import os
import re
import torch
import logging
import gc
import sys
import pwd  # Added for monkey patch
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Dict, List, Optional
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from tokenizers.normalizers import Sequence, Replace, Strip
from tokenizers import Regex
from huggingface_hub import hf_hub_download  # Added for reliable HF downloads

# =====================================================
# 🛠️ Monkey Patch for Docker/Container UID Issue
# =====================================================
# Fix for 'getpwuid(): uid not found: 1000' in containerized environments
def patched_getpwuid(uid_num):
    try:
        return original_getpwuid(uid_num)
    except KeyError:
        if uid_num == os.getuid():
            # Create fake user entry
            return pwd.struct_pwent(
                name='dockeruser',
                passwd='x',
                uid=uid_num,
                gid=os.getgid(),
                gecos='Docker User',
                dir='/tmp',
                shell='/bin/sh'
            )
        raise

original_getpwuid = pwd.getpwuid
pwd.getpwuid = patched_getpwuid

# Set fallback env vars to avoid user-dependent paths
os.environ.setdefault('HOME', '/tmp')
os.environ.setdefault('USER', 'dockeruser')

# =====================================================
# 🔧 تكوين البيئة والإعدادات
# =====================================================
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# إعدادات الذاكرة والكاش
CACHE_DIR = "/tmp/huggingface_cache"
os.makedirs(CACHE_DIR, exist_ok=True)

# تكوين متغيرات البيئة لـ Hugging Face
os.environ.update({
    "HF_HOME": CACHE_DIR,
    "TRANSFORMERS_CACHE": CACHE_DIR,
    "HF_DATASETS_CACHE": CACHE_DIR,
    "HUGGINGFACE_HUB_CACHE": CACHE_DIR,
    "TORCH_HOME": CACHE_DIR,
    "TOKENIZERS_PARALLELISM": "false",  # منع مشاكل threading
    "TRANSFORMERS_OFFLINE": "0",  # السماح بالتحميل من الإنترنت
})

# إعدادات PyTorch للذاكرة
if torch.cuda.is_available():
    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'
    torch.backends.cudnn.benchmark = True

# =====================================================
# 🚀 تحديد الجهاز (GPU أو CPU)
# =====================================================
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f"🖥️ Using device: {device}")
if torch.cuda.is_available():
    logger.info(f"🎮 CUDA Device: {torch.cuda.get_device_name(0)}")
    logger.info(f"💾 CUDA Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")

# =====================================================
# 📊 خريطة الموديلات
# =====================================================
label_mapping = {
    0: '13B', 1: '30B', 2: '65B', 3: '7B', 4: 'GLM130B', 5: 'bloom_7b',
    6: 'bloomz', 7: 'cohere', 8: 'davinci', 9: 'dolly', 10: 'dolly-v2-12b',
    11: 'flan_t5_base', 12: 'flan_t5_large', 13: 'flan_t5_small',
    14: 'flan_t5_xl', 15: 'flan_t5_xxl', 16: 'gemma-7b-it', 17: 'gemma2-9b-it',
    18: 'gpt-3.5-turbo', 19: 'gpt-35', 20: 'gpt4', 21: 'gpt4o',
    22: 'gpt_j', 23: 'gpt_neox', 24: 'human', 25: 'llama3-70b', 26: 'llama3-8b',
    27: 'mixtral-8x7b', 28: 'opt_1.3b', 29: 'opt_125m', 30: 'opt_13b',
    31: 'opt_2.7b', 32: 'opt_30b', 33: 'opt_350m', 34: 'opt_6.7b',
    35: 'opt_iml_30b', 36: 'opt_iml_max_1.3b', 37: 't0_11b', 38: 't0_3b',
    39: 'text-davinci-002', 40: 'text-davinci-003'
}

