"""
NETRA - Video Surveillance and Analysis Web Application
Main Flask Application
"""

from flask import Flask, render_template, request, jsonify, session, redirect, url_for, Response, send_from_directory
from flask_sqlalchemy import SQLAlchemy
from werkzeug.security import generate_password_hash, check_password_hash
from werkzeug.utils import secure_filename
from werkzeug.middleware.proxy_fix import ProxyFix
import cv2
import numpy as np
import os
import sys
from datetime import datetime, date
import base64
from pathlib import Path
import threading
import queue
import mimetypes
import json
import uuid
import shutil
import tempfile

# ===================== Dynamic Path Detection (Fully Portable) =====================
# Determine project root regardless of where app.py is located or device
# This makes the project fully portable across different machines and paths

def detect_project_root():
    """
    Dynamically detect PROJECT_ROOT by looking for config/ and src/ directories
    Works on any device, any installation path
    """
    current = Path(__file__).parent.parent
    if (current / 'config').exists() and (current / 'src').exists():
        return current
    
    # Fallback: search upwards
    current = Path(__file__).parent
    while current != current.parent:
        if (current / 'config').exists() and (current / 'src').exists():
            return current
        current = current.parent
    
    # Last resort: current working directory
    return Path.cwd()

PROJECT_ROOT = detect_project_root()

# Ensure PROJECT_ROOT is in Python path for imports
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

# Determine webapp folder dynamically
WEBAPP_FOLDER = PROJECT_ROOT / 'webapp'

print(f"\n{'='*60}")
print(f"🌍 PROJECT CONFIGURATION (Portable Setup)")
print(f"{'='*60}")
print(f"📁 PROJECT_ROOT: {PROJECT_ROOT}")
print(f"📁 WEBAPP_FOLDER: {WEBAPP_FOLDER}")
print(f"{'='*60}\n")

# Import detection modules from new src structure
from src import (
    ViolenceDetector,
    YOLODetector,
    WeaponPersonDetector,
    PoseDetection,
    AnomalyDetector,
    VideoCapture,
)

# Import centralized configuration
from config import (
    MODEL_PATHS,
    get_model_path,
    DETECTION_THRESHOLDS,
    PROCESSING_PARAMS,
    SECRET_KEY,
    DATABASE_URI,
    MAX_CONTENT_LENGTH,
    UPLOAD_FOLDER,
    PROCESSED_FOLDER,
)

# Import model downloader for Hugging Face Hub models
from src.utils.model_downloader import setup_all_models

# ===================== Setup Models =====================
# Download models from Hugging Face Hub on startup
setup_all_models()

# ===================== Codec Selection =====================
def get_video_codec():
    """
    Select the best video codec for cross-platform compatibility.
    XVID is widely supported on Windows, Mac, and Linux.
    """
    try:
        # Try XVID first (best Windows compatibility)
        codec = cv2.VideoWriter_fourcc(*'XVID')
        # Test if codec is available
        test_path = str(Path(tempfile.gettempdir()) / 'codec_test.avi')
        test_writer = cv2.VideoWriter(test_path, codec, 1.0, (640, 480))
        if test_writer.isOpened():
            test_writer.release()
            if os.path.exists(test_path):
                os.remove(test_path)
            return codec, '.avi'
    except Exception:
        pass
    
    try:
        # Fallback to MJPG (Motion JPEG) - universally supported but larger files
        codec = cv2.VideoWriter_fourcc(*'MJPG')
        test_path = str(Path(tempfile.gettempdir()) / 'codec_test.avi')
        test_writer = cv2.VideoWriter(test_path, codec, 1.0, (640, 480))
        if test_writer.isOpened():
            test_writer.release()
            if os.path.exists(test_path):
                os.remove(test_path)
            return codec, '.avi'
    except Exception:
        pass
    
