # OMW data
        [('omw-1.4', 'corpora/omw-1.4')]
    ]
    
    for resource_group in resources_to_try:
        downloaded = False
        for resource_name, resource_path in resource_group:
            try:
                nltk.data.find(resource_path)
                print(f"✅ Found {resource_name}")
                downloaded = True
                break
            except LookupError:
                try:
                    print(f"🔄 Downloading {resource_name}...")
                    nltk.download(resource_name, quiet=True)
                    print(f"✅ Downloaded {resource_name}")
                    downloaded = True
                    break
                except Exception as e:
                    print(f"⚠️ Failed to download {resource_name}: {e}")
                    continue
        
        if not downloaded:
            resource_names = [name for name, _ in resource_group]
            print(f"❌ Could not download any of: {resource_names}")

# Alternative function that tries multiple approaches
def robust_nltk_setup():
    """More robust NLTK setup with multiple fallback strategies"""
    
    print("🔧 Setting up NLTK resources...")
    
    # Strategy 1: Try standard downloads
    try:
        ensure_nltk_data()
    except Exception as e:
        print(f"⚠️ Standard setup failed: {e}")
    
    # Strategy 2: Force download common resources
    common_resources = ['punkt', 'punkt_tab', 'wordnet', 'omw-1.4', 'averaged_perceptron_tagger']
    for resource in common_resources:
        try:
            nltk.download(resource, quiet=True)
            print(f"✅ Force downloaded {resource}")
        except Exception as e:
            print(f"⚠️ Could not force download {resource}: {e}")
    
    # Strategy 3: Test if tokenization works
    try:
        from nltk.tokenize import sent_tokenize, word_tokenize
        # Test with a simple sentence
        test_sentences = sent_tokenize("This is a test. This is another test.")
        test_words = word_tokenize("This is a test sentence.")
        print(f"✅ Tokenization test passed: {len(test_sentences)} sentences, {len(test_words)} words")
        return True
    except Exception as e:
        print(f"❌ Tokenization test failed: {e}")
        return False

# Run the robust setup
print("🚀 Loading Authentic AI Text Humanizer...")
setup_success = robust_nltk_setup()

# Try importing NLTK functions with fallbacks
try:
    from nltk.tokenize import sent_tokenize, word_tokenize
    from nltk.corpus import wordnet
    print("✅ NLTK imports successful")
    NLTK_AVAILABLE = True
except ImportError as e:
    print(f"❌ NLTK imports failed: {e}")
    print("🔄 Trying alternative tokenization methods...")
    NLTK_AVAILABLE = False
    
    # Fallback tokenization functions
    def sent_tokenize(text):
        """Fallback sentence tokenizer"""
        import re
        # Simple sentence splitting on periods, exclamation marks, question marks
        sentences = re.split(r'[.!?]+', text)
        return [s.strip() for s in sentences if s.strip()]
    
    def word_tokenize(text):
        """Fallback word tokenizer"""
        import re
        # Simple word splitting on whitespace and punctuation
        words = re.findall(r'\b\w+\b|[^\w\s]', text)
        return words
    
    # Mock wordnet for fallback
    class MockWordNet:
        def synsets(self, word):
            return []
    
    wordnet = MockWordNet()

# Advanced imports with fallbacks
def safe_import_with_fallback(module_name, component=None):
    """Safe import with fallback handling"""
    try:
        if component:
            module = __import__(module_name, fromlist=[component])
            return getattr(module, component), True
        else:
            return __import__(module_name), True
    except ImportError:
        return None, False
    except Exception:
        return None, False

# Load advanced models
SentenceTransformer, SENTENCE_TRANSFORMERS_AVAILABLE = safe_import_with_fallback('sentence_transformers', 'SentenceTransformer')
pipeline, TRANSFORMERS_AVAILABLE = safe_import_with_fallback('transformers', 'pipeline')

try:
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.metrics.pairwise import cosine_similarity as sklearn_cosine_similarity
    SKLEARN_AVAILABLE = True
except ImportError:
    SKLEARN_AVAILABLE = False

try:
    import torch
    TORCH_AVAILABLE = True
except ImportError:
    TORCH_AVAILABLE = False

class AuthenticAITextHumanizer:
    """
    Authentic AI Text Humanizer - Makes text truly sound human and natural
    Based on analysis of authentic human writing patterns
    """
    
    def __init__(self, enable_gpu=True):
        print("🎯 Initializing Authentic AI Text Humanizer...")
        print("✨ Designed to write like a real human - authentic & natural")
        
        self.enable_gpu = enable_gpu and TORCH_AVAILABLE
        self.nltk_available = NLTK_AVAILABLE
        