# =====================================================
# 🤖 Model Manager - إدارة الموديلات
# =====================================================
class ModelManager:
    def __init__(self):
        self.tokenizer = None
        self.models = []
        self.models_loaded = False
        self.model_urls = [
            "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed12",
            "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed22"
        ]
        self.base_model_id = "answerdotai/ModernBERT-base"  # Primary
        self.fallback_model_id = "bert-base-uncased"  # Fallback if ModernBERT fails
        self.using_fallback = False
        
    def load_tokenizer(self):
        """تحميل الـ Tokenizer مع fallback"""
        try:
            logger.info(f"📝 Loading tokenizer from {self.base_model_id}...")
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.base_model_id,
                cache_dir=CACHE_DIR,
                use_fast=True,
                trust_remote_code=False
            )
            logger.info("✅ Primary tokenizer loaded successfully")
            
        except Exception as e:
            logger.warning(f"⚠️ Failed to load primary tokenizer: {e}")
            try:
                logger.info(f"🔄 Falling back to {self.fallback_model_id}...")
                self.tokenizer = AutoTokenizer.from_pretrained(
                    self.fallback_model_id,
                    cache_dir=CACHE_DIR,
                    use_fast=True,
                    trust_remote_code=False
                )
                self.using_fallback = True
                logger.info("✅ Fallback tokenizer loaded successfully")
            except Exception as fallback_e:
                logger.error(f"❌ Failed to load fallback tokenizer: {fallback_e}")
                return False
        
        # إعداد معالج النصوص
        try:
            newline_to_space = Replace(Regex(r'\s*\n\s*'), " ")
            join_hyphen_break = Replace(Regex(r'(\w+)[--]\s*\n\s*(\w+)'), r"\1\2")
            self.tokenizer.backend_tokenizer.normalizer = Sequence([
                self.tokenizer.backend_tokenizer.normalizer,
                join_hyphen_break,
                newline_to_space,
                Strip()
            ])
        except Exception as e:
            logger.warning(f"⚠️ Could not set custom normalizer: {e}")
        
        return True
    
    def load_single_model(self, model_url=None, model_path=None, model_name="Model"):
        """تحميل موديل واحد مع fallback ومعالجة شاملة للأخطاء"""
        base_model = None
        try:
            logger.info(f"🤖 Loading base {model_name} from {self.base_model_id}...")
            
            # محاولة تحميل الموديل الأساسي الرئيسي
            base_model = AutoModelForSequenceClassification.from_pretrained(
                self.base_model_id,
                num_labels=41,
                cache_dir=CACHE_DIR,
                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
                low_cpu_mem_usage=True,
                trust_remote_code=False
            )
            logger.info("✅ Primary base model loaded")
            
        except Exception as e:
            logger.warning(f"⚠️ Failed to load primary base model: {e}")
            try:
                logger.info(f"🔄 Falling back to {self.fallback_model_id}...")
                base_model = AutoModelForSequenceClassification.from_pretrained(
                    self.fallback_model_id,
                    num_labels=41,
                    cache_dir=CACHE_DIR,
                    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
                    low_cpu_mem_usage=True,
                    trust_remote_code=False
                )
                self.using_fallback = True
                logger.info("✅ Fallback base model loaded (note: weights may not be compatible)")
            except Exception as fallback_e:
                logger.error(f"❌ Failed to load fallback base model: {fallback_e}")
                return None
        
        # محاولة تحميل الأوزان (فقط إذا لم نستخدم fallback، أو إذا كانت متوافقة)
        try:
            if model_path and os.path.exists(model_path):
                logger.info(f"📁 Loading from local file: {model_path}")
                state_dict = torch.load(model_path, map_location=device, weights_only=True)
                base_model.load_state_dict(state_dict, strict=False)
            elif model_url:
                # استخدام hf_hub_download بدلاً من torch.hub للـ HF repos
                logger.info(f"🌐 Downloading weights from HF repo...")
                repo_id = "mihalykiss/modernbert_2"
                filename = model_url.split("/")[-1]
                local_path = hf_hub_download(
                    repo_id=repo_id,
                    filename=filename,
                    cache_dir=CACHE_DIR
                )
                logger.info(f"✅ Downloaded to {local_path}")
                state_dict = torch.load(local_path, map_location=device, weights_only=True)
                base_model.load_state_dict(state_dict, strict=False)
            
            logger.info(f"✅ {model_name} weights loaded successfully")
        except Exception as e:
            logger.warning(f"⚠️ Could not load custom weights for {model_name}: {e}")
            logger.info("📌 Using base model without fine-tuned weights")
        