    # Final fallback - return XVID (should work on most systems)
    return cv2.VideoWriter_fourcc(*'XVID'), '.avi'

app = Flask(__name__, template_folder=str(WEBAPP_FOLDER / 'templates'), 
            static_folder=str(WEBAPP_FOLDER / 'static'))
app.config['SECRET_KEY'] = SECRET_KEY
app.config['SQLALCHEMY_DATABASE_URI'] = DATABASE_URI
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.config['UPLOAD_FOLDER'] = str(UPLOAD_FOLDER)
app.config['PROCESSED_FOLDER'] = str(PROCESSED_FOLDER)
app.config['MAX_CONTENT_LENGTH'] = MAX_CONTENT_LENGTH

# ===================== Secure Session Configuration for Hugging Face =====================
# These settings ensure cookies work correctly on Hugging Face Spaces (HTTPS environment)
app.config['SESSION_COOKIE_SECURE'] = True  # Only send cookies over HTTPS
app.config['SESSION_COOKIE_HTTPONLY'] = True  # Prevent JavaScript from accessing cookies
app.config['SESSION_COOKIE_SAMESITE'] = 'Lax'  # Allow cross-site form submissions
app.config['PERMANENT_SESSION_LIFETIME'] = 86400  # Session valid for 24 hours
app.config['SESSION_REFRESH_EACH_REQUEST'] = True  # Refresh session timeout on each request

# ===================== Proxy Middleware for Hugging Face =====================
# Apply ProxyFix to handle X-Forwarded-* headers from reverse proxy (HF Spaces)
# This ensures Flask recognizes HTTPS connections from the proxy
app.wsgi_app = ProxyFix(app.wsgi_app, x_for=1, x_proto=1, x_host=1, x_port=1, x_prefix=1)

# Folders are created by config/settings.py
# Ensure they exist
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
os.makedirs(app.config['PROCESSED_FOLDER'], exist_ok=True)

db = SQLAlchemy(app)

# ===================== Database Models =====================
class User(db.Model):
    """User model for authentication"""
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False)
    email = db.Column(db.String(120), unique=True, nullable=False)
    password_hash = db.Column(db.String(200), nullable=False)
    created_at = db.Column(db.DateTime, default=datetime.utcnow)
    
    def set_password(self, password):
        self.password_hash = generate_password_hash(password)
    
    def check_password(self, password):
        return check_password_hash(self.password_hash, password)


class AnalysisHistory(db.Model):
    """Store analysis history"""
    id = db.Column(db.Integer, primary_key=True)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
    analysis_type = db.Column(db.String(50), nullable=False)  # 'video' or 'live'
    filename = db.Column(db.String(200))
    detections = db.Column(db.Text)
    alert_count = db.Column(db.Integer, default=0)
    detection_count = db.Column(db.Integer, default=0)
    duration = db.Column(db.Integer, default=0)  # Duration in seconds for live monitoring
    models_used = db.Column(db.Text)  # JSON string of selected models
    created_at = db.Column(db.DateTime, default=datetime.utcnow)
    ended_at = db.Column(db.DateTime)
    # New fields for video analysis
    original_filename = db.Column(db.String(200))  # Original uploaded filename
    processed_video_url = db.Column(db.String(500))  # URL to processed video
    preview_image_url = db.Column(db.String(500))  # URL to preview image
    emergency_frames = db.Column(db.Text)  # JSON array of emergency frame filenames
    total_frames = db.Column(db.Integer, default=0)  # Total frames in video
    processed_frames = db.Column(db.Integer, default=0)  # Frames that were analyzed
    frame_summaries = db.Column(db.Text)  # JSON with frame-by-frame details


class PersonDetection(db.Model):
    """Store person detection records for live camera monitoring"""
    id = db.Column(db.Integer, primary_key=True)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
    person_count = db.Column(db.Integer, default=0)  # Number of people detected
    is_present = db.Column(db.Boolean, default=False)  # Whether person is currently present
    confidence = db.Column(db.Float, default=0.0)  # Detection confidence score
    detection_details = db.Column(db.Text)  # JSON string with additional details
    detected_at = db.Column(db.DateTime, default=datetime.utcnow)
    session_date = db.Column(db.Date, default=datetime.utcnow)  # For grouping by date
    
    def __repr__(self):
        return f'<PersonDetection {self.id}: {self.person_count} people on {self.detected_at}>'