        # Initialize models and authentic patterns
        self._load_models()
        self._initialize_authentic_patterns()
        
        print("✅ Authentic AI Text Humanizer ready!")
        self._print_status()
    
    def _load_models(self):
        """Load AI models with graceful fallbacks"""
        self.similarity_model = None
        self.paraphraser = None
        
        # Load sentence transformer for quality control
        if SENTENCE_TRANSFORMERS_AVAILABLE:
            try:
                device = 'cuda' if self.enable_gpu and TORCH_AVAILABLE and torch.cuda.is_available() else 'cpu'
                self.similarity_model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
                print("✅ Advanced similarity model loaded")
            except Exception as e:
                print(f"⚠️ Similarity model unavailable: {e}")
        
        # Load paraphrasing model
        if TRANSFORMERS_AVAILABLE:
            try:
                device = 0 if self.enable_gpu and TORCH_AVAILABLE and torch.cuda.is_available() else -1
                self.paraphraser = pipeline(
                    "text2text-generation",
                    model="google/flan-t5-small",
                    device=device,
                    max_length=256
                )
                print("✅ AI paraphrasing model loaded")
            except Exception as e:
                print(f"⚠️ Paraphrasing model unavailable: {e}")
        
        # Fallback similarity using TF-IDF
        if SKLEARN_AVAILABLE:
            self.tfidf_vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1, 2), max_features=5000)
        else:
            self.tfidf_vectorizer = None
    
    def _initialize_authentic_patterns(self):
        """Initialize authentic human writing patterns"""
        
        # Authentic word replacements - how humans actually write
        self.authentic_replacements = {
            # Business jargon -> Natural language
            "utilize": ["use", "work with", "employ"],
            "facilitate": ["help", "make it easier to", "enable", "allow"],
            "demonstrate": ["show", "prove", "reveal", "display"],
            "implement": ["put in place", "start using", "set up", "roll out"],
            "optimize": ["improve", "make better", "enhance"],
            "leverage": ["use", "take advantage of", "make use of"],
            "comprehensive": ["complete", "thorough", "full", "extensive"],
            "substantial": ["significant", "major", "big", "considerable"],
            "exceptional": ["outstanding", "remarkable", "impressive", "excellent"],
            "systematic": ["structured", "organized", "methodical"],
            "revolutionary": ["groundbreaking", "innovative", "cutting-edge", "game-changing"],
            "unprecedented": ["never-before-seen", "unique", "extraordinary", "first-of-its-kind"],
            "methodology": ["approach", "method", "way", "strategy"],
            "enhancement": ["improvement", "upgrade", "boost"],
            "acquisition": ["purchase", "buying", "getting"],
            "transformation": ["change", "shift", "evolution"],
            "optimization": ["improvement", "fine-tuning", "enhancement"],
            "establishment": ["creation", "setup", "building"],
            "implementation": ["rollout", "launch", "deployment"],
            "operational": ["day-to-day", "working", "running"],
            "capabilities": ["abilities", "features", "what it can do"],
            "specifications": ["specs", "details", "features"],
            "functionality": ["features", "what it does", "capabilities"],
            "performance": ["how well it works", "results", "output"],
            "architecture": ["design", "structure", "framework"],
            "integration": ["bringing together", "combining", "merging"],
            "sustainability": ["long-term viability", "lasting success"],
            "competitive advantages": ["edge over competitors", "what sets us apart"]
        }
        