        # نقل للجهاز وضبط الوضع
        try:
            base_model = base_model.to(device)
            base_model.eval()
            logger.info(f"✅ {model_name} moved to {device} and set to eval mode")
            return base_model
        except Exception as e:
            logger.error(f"❌ Failed to prepare {model_name}: {e}")
            return None
    
    def load_models(self):
        """تحميل جميع الموديلات"""
        if self.models_loaded:
            return True
        
        try:
            # تحميل tokenizer
            if not self.load_tokenizer():
                return False
            
            # تحميل كل موديل
            for i, model_url in enumerate(self.model_urls):
                model = self.load_single_model(
                    model_url=model_url,
                    model_name=f"Model {i+1}"
                )
                if model is None:
                    logger.warning(f"⚠️ Failed to load model {i+1}")
                    continue
                self.models.append(model)
            
            if len(self.models) == 0:
                logger.error("❌ No models loaded successfully")
                return False
            
            self.models_loaded = True
            logger.info(f"✅ Successfully loaded {len(self.models)} model(s)")
            return True
            
        except Exception as e:
            logger.error(f"❌ Model loading error: {e}", exc_info=True)
            return False
    
    def classify_text(self, text: str, max_length: int = 512) -> Dict:
        """تصنيف النص"""
        if not self.models_loaded or not self.tokenizer:
            raise RuntimeError("Models or tokenizer not loaded")
        
        try:
            # Tokenization
            inputs = self.tokenizer(
                text,
                return_tensors="pt",
                truncation=True,
                max_length=max_length,
                padding=True
            ).to(device)
            
            # التنبؤ باستخدام جميع الموديلات
            all_logits = []
            with torch.no_grad():
                for model in self.models:
                    outputs = model(**inputs)
                    all_logits.append(outputs.logits)
            
            # حساب المتوسط
            avg_logits = torch.mean(torch.stack(all_logits), dim=0)
            probabilities = torch.nn.functional.softmax(avg_logits, dim=-1)
            
            # الحصول على أعلى التنبؤات
            top_probs, top_indices = torch.topk(probabilities[0], k=5)
            
            # حساب احتمالات AI vs Human
            ai_prob = 1.0 - probabilities[0][24].item()  # 24 = human
            human_prob = probabilities[0][24].item()
            
            # الموديل المتوقع
            predicted_idx = top_indices[0].item()
            predicted_model = label_mapping.get(predicted_idx, "unknown")
            
            # Top 5 predictions
            top_5 = [
                {
                    "model": label_mapping.get(idx.item(), "unknown"),
                    "probability": prob.item()
                }
                for prob, idx in zip(top_probs, top_indices)
            ]
            
            return {
                "ai_percentage": round(ai_prob * 100, 2),
                "human_percentage": round(human_prob * 100, 2),
                "predicted_model": predicted_model,
                "top_5_predictions": top_5,
                "models_used": len(self.models),
                "using_fallback": self.using_fallback
            }
            
        except Exception as e:
            logger.error(f"Classification error: {e}", exc_info=True)
            raise

# =====================================================
# 🆕 NEW HELPER FUNCTIONS - Content Cleaning & Splitting
# =====================================================
def clean_content_for_analysis(text: str, min_line_length: int = 30) -> str:
    """
    Clean content by removing short lines (headlines, etc.)
    
    Args:
        text: Original text
        min_line_length: Minimum character length for a line to be kept (default: 30)
    
    Returns:
        Cleaned text with only substantial content lines
    """
    lines = text.split('\n')
    cleaned_lines = []
    
    for line in lines:
        stripped = line.strip()
        # Keep lines that are longer than min_line_length
        if len(stripped) >= min_line_length:
            cleaned_lines.append(stripped)
    
    return ' '.join(cleaned_lines)


def split_content_in_half(text: str) -> tuple:
    """
    Split cleaned content into two halves
    