class DetectionHistory(db.Model):
    """Store detection screenshots for weapons, unusual activity, and risks"""
    id = db.Column(db.Integer, primary_key=True)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
    detection_type = db.Column(db.String(50), nullable=False)  # 'weapon', 'unusual', 'risk'
    alert_level = db.Column(db.String(20), default='MEDIUM')  # LOW, MEDIUM, HIGH, CRITICAL
    confidence = db.Column(db.Float, default=0.0)  # Detection confidence
    image_filename = db.Column(db.String(200), nullable=False)  # Image filename in uploads/
    detection_details = db.Column(db.Text)  # JSON string with detection info (class, action, etc)
    detected_at = db.Column(db.DateTime, default=datetime.utcnow)
    session_date = db.Column(db.Date, default=datetime.utcnow)  # For grouping by date
    
    def __repr__(self):
        return f'<DetectionHistory {self.id}: {self.detection_type} on {self.detected_at}>'


class LiveSession(db.Model):
    """Store live monitoring session recordings and metadata"""
    id = db.Column(db.Integer, primary_key=True)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
    session_name = db.Column(db.String(200))  # User-friendly name
    video_filename = db.Column(db.String(200), nullable=False)  # MP4 filename in uploads/sessions/
    preview_image = db.Column(db.String(200))  # First frame preview
    detection_count = db.Column(db.Integer, default=0)  # Total detections
    alert_count = db.Column(db.Integer, default=0)  # Total alerts
    threat_count = db.Column(db.Integer, default=0)  # Critical threats (weapons, violence)
    duration = db.Column(db.Integer, default=0)  # Recording duration in seconds
    total_frames = db.Column(db.Integer, default=0)  # Total frames recorded
    models_used = db.Column(db.Text)  # JSON string of models used
    detections_summary = db.Column(db.Text)  # JSON with detection breakdown
    alerts_summary = db.Column(db.Text)  # JSON with alerts list
    emergency_frames = db.Column(db.Text)  # JSON array of emergency frame filenames
    created_at = db.Column(db.DateTime, default=datetime.utcnow)
    ended_at = db.Column(db.DateTime)
    is_critical = db.Column(db.Boolean, default=False)  # Flag for critical incidents
    notes = db.Column(db.Text)  # User notes
    
    def __repr__(self):
        return f'<LiveSession {self.id}: {self.session_name} on {self.created_at}>'


# ===================== Model Manager =====================
class ModelManager:
    """Manages all AI models for detection"""
    
    def __init__(self):
        self.models = {}
        self.load_models()
    
    def load_models(self):
        """Load all available models from ai_models/ directory"""
        ai_models_dir = PROJECT_ROOT / 'ai_models'
        
        print(f"\n📁 Looking for models in: {ai_models_dir}")
        print(f"📁 Directory exists: {ai_models_dir.exists()}")
        
        # List what's in the directory for debugging
        if ai_models_dir.exists():
            print(f"📂 Contents of ai_models:")
            for item in ai_models_dir.rglob("*"):
                if item.is_file():
                    print(f"   - {item.relative_to(ai_models_dir)}")
        
        def find_model_file(patterns):
            """Find model file matching any of the patterns"""
            for pattern in patterns:
                # Try direct path
                path = ai_models_dir / pattern
                if path.exists():
                    return path
                
                # Try with ai_models/ prefix (nested structure)
                path = ai_models_dir / 'ai_models' / pattern
                if path.exists():
                    return path
            
            return None
        
        try:
            # Load Violence Detection Model
            violence_path = find_model_file(['activity_recognition/violence_model.h5'])
            if violence_path:
                self.models['violence'] = ViolenceDetector()
                print(f"✓ Violence Detection loaded from: {violence_path.relative_to(PROJECT_ROOT)}")
            else:
                print(f"⚠ Violence model not found")
        except Exception as e:
            print(f"✗ Error loading violence model: {e}")
        