        # Remove robotic AI phrases completely
        self.ai_phrase_removals = {
            "furthermore,": ["Also,", "Plus,", "What's more,", "On top of that,", "Additionally,"],
            "moreover,": ["Also,", "Plus,", "What's more,", "Besides,"],
            "subsequently,": ["Then,", "Next,", "After that,", "Later,"],
            "consequently,": ["So,", "As a result,", "Therefore,", "This means"],
            "accordingly,": ["So,", "Therefore,", "As a result,"],
            "nevertheless,": ["However,", "But,", "Still,", "Even so,"],
            "nonetheless,": ["However,", "But,", "Still,", "Even so,"],
            "it is important to note that": ["Worth noting:", "Importantly,", "Keep in mind that", "Remember that"],
            "it is crucial to understand that": ["Here's what's important:", "You should know that", "The key thing is"],
            "it should be emphasized that": ["Importantly,", "Key point:", "Worth highlighting:"],
            "it is worth mentioning that": ["Also worth noting:", "By the way,", "Interestingly,"],
            "from a practical standpoint": ["In practice,", "Realistically,", "In real terms"],
            "in terms of implementation": ["When putting this into practice,", "For implementation,", "To make this work"],
            "with respect to the aforementioned": ["Regarding what I mentioned,", "About that,", "On this point"],
            "as previously mentioned": ["As I said earlier,", "Like I mentioned,", "As noted before"],
            "in light of this": ["Because of this,", "Given this,", "With this in mind"],
            "upon careful consideration": ["After thinking about it,", "Looking at this closely,", "When you consider"],
            "in the final analysis": ["Ultimately,", "When it comes down to it,", "In the end"],
            "one must consider": ["You should think about", "Consider", "Keep in mind"],
            "it is evident that": ["Clearly,", "Obviously,", "You can see that"],
            "it can be observed that": ["You can see", "It's clear that", "Obviously"]
        }
        
        # Natural sentence starters for conversational flow
        self.natural_starters = [
            "Here's the thing:", "Look,", "The reality is", "What's interesting is", "The truth is",
            "Think about it:", "Consider this:", "Here's what happens:", "What this means is",
            "The bottom line is", "Simply put,", "In other words,", "To put it another way,",
            "What you'll find is", "The key insight is", "What stands out is"
        ]
        
        # Conversational connectors
        self.conversational_connectors = [
            "And here's why:", "Plus,", "On top of that,", "What's more,", "Beyond that,",
            "Here's another thing:", "But wait, there's more:", "And that's not all:",
            "Speaking of which,", "Along those lines,", "In the same vein,"
        ]
        
        # Sentence ending variations
        self.authentic_endings = [
            "which is pretty impressive", "and that's significant", "which makes sense",
            "and that matters", "which is key", "and this is important"
        ]
        
        # Professional contractions
        self.contractions = {
            "do not": "don't", "does not": "doesn't", "did not": "didn't", "will not": "won't",
            "would not": "wouldn't", "should not": "shouldn't", "could not": "couldn't", 
            "cannot": "can't", "is not": "isn't", "are not": "aren't", "was not": "wasn't", 
            "were not": "weren't", "have not": "haven't", "has not": "hasn't", "had not": "hadn't",
            "I am": "I'm", "you are": "you're", "he is": "he's", "she is": "she's", "it is": "it's",
            "we are": "we're", "they are": "they're", "I have": "I've", "you have": "you've",
            "we have": "we've", "they have": "they've", "I will": "I'll", "you will": "you'll",
            "we will": "we'll", "they will": "they'll", "that is": "that's", "there is": "there's",
            "here is": "here's", "what is": "what's", "where is": "where's", "who is": "who's"
        }
    
    def preserve_structure(self, original: str, processed: str) -> str:
        """Preserve original text structure (paragraphs, formatting)"""
        # Split by double newlines (paragraphs)
        original_paragraphs = re.split(r'\n\s*\n', original)
        if len(original_paragraphs) <= 1:
            return processed
        
        # Split processed text into sentences
        try:
            processed_sentences = sent_tokenize(processed)
        except Exception as e:
            print(f"⚠️ Sentence tokenization failed, using fallback: {e}")
            processed_sentences = re.split(r'[.!?]+', processed)
            processed_sentences = [s.strip() for s in processed_sentences if s.strip()]
        