    Args:
        text: Cleaned text
    
    Returns:
        Tuple of (first_half, second_half)
    """
    words = text.split()
    mid_point = len(words) // 2
    
    first_half = ' '.join(words[:mid_point])
    second_half = ' '.join(words[mid_point:])
    
    return first_half, second_half


def analyze_content_halves(model_manager, text: str, overall_result: Dict = None) -> Dict:
    """
    Analyze text by splitting it into two halves after cleaning
    Uses BOTH models for ensemble predictions on each half for improved accuracy
    
    Args:
        model_manager: The ModelManager instance
        text: Original text to analyze
        overall_result: Overall classification result for variance calculation
    
    Returns:
        Dictionary with analysis of both halves and final decision
    """
    try:
        # Clean the content first
        cleaned_text = clean_content_for_analysis(text)
        
        if not cleaned_text or len(cleaned_text.split()) < 10:
            return {
                "halves_analysis_available": False,
                "reason": "Content too short after cleaning"
            }
        
        # Split into halves
        first_half, second_half = split_content_in_half(cleaned_text)
        
        # Analyze first half using BOTH models (ensemble prediction)
        logger.info("🔍 Analyzing first half with both models...")
        first_half_result = model_manager.classify_text(first_half)
        first_half_words = len(first_half.split())
        
        # Analyze second half using BOTH models (ensemble prediction)
        logger.info("🔍 Analyzing second half with both models...")
        second_half_result = model_manager.classify_text(second_half)
        second_half_words = len(second_half.split())
        
        # Extract key metrics
        first_ai = first_half_result["ai_percentage"]
        second_ai = second_half_result["ai_percentage"]
        first_model = first_half_result["predicted_model"]
        second_model = second_half_result["predicted_model"]
        
        # Get top predictions from both halves for comparison
        first_top5 = first_half_result.get("top_5_predictions", [])
        second_top5 = second_half_result.get("top_5_predictions", [])
        
        # Calculate average AI score from both halves
        avg_halves_ai_score = (first_ai + second_ai) / 2
        
        # Calculate variance between halves
        variance_between_halves = abs(first_ai - second_ai)
        
        # Overall AI probability (use overall_result if provided, otherwise calculate)
        overall_ai_prob = overall_result["ai_percentage"] / 100 if overall_result else avg_halves_ai_score / 100
        
        # Check model consistency across halves
        models_agree = first_model == second_model
        
        # Calculate confidence boost from using both models
        models_used = first_half_result.get("models_used", 1)
        ensemble_confidence_boost = "High" if models_used > 1 else "Low"
        
        logger.info(f"✅ First half: {first_ai}% AI ({first_model}) | Second half: {second_ai}% AI ({second_model})")
        logger.info(f"📊 Models used per half: {models_used} | Agreement: {models_agree}")
        
        # ===== FINAL DECISION LOGIC =====
        verdict = None
        confidence = None
        reasoning = None
        
        # Condition 1: Both halves < 50% AI AND second_half predicted_model is "human"
        if first_ai < 50 and second_ai < 50 and second_model.lower() == "human":
            verdict = "HUMAN"
            confidence = "High" if variance_between_halves < 15 else "Medium"
            reasoning = (
                f"Both halves scored below 50% AI probability (First: {first_ai}%, Second: {second_ai}%) "
                f"using ensemble prediction from {models_used} model(s). "
                f"The second half was classified as human-written. "
                f"Variance between halves is {variance_between_halves:.2f}%, indicating "
                f"{'consistent human patterns' if variance_between_halves < 15 else 'some variation but still human-like'}. "
                f"Model predictions {'agree' if models_agree else 'differ'} across halves."
            )
        
        # Condition 2: Both halves > 50% AI AND second_half predicted_model is NOT "human"
        elif first_ai > 50 and second_ai > 50 and second_model.lower() != "human":
            verdict = "AI"
            
            # Determine confidence based on scores and model agreement
            if first_ai > 80 and second_ai > 80:
                confidence = "Very High"
            elif first_ai > 70 and second_ai > 70:
                confidence = "High"
            else:
                confidence = "Medium"
            