        try:
            # Load YOLO Object Detection Model
            yolo_path = find_model_file(['object_detection/yolov8n.pt'])
            if yolo_path:
                self.models['yolo'] = YOLODetector(model_path=str(yolo_path))
                print(f"✓ YOLO Object Detection loaded from: {yolo_path.relative_to(PROJECT_ROOT)}")
            else:
                print(f"⚠ YOLO model not found")
        except Exception as e:
            print(f"✗ Error loading YOLO model: {e}")
        
        try:
            # Load Gun/Weapon Detection Model
            gun_path = find_model_file(['weapon_detection/best.pt'])
            person_path = find_model_file(['object_detection/yolov8n.pt'])

            if gun_path and person_path:
                self.models['weapon'] = WeaponPersonDetector(
                    gun_model_path=str(gun_path),
                    person_model_path=str(person_path)
                )
                print(f"✓ Weapon Detection loaded")
            else:
                print(f"⚠ Weapon detection models not found")
        except Exception as e:
            print(f"✗ Error loading weapon model: {e}")

        try:
            # Load Pose Detection Model
            pose_path = find_model_file(['pose_detection/yolo11n-pose.pt'])
            if pose_path:
                self.models['pose'] = PoseDetection(model_path=str(pose_path))
                print(f"✓ Pose Detection loaded from: {pose_path.relative_to(PROJECT_ROOT)}")
            else:
                print(f"⚠ Pose model not found")
        except Exception as e:
            print(f"✗ Error loading pose model: {e}")
        
        try:
            # Load Anomaly Detection Model
            anomaly_path = find_model_file(['weapon_detection/best.bin'])
            if anomaly_path:
                try:
                    self.models['anomaly'] = AnomalyDetector(model_path=str(anomaly_path))
                    print(f"✓ Anomaly Detection loaded")
                except Exception as init_error:
                    print(f"⚠ Failed to init anomaly detector: {init_error}")
            else:
                print(f"⚠ Anomaly model not found")
        except Exception as e:
            print(f"✗ Error with anomaly model: {e}")
        
        try:
            # Load Analysis Model (Fight/Behavior Detection) - try multiple variants
            analysis_path = find_model_file([
                'analysis_models/fight_detection_model.h5',
                'analysis_models/CustomCNN.h5',
                'analysis_models/binarycnn200.h5'
            ])
            if analysis_path:
                try:
                    from tensorflow import keras
                    self.models['analysis'] = keras.models.load_model(str(analysis_path))
                    print(f"✓ Fight/Behavior Detection loaded from: {analysis_path.name}")
                except Exception as init_error:
                    print(f"⚠ Failed to load analysis model: {init_error}")
            else:
                print(f"⚠ Analysis models not found")
        except Exception as e:
            print(f"✗ Error loading analysis model: {e}")
        
        try:
            # Load LSTM Model (Behavior/Activity Sequence Analysis)
            lstm_path = find_model_file([
                'LSTM Model/MobBiLSTM_model_saved101.keras',
                'LSTM_Model/MobBiLSTM_model_saved101.keras'
            ])
            if lstm_path:
                try:
                    from tensorflow import keras
                    self.models['lstm'] = keras.models.load_model(str(lstm_path), compile=False)
                    print(f"✓ LSTM Sequence Analysis loaded")
                except Exception as lstm_error:
                    print(f"⚠ Failed to load LSTM model: {lstm_error}")
            else:
                print(f"⚠ LSTM model not found")
        except Exception as e:
            print(f"✗ Error with LSTM model: {e}")
        
        print(f"\n✓ Total models loaded: {len(self.models)}\n")
    
    def add_model(self, model_name, model_instance):
        """Add a new model dynamically"""
        self.models[model_name] = model_instance
    
    def get_model(self, model_name):
        """Get a specific model"""
        return self.models.get(model_name)
    
    def list_models(self):
        """List all available models"""
        return list(self.models.keys())
    