        # Try to maintain paragraph structure
        result_paragraphs = []
        sentence_idx = 0
        
        for para in original_paragraphs:
            try:
                para_sentences = sent_tokenize(para)
            except Exception:
                para_sentences = re.split(r'[.!?]+', para)
                para_sentences = [s.strip() for s in para_sentences if s.strip()]
            
            para_sentence_count = len(para_sentences)
            
            if sentence_idx + para_sentence_count <= len(processed_sentences):
                para_processed = ' '.join(processed_sentences[sentence_idx:sentence_idx + para_sentence_count])
                result_paragraphs.append(para_processed)
                sentence_idx += para_sentence_count
            else:
                # Add remaining sentences to this paragraph
                remaining = ' '.join(processed_sentences[sentence_idx:])
                if remaining:
                    result_paragraphs.append(remaining)
                break
        
        return '\n\n'.join(result_paragraphs)
    
    def break_long_sentences(self, text: str) -> str:
        """Break overly long sentences into natural, shorter ones"""
        try:
            sentences = sent_tokenize(text)
        except Exception:
            sentences = re.split(r'[.!?]+', text)
            sentences = [s.strip() for s in sentences if s.strip()]
        
        processed_sentences = []
        
        for sentence in sentences:
            words = sentence.split()
            
            # Break sentences longer than 20 words
            if len(words) > 20:
                # Find natural break points
                break_words = ['and', 'but', 'while', 'because', 'since', 'when', 'where', 'which', 'that', 'as']
                
                for break_word in break_words:
                    break_positions = [i for i, word in enumerate(words) if word.lower() == break_word]
                    
                    for pos in break_positions:
                        # Only break if it creates reasonable sentence lengths
                        if 8 <= pos <= len(words) - 8:
                            first_part = ' '.join(words[:pos]).strip()
                            second_part = ' '.join(words[pos:]).strip()
                            
                            if first_part and second_part:
                                # Ensure proper capitalization
                                if not first_part.endswith('.'):
                                    first_part += '.'
                                second_part = second_part[0].upper() + second_part[1:] if len(second_part) > 1 else second_part.upper()
                                
                                processed_sentences.extend([first_part, second_part])
                                break
                    else:
                        continue
                    break
                else:
                    # No good break point found, keep original
                    processed_sentences.append(sentence)
            else:
                processed_sentences.append(sentence)
        
        return ' '.join(processed_sentences)
    
    def apply_authentic_word_replacements(self, text: str, intensity: float = 0.8) -> str:
        """Replace business jargon with authentic, natural language"""
        try:
            words = word_tokenize(text)
        except Exception:
            words = re.findall(r'\b\w+\b|[^\w\s]', text)
        
        modified_words = []
        
        for word in words:
            word_clean = word.lower().strip('.,!?;:"')
            
            if word_clean in self.authentic_replacements and random.random() < intensity:
                replacements = self.authentic_replacements[word_clean]
                replacement = random.choice(replacements)
                
                # Preserve case
                if word.isupper():
                    replacement = replacement.upper()
                elif word.istitle():
                    replacement = replacement.title()
                
                modified_words.append(replacement)
            else:
                modified_words.append(word)
        
        # Reconstruct with proper spacing
        result = ""
        for i, word in enumerate(modified_words):
            if i > 0 and word not in ".,!?;:\"')":
                result += " "
            result += word
        
        return result
    
    def remove_ai_phrases(self, text: str, intensity: float = 0.9) -> str:
        """Remove robotic AI phrases and replace with natural alternatives"""
        
        # Sort by length (longest first) to avoid partial replacements
        sorted_phrases = sorted(self.ai_phrase_removals.items(), key=lambda x: len(x[0]), reverse=True)
        
        for ai_phrase, natural_alternatives in sorted_phrases:
            # Case-insensitive search
            pattern = re.compile(re.escape(ai_phrase), re.IGNORECASE)
            
            if pattern.search(text) and random.random() < intensity:
                replacement = random.choice(natural_alternatives)
                