            # Boost confidence if models agree
            if models_agree and confidence != "Very High":
                confidence_levels = ["Low", "Medium", "High", "Very High"]
                current_idx = confidence_levels.index(confidence)
                if current_idx < len(confidence_levels) - 1:
                    confidence = f"{confidence} (boosted by model agreement)"
            
            reasoning = (
                f"Both halves scored above 50% AI probability (First: {first_ai}%, Second: {second_ai}%) "
                f"using ensemble prediction from {models_used} model(s). "
                f"The pattern matches {second_model} outputs. "
                f"First half suggests {first_model} while second half suggests {second_model}. "
                f"Variance between halves is {variance_between_halves:.2f}%, "
                f"{'showing consistent AI patterns throughout' if variance_between_halves < 20 else 'with some variation in AI generation style'}. "
                f"{'Both halves agree on the AI model type, strengthening confidence' if models_agree else 'Different AI models detected in each half'}."
            )
        
        # Condition 3: Mixed results - one half AI, one half human
        elif (first_ai > 50 and second_ai < 50) or (first_ai < 50 and second_ai > 50):
            verdict = "MIXED"
            confidence = "Low"
            reasoning = (
                f"Mixed signals detected using {models_used} model(s) for ensemble prediction. "
                f"First half: {first_ai}% AI ({first_model}), Second half: {second_ai}% AI ({second_model}). "
                f"One portion appears AI-generated while the other seems human-written. "
                f"This could indicate: partial AI assistance, human editing of AI content, "
                f"or AI completion of human-started text. High variance of {variance_between_halves:.2f}% supports mixed authorship."
            )
        
        # Condition 4: Both around 50% - uncertain
        else:
            # Check if second_model is human but scores are borderline
            if second_model.lower() == "human":
                verdict = "LIKELY_HUMAN"
                confidence = "Low"
                reasoning = (
                    f"Borderline case with scores near 50% threshold (First: {first_ai}%, Second: {second_ai}%) "
                    f"analyzed using {models_used} model(s). "
                    f"Second half classified as human-written. The text shows characteristics of both "
                    f"human and AI writing. Variance: {variance_between_halves:.2f}%."
                )
            else:
                verdict = "LIKELY_AI"
                confidence = "Low"
                reasoning = (
                    f"Borderline case with scores near 50% threshold (First: {first_ai}%, Second: {second_ai}%) "
                    f"analyzed using {models_used} model(s). "
                    f"Pattern suggests {second_model} but confidence is low. "
                    f"Variance: {variance_between_halves:.2f}%."
                )
        
        # Prepare final decision structure with enhanced model information
        final_decision = {
            "verdict": verdict,
            "confidence": confidence,
            "reasoning": reasoning,
            "supporting_data": {
                "overall_ai_prob": round(overall_ai_prob, 3),
                "first_half_ai_score": round(first_ai / 100, 3),
                "second_half_ai_score": round(second_ai / 100, 3),
                "avg_halves_ai_score": round(avg_halves_ai_score / 100, 3),
                "variance_between_halves": round(variance_between_halves, 2),
                "first_half_model": first_model,
                "second_half_model": second_model,
                "models_agree": models_agree,
                "ensemble_models_used": models_used,
                "ensemble_confidence": ensemble_confidence_boost
            }
        }
        
        return {
            "halves_analysis_available": True,
            "cleaned_content": {
                "total_words": len(cleaned_text.split()),
                "first_half_words": first_half_words,
                "second_half_words": second_half_words
            },
            "first_half": {
                "ai_percentage": first_ai,
                "human_percentage": first_half_result["human_percentage"],
                "predicted_model": first_model,
                "word_count": first_half_words,
                "preview": first_half[:200] + "..." if len(first_half) > 200 else first_half,
                "top_5_predictions": first_top5,
                "models_used": models_used
            },
            "second_half": {
                "ai_percentage": second_ai,
                "human_percentage": second_half_result["human_percentage"],
                "predicted_model": second_model,
                "word_count": second_half_words,
                "preview": second_half[:200] + "..." if len(second_half) > 200 else second_half,
                "top_5_predictions": second_top5,
                "models_used": models_used
            },
            "final_decision": final_decision
        }
        
    except Exception as e:
        logger.error(f"Error in halves analysis: {e}", exc_info=True)
        return {
            "halves_analysis_available": False,
            "error": str(e)
        }