    def get_model_details(self):
        """Get details about all available models (loaded and available)"""
        model_info = {
            'violence': {'name': 'Violence Detection', 'description': 'Detects violent activities in video'},
            'yolo': {'name': 'Object Detection', 'description': 'Detects common objects (people, cars, etc)'},
            'weapon': {'name': 'Weapon Detection', 'description': 'Detects guns, knives and other weapons'},
            'pose': {'name': 'Pose Detection', 'description': 'Detects human poses and body movements'},
            'anomaly': {'name': 'Anomaly Detection (best.bin)', 'description': 'Detects unusual/anomalous behavior patterns'},
            'analysis': {'name': 'Advanced Analysis (Fight/Behavior)', 'description': 'Advanced behavior and fight detection analysis'},
            'lstm': {'name': 'LSTM Sequence Analysis', 'description': 'Temporal behavior analysis using bidirectional LSTM'},
        }
        
        model_details = {}
        # Return all models that are actually loaded
        for model_name in self.models.keys():
            model_details[model_name] = {
                'name': model_info.get(model_name, {}).get('name', model_name),
                'description': model_info.get(model_name, {}).get('description', ''),
                'enabled': True
            }
        
        return model_details


# Initialize model manager
model_manager = ModelManager()

# ===================== Video Processing =====================
class VideoProcessor:
    """Processes video frames and applies AI models"""
    
    def __init__(self):
        self.active_stream = None
        self.processing = False
        self.frame_index = 0
        self.alert_hit_threshold = 3
        self.unsafe_hit_streak = 0
        self.unsafe_alert_latched = False
        self.selected_models = None  # None means use all models
        # Temporal tracking for statistics (cumulative in video)
        self.person_detection_frames = 0
        self.weapon_detection_frames = 0
        self.person_consecutive_frames = 0
        self.weapon_consecutive_frames = 0
        self.weapon_alert_triggered = False  # Track if weapon alert already triggered
        # Session tracking
        self.session_detections = []
        self.session_alerts = []
        # LSTM frame buffer for temporal analysis (30 frames = ~1.5 seconds @ 20fps)
        self.lstm_frame_buffer = []
        self.lstm_buffer_size = 16
        self.lstm_prediction_cache = None
        self.last_pose_summary = None

    def set_selected_models(self, model_list):
        """Set which models to use for processing (None means all models)"""
        self.selected_models = model_list if model_list else None
    
    def is_model_selected(self, model_name):
        """Check if a model should be used"""
        if self.selected_models is None:
            return True
        return model_name in self.selected_models

    def reset_session_state(self):
        """Reset temporal detector state before a new video/live analysis session."""
        self.frame_index = 0
        self.unsafe_hit_streak = 0
        self.unsafe_alert_latched = False
        pose_model = model_manager.get_model('pose')
        if pose_model:
            try:
                pose_model.reset_movement_state()
            except Exception as e:
                pass
        
        anomaly_model = model_manager.get_model('anomaly')
        if anomaly_model:
            try:
                anomaly_model.reset()
            except Exception as e:
                pass
        
        # Reset temporal tracking
        self.person_detection_frames = 0
        self.weapon_detection_frames = 0
        self.person_consecutive_frames = 0
        self.weapon_consecutive_frames = 0
        # Reset pose tracking
        self.last_pose_summary = None
        self.weapon_alert_triggered = False
        # Reset session tracking
        self.session_detections = []
        self.session_alerts = []
        # Reset LSTM buffer
        self.lstm_frame_buffer = []
        self.lstm_prediction_cache = None

    def _update_alert_streak(self, condition_active, current_streak, is_latched):
        """Trigger one alert only after N consecutive hits, then wait for reset."""
        if condition_active:
            current_streak += 1
        else:
            return 0, False, False

        if current_streak >= self.alert_hit_threshold and not is_latched:
            return current_streak, True, True

        return current_streak, is_latched, False

    def _short_text(self, text, max_chars=28):
        """Trim long text so it fits compact overlay cells."""
        if not text:
            return "None"
        if len(text) <= max_chars:
            return text
        return text[:max_chars - 3] + "..."