                # Preserve original case style
                if ai_phrase[0].isupper():
                    replacement = replacement.capitalize()
                
                text = pattern.sub(replacement, text)
        
        return text
    
    def add_conversational_flow(self, text: str, style: str, intensity: float = 0.6) -> str:
        """Add natural, conversational flow to the text"""
        try:
            sentences = sent_tokenize(text)
        except Exception:
            sentences = re.split(r'[.!?]+', text)
            sentences = [s.strip() for s in sentences if s.strip()]
        
        if len(sentences) < 2:
            return text
        
        enhanced_sentences = []
        
        for i, sentence in enumerate(sentences):
            # Add conversational starters occasionally
            if (i == 0 or (i > 0 and random.random() < intensity * 0.3)) and style == "conversational":
                if random.random() < 0.4:
                    starter = random.choice(self.natural_starters)
                    sentence = starter + " " + sentence.lower()
            
            # Add conversational connectors between sentences
            elif i > 0 and random.random() < intensity * 0.2 and style == "conversational":
                connector = random.choice(self.conversational_connectors)
                sentence = connector + " " + sentence.lower()
            
            # Occasionally add authentic endings to sentences
            if random.random() < intensity * 0.1 and len(sentence.split()) > 8:
                if not sentence.endswith(('.', '!', '?')):
                    sentence += '.'
                ending = random.choice(self.authentic_endings)
                sentence = sentence[:-1] + ", " + ending + "."
            
            enhanced_sentences.append(sentence)
        
        return ' '.join(enhanced_sentences)
    
    def apply_natural_contractions(self, text: str, intensity: float = 0.7) -> str:
        """Apply contractions for natural flow"""
        
        # Sort by length (longest first) to avoid partial replacements
        sorted_contractions = sorted(self.contractions.items(), key=lambda x: len(x[0]), reverse=True)
        
        for formal, contracted in sorted_contractions:
            if random.random() < intensity:
                pattern = r'\b' + re.escape(formal) + r'\b'
                text = re.sub(pattern, contracted, text, flags=re.IGNORECASE)
        
        return text
    
    def add_human_variety(self, text: str, intensity: float = 0.4) -> str:
        """Add natural human writing variety and personality"""
        try:
            sentences = sent_tokenize(text)
        except Exception:
            sentences = re.split(r'[.!?]+', text)
            sentences = [s.strip() for s in sentences if s.strip()]
        
        varied_sentences = []
        
        for sentence in sentences:
            # Vary sentence structure
            if len(sentence.split()) > 12 and random.random() < intensity:
                # Sometimes start with a dependent clause
                if random.random() < 0.3:
                    # Move a prepositional phrase to the beginning
                    words = sentence.split()
                    prep_words = ['with', 'through', 'by', 'using', 'for', 'in', 'on', 'at']
                    
                    for j, word in enumerate(words):
                        if word.lower() in prep_words and j > 3:
                            # Find the end of the prepositional phrase
                            end_j = min(j + 4, len(words))
                            prep_phrase = ' '.join(words[j:end_j])
                            remaining = ' '.join(words[:j] + words[end_j:])
                            
                            if remaining:
                                sentence = prep_phrase.capitalize() + ', ' + remaining.lower()
                            break
                
                # Sometimes add emphasis with "really", "actually", "definitely"
                elif random.random() < 0.2:
                    emphasis_words = ['really', 'actually', 'definitely', 'truly', 'genuinely']
                    emphasis = random.choice(emphasis_words)
                    words = sentence.split()
                    
                    # Insert emphasis word after first few words
                    insert_pos = random.randint(2, min(5, len(words)-1))
                    words.insert(insert_pos, emphasis)
                    sentence = ' '.join(words)
            
            varied_sentences.append(sentence)
        
        return ' '.join(varied_sentences)
    
    def calculate_similarity(self, text1: str, text2: str) -> float:
        """Calculate semantic similarity"""
        if self.similarity_model:
            try:
                embeddings1 = self.similarity_model.encode([text1])
                embeddings2 = self.similarity_model.encode([text2])
                similarity = np.dot(embeddings1[0], embeddings2[0]) / (
                    np.linalg.norm(embeddings1[0]) * np.linalg.norm(embeddings2[0])
                )
                return float(similarity)
            except Exception:
                pass
        