# =====================================================
# 📝 Pydantic Models
# =====================================================
class TextInput(BaseModel):
    text: str
    analyze_paragraphs: bool = False

class SimpleTextInput(BaseModel):
    text: str

class DetectionResult(BaseModel):
    success: bool
    code: int
    message: str
    data: Dict

# =====================================================
# 🔧 مساعدات
# =====================================================
def split_into_paragraphs(text: str, min_length: int = 100) -> List[str]:
    """تقسيم النص إلى فقرات"""
    paragraphs = re.split(r'\n\s*\n', text)
    return [p.strip() for p in paragraphs if len(p.strip()) >= min_length]

# =====================================================
# 🌐 FastAPI Application
# =====================================================
app = FastAPI(
    title="ModernBERT AI Text Detector API",
    description="API for detecting AI-generated text using ModernBERT",
    version="2.0.0"
)

# CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Model Manager Instance
model_manager = ModelManager()

# =====================================================
# 🚀 Startup Event
# =====================================================
@app.on_event("startup")
async def startup_event():
    """تحميل الموديلات عند بدء التطبيق"""
    logger.info("🚀 Starting application...")
    logger.info("📦 Loading models...")
    success = model_manager.load_models()
    if success:
        logger.info("✅ Application ready! (Fallback mode: %s)", model_manager.using_fallback)
    else:
        logger.error("⚠️ Failed to load models - API will return errors")
        logger.info("💡 Tip: Ensure 'transformers>=4.45.0' and 'huggingface_hub' are installed. Run: pip install --upgrade transformers huggingface_hub")

@app.get("/")
async def root():
    """الصفحة الرئيسية"""
    return {
        "message": "ModernBERT AI Text Detector API",
        "status": "online" if model_manager.models_loaded else "initializing",
        "models_loaded": len(model_manager.models),
        "using_fallback": model_manager.using_fallback,
        "device": str(device),
        "endpoints": {
            "analyze": "/analyze",
            "simple": "/analyze-simple",
            "health": "/health",
            "docs": "/docs"
        }
    }

@app.get("/health")
async def health_check():
    """فحص صحة الخدمة"""
    memory_info = {}
    if torch.cuda.is_available():
        memory_info = {
            "gpu_allocated_gb": round(torch.cuda.memory_allocated() / 1024**3, 2),
            "gpu_reserved_gb": round(torch.cuda.memory_reserved() / 1024**3, 2)
        }
    
    return {
        "status": "healthy" if model_manager.models_loaded else "unhealthy",
        "models_loaded": len(model_manager.models),
        "using_fallback": model_manager.using_fallback,
        "device": str(device),
        "cuda_available": torch.cuda.is_available(),
        "memory_info": memory_info
    }

@app.post("/analyze", response_model=DetectionResult)
async def analyze_text(data: TextInput):
    """
    تحليل النص للكشف عن AI
    يحاكي نفس وظيفة Gradio classify_text
    """
    try:
        # التحقق من النص
        text = data.text.strip()
        if not text:
            return DetectionResult(
                success=False,
                code=400,
                message="Empty input text",
                data={}
            )
        
        # التأكد من تحميل الموديلات
        if not model_manager.models_loaded:
            # محاولة تحميل الموديلات
            if not model_manager.load_models():
                return DetectionResult(
                    success=False,
                    code=503,
                    message="Models not available. Check logs for details.",
                    data={}
                )
        
        # حساب عدد الكلمات
        total_words = len(text.split())
        
        # التحليل الأساسي
        result = model_manager.classify_text(text)
        
        # النتائج الأساسية
        ai_percentage = result["ai_percentage"]
        human_percentage = result["human_percentage"]
        ai_words = int(total_words * (ai_percentage / 100))
        