        # Fallback to TF-IDF
        if self.tfidf_vectorizer and SKLEARN_AVAILABLE:
            try:
                tfidf_matrix = self.tfidf_vectorizer.fit_transform([text1, text2])
                similarity = sklearn_cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0]
                return float(similarity)
            except Exception:
                pass
        
        # Basic word overlap fallback
        try:
            words1 = set(word_tokenize(text1.lower()))
            words2 = set(word_tokenize(text2.lower()))
        except Exception:
            words1 = set(re.findall(r'\b\w+\b', text1.lower()))
            words2 = set(re.findall(r'\b\w+\b', text2.lower()))
        
        if not words1 or not words2:
            return 1.0 if text1 == text2 else 0.0
        
        intersection = words1.intersection(words2)
        union = words1.union(words2)
        return len(intersection) / len(union) if union else 1.0
    
    def humanize_text_authentic(self, 
                               text: str, 
                               style: str = "natural",
                               intensity: float = 0.7) -> Dict:
        """
        Authentic text humanization that makes text truly sound human
        
        Args:
            text: Input text to humanize
            style: 'natural' or 'conversational'
            intensity: Transformation intensity (0.0 to 1.0)
        
        Returns:
            Dictionary with results and metrics
        """
        if not text.strip():
            return {
                "original_text": text,
                "humanized_text": text,
                "similarity_score": 1.0,
                "changes_made": [],
                "processing_time_ms": 0.0,
                "style": style,
                "intensity": intensity,
                "structure_preserved": True
            }
        
        start_time = time.time()
        original_text = text
        humanized_text = text
        changes_made = []
        
        try:
            # Phase 1: Remove AI phrases and replace with natural alternatives
            if intensity > 0.2:
                before = humanized_text
                humanized_text = self.remove_ai_phrases(humanized_text, intensity * 0.95)
                if humanized_text != before:
                    changes_made.append("Replaced robotic phrases with natural language")
            
            # Phase 2: Break up long, complex sentences
            if intensity > 0.3:
                before = humanized_text
                humanized_text = self.break_long_sentences(humanized_text)
                if humanized_text != before:
                    changes_made.append("Broke up complex sentences for better flow")
            
            # Phase 3: Replace business jargon with authentic language
            if intensity > 0.4:
                before = humanized_text
                humanized_text = self.apply_authentic_word_replacements(humanized_text, intensity * 0.8)
                if humanized_text != before:
                    changes_made.append("Replaced jargon with natural, everyday language")
            
            # Phase 4: Add conversational flow and personality
            if intensity > 0.5:
                before = humanized_text
                humanized_text = self.add_conversational_flow(humanized_text, style, intensity * 0.6)
                if humanized_text != before:
                    changes_made.append("Added conversational flow and personality")
            
            # Phase 5: Apply natural contractions
            if intensity > 0.6:
                before = humanized_text
                humanized_text = self.apply_natural_contractions(humanized_text, intensity * 0.7)
                if humanized_text != before:
                    changes_made.append("Added natural contractions")
            
            # Phase 6: Add human variety and natural patterns
            if intensity > 0.7:
                before = humanized_text
                humanized_text = self.add_human_variety(humanized_text, intensity * 0.4)
                if humanized_text != before:
                    changes_made.append("Added natural human writing variety")
            
            # Phase 7: Preserve original structure
            humanized_text = self.preserve_structure(original_text, humanized_text)
            
            # Calculate quality metrics
            similarity_score = self.calculate_similarity(original_text, humanized_text)
            processing_time = (time.time() - start_time) * 1000
            
            # Quality control - revert if too different
            if similarity_score < 0.65:
                print(f"⚠️ Similarity too low ({similarity_score:.3f}), reverting changes")
                humanized_text = original_text
                similarity_score = 1.0
                changes_made = ["Reverted - maintained original meaning"]
            
        except Exception as e:
            print(f"❌ Error during authentic humanization: {e}")
            humanized_text = original_text
            similarity_score = 1.0
            changes_made = [f"Processing error - returned original: {str(e)[:100]}"]
        
        return {
            "original_text": original_text,
            "humanized_text": humanized_text,
            "similarity_score": similarity_score,
            "changes_made": changes_made,
            "processing_time_ms": (time.time() - start_time) * 1000,
            "style": style,
            "intensity": intensity,
            "structure_preserved": True,
            "word_count_original": len(original_text.split()),
            "word_count_humanized": len(humanized_text.split()),
            "character_count_original": len(original_text),
            "character_count_humanized": len(humanized_text)
        }
    
    def _print_status(self):
        """Print current status"""
        print("\n📊 AUTHENTIC AI TEXT HUMANIZER STATUS:")
        print("-" * 50)
        print(f"🧠 Advanced Similarity: {'✅' if self.similarity_model else '❌'}")
        print(f"🤖 AI Paraphrasing: {'✅' if self.paraphraser else '❌'}")
        print(f"📊 TF-IDF Fallback: {'✅' if self.tfidf_vectorizer else '❌'}")
        print(f"🚀 GPU Acceleration: {'✅' if self.enable_gpu else '❌'}")
        print(f"📚 NLTK Available: {'✅' if self.nltk_available else '❌ (using fallbacks)'}")
        print(f"✨ Authentic Patterns: ✅ LOADED")
        print(f"📝 Authentic Replacements: ✅ {len(self.authentic_replacements)} mappings")
        print(f"🚫 AI Phrase Removals: ✅ {len(self.ai_phrase_removals)} patterns")
        print(f"💬 Natural Contractions: ✅ {len(self.contractions)} patterns")
        print(f"🗣️ Conversational Elements: ✅ {len(self.natural_starters)} starters")
        print(f"🏗️ Structure Preservation: ✅ ENABLED")
        
        # Calculate feature completeness
        features = [
            bool(self.similarity_model),
            bool(self.paraphraser),
            bool(self.tfidf_vectorizer),
            True,  # Authentic patterns
            True,  # Sentence breaking
            True,  # Conversational flow
            True,  # Structure preservation
            True   # Quality control
        ]
        completeness = (sum(features) / len(features)) * 100
        print(f"🎯 Authentic System Completeness: {completeness:.1f}%")
        
        if completeness >= 80:
            print("🎉 READY FOR AUTHENTIC HUMANIZATION!")
        elif completeness >= 60:
            print("✅ Core features ready - some advanced features may be limited")
        else:
            print("⚠️ Basic mode - install additional dependencies for full features")

# For backward compatibility, use the same method name
UniversalAITextHumanizer = AuthenticAITextHumanizer

# Test function
if __name__ == "__main__":
    humanizer = AuthenticAITextHumanizer()
    
    # Test with your examples
    test_cases = [
        {
            "name": "Smartphone Description",
            "text": "Furthermore, this revolutionary smartphone demonstrates exceptional technological capabilities and utilizes advanced processing architecture to ensure optimal performance across all applications. Subsequently, users will experience significant improvements in their daily productivity and entertainment consumption. Moreover, the comprehensive design facilitates seamless integration with existing ecosystems while maintaining superior battery efficiency.",
            "style": "natural"
        },
        {
            "name": "Business Proposal",
            "text": "Our comprehensive proposal demonstrates significant value proposition and utilizes proven methodologies to ensure optimal project outcomes. Furthermore, the systematic implementation of our advanced framework will facilitate substantial improvements in your operational efficiency. It is important to note that our experienced team possesses exceptional expertise and demonstrates remarkable track record in delivering complex solutions.",
            "style": "conversational"
        }
    ]
    
    print(f"\n🧪 TESTING AUTHENTIC HUMANIZER")
    print("=" * 45)
    
    for i, test_case in enumerate(test_cases, 1):
        print(f"\n🔬 Test {i}: {test_case['name']}")
        print("-" * 50)
        print(f"📝 Original: {test_case['text']}")
        
        result = humanizer.humanize_text_authentic(
            text=test_case['text'],
            style=test_case['style'],
            intensity=0.8
        )
        
        print(f"✨ Authentic: {result['humanized_text']}")
        print(f"📊 Similarity: {result['similarity_score']:.3f}")
        print(f"⚡ Processing: {result['processing_time_ms']:.1f}ms")
        print(f"🔧 Changes: {', '.join(result['changes_made'])}")
    
    print(f"\n🎉 Authentic testing completed!")
    print(f"✨ Ready for truly human-like text transformation!")