        # تحليل الفقرات إذا طُلب ذلك
        paragraphs_analysis = []
        if data.analyze_paragraphs and ai_percentage > 50:
            paragraphs = split_into_paragraphs(text)
            recalc_ai_words = 0
            recalc_total_words = 0
            
            for para in paragraphs[:10]:  # حد أقصى 10 فقرات
                if para.strip():
                    try:
                        para_result = model_manager.classify_text(para)
                        para_words = len(para.split())
                        recalc_total_words += para_words
                        recalc_ai_words += para_words * (para_result["ai_percentage"] / 100)
                        
                        paragraphs_analysis.append({
                            "paragraph": para[:200] + "..." if len(para) > 200 else para,
                            "ai_generated_score": para_result["ai_percentage"] / 100,
                            "human_written_score": para_result["human_percentage"] / 100,
                            "predicted_model": para_result["predicted_model"]
                        })
                    except Exception as e:
                        logger.warning(f"Failed to analyze paragraph: {e}")
            
            # إعادة حساب النسب بناءً على الفقرات
            if recalc_total_words > 0:
                ai_percentage = round((recalc_ai_words / recalc_total_words) * 100, 2)
                human_percentage = round(100 - ai_percentage, 2)
                ai_words = int(recalc_ai_words)
        
        # 🆕 NEW FEATURE: Analyze content by halves (pass overall result for variance calculation)
        halves_analysis = analyze_content_halves(model_manager, text, result)
        
        # إنشاء رسالة التغذية الراجعة
        if ai_percentage > 50:
            feedback = "Most of Your Text is AI/GPT Generated"
        else:
            feedback = "Most of Your Text Appears Human-Written"
        
        # إرجاع النتائج بنفس تنسيق الكود الأصلي + إضافة تحليل النصفين
        return DetectionResult(
            success=True,
            code=200,
            message="analysis completed",
            data={
                "fakePercentage": ai_percentage,
                "isHuman": human_percentage,
                "textWords": total_words,
                "aiWords": ai_words,
                "paragraphs": paragraphs_analysis,
                "predicted_model": result["predicted_model"],
                "feedback": feedback,
                "input_text": text[:500] + "..." if len(text) > 500 else text,
                "detected_language": "en",
                "top_5_predictions": result.get("top_5_predictions", []),
                "models_used": result.get("models_used", 1),
                "using_fallback": result.get("using_fallback", False),
                # 🆕 NEW: Halves analysis appended to response
                "halves_analysis": halves_analysis
            }
        )
        
    except Exception as e:
        logger.error(f"Analysis error: {e}", exc_info=True)
        return DetectionResult(
            success=False,
            code=500,
            message=f"Analysis failed: {str(e)}",
            data={}
        )

@app.post("/analyze-simple")
async def analyze_simple(data: SimpleTextInput):
    """
    تحليل مبسط - يرجع النتائج الأساسية فقط
    """
    try:
        text = data.text.strip()
        if not text:
            raise HTTPException(status_code=400, detail="Empty text")
        
        if not model_manager.models_loaded:
            if not model_manager.load_models():
                raise HTTPException(status_code=503, detail="Models not available")
        
        result = model_manager.classify_text(text)
        
        return {
            "is_ai": result["ai_percentage"] > 50,
            "ai_score": result["ai_percentage"],
            "human_score": result["human_percentage"],
            "detected_model": result["predicted_model"] if result["ai_percentage"] > 50 else None,
            "confidence": max(result["ai_percentage"], result["human_percentage"]),
            "using_fallback": result.get("using_fallback", False)
        }
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Simple analysis error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

# =====================================================
# 🏃 تشغيل التطبيق
# =====================================================
if __name__ == "__main__":
    import uvicorn
    
    # الحصول على الإعدادات من البيئة
    port = int(os.environ.get("PORT", 8000))
    host = os.environ.get("HOST", "0.0.0.0")
    workers = int(os.environ.get("WORKERS", 1))
    
    logger.info("=" * 50)
    logger.info(f"🌐 Starting server on {host}:{port}")
    logger.info(f"👷 Workers: {workers}")
    logger.info(f"📚 Documentation: http://{host}:{port}/docs")
    logger.info("=" * 50)
    
    uvicorn.run(
        "main:app",  # Assuming this file is named main.py
        host=host,
        port=port,
        workers=workers,
        reload=False  # Set to True for dev
    )