    def _build_overlay_summary(self, results):
        """Build top panel summary from frame detections and alerts."""
        object_classes = []
        weapon_classes = []

        for det in results['detections']:
            det_type = det.get('type')
            det_class = det.get('class', 'unknown')
            if det_type == 'object':
                object_classes.append(det_class)
            elif det_type == 'weapon':
                weapon_classes.append(det_class)

        unique_objects = sorted(set(object_classes))
        unique_weapons = sorted(set(weapon_classes))

        object_text = ', '.join(unique_objects[:3]) if unique_objects else 'None'
        weapon_text = ', '.join(unique_weapons[:2]) if unique_weapons else 'None'

        behavior_text = 'Normal Activity'
        pose_summary = results.get('pose', {})
        pose_risk = pose_summary.get('risk_level')
        pose_action = pose_summary.get('action')
        if pose_risk and pose_risk != 'SAFE':
            behavior_text = f"{pose_risk.replace('_', ' ')}: {pose_action}"
        elif any(alert.get('type') == 'violence' for alert in results['alerts']):
            behavior_text = 'Violence Detected'

        if results['alerts']:
            highest_severity = results['alerts'][0].get('severity', 'ALERT')
            alert_text = f"{len(results['alerts'])} Active ({highest_severity})"
        else:
            alert_text = 'No Active Alerts'

        return {
            'Detected Objects': self._short_text(object_text),
            'Weapon Status': self._short_text(weapon_text),
            'Alert System': self._short_text(alert_text),
            'Behavior Analysis': self._short_text(behavior_text)
        }

    def _draw_top_info_panel(self, frame, summary):
        """Draw a compact 2x2 information panel at the top-center of the frame."""
        frame_h, frame_w = frame.shape[:2]

        panel_w = min(max(int(frame_w * 0.62), 360), max(frame_w - 20, 200))
        panel_h = min(max(int(frame_h * 0.20), 110), 170)

        x1 = (frame_w - panel_w) // 2
        y1 = 10
        x2 = x1 + panel_w
        y2 = y1 + panel_h

        overlay = frame.copy()
        cv2.rectangle(overlay, (x1, y1), (x2, y2), (18, 18, 18), -1)
        cv2.addWeighted(overlay, 0.65, frame, 0.35, 0, frame)

        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 200, 255), 1)

        cell_w = panel_w // 2
        cell_h = panel_h // 2

        cv2.line(frame, (x1 + cell_w, y1), (x1 + cell_w, y2), (70, 70, 70), 1)
        cv2.line(frame, (x1, y1 + cell_h), (x2, y1 + cell_h), (70, 70, 70), 1)

        cells = [
            ('Detected Objects', summary['Detected Objects'], (80, 220, 80)),
            ('Weapon Status', summary['Weapon Status'], (70, 200, 255)),
            ('Alert System', summary['Alert System'], (0, 120, 255)),
            ('Behavior Analysis', summary['Behavior Analysis'], (160, 180, 255))
        ]

        for idx, (title, value, color) in enumerate(cells):
            col = idx % 2
            row = idx // 2
            tx = x1 + col * cell_w + 10
            ty = y1 + row * cell_h + 20

            cv2.putText(frame, title, (tx, ty), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 1)
            cv2.putText(frame, value, (tx, ty + 22), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (240, 240, 240), 1)

    def _draw_person_bounding_boxes(self, frame, detections):
        """Draw bounding boxes for detected persons with labels"""
        person_detections = [det for det in detections if det.get('class') == 'person']
        
        for det in person_detections:
            bbox = det.get('bbox', [])
            confidence = det.get('confidence', 0.0)
            
            if len(bbox) >= 4:
                x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
                
                # Ensure coordinates are within frame bounds
                h, w = frame.shape[:2]
                x1, y1 = max(0, x1), max(0, y1)
                x2, y2 = min(w, x2), min(h, y2)
                
                # Draw bounding box (green for person)
                color = (0, 255, 0)  # Green in BGR
                thickness = 2
                cv2.rectangle(frame, (x1, y1), (x2, y2), color, thickness)
                
                # Draw label with confidence
                label = f"Person {confidence:.2f}"
                label_size, baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
                
                # Draw label background
                label_top = max(y1 - label_size[1] - 5, 0)
                cv2.rectangle(frame, (x1, label_top), (x1 + label_size[0], label_top + label_size[1] + 5), color, -1)
                
                # Draw label text
                cv2.putText(frame, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)

    def _severity_rank(self, severity):
        ranks = {'LOW': 1, 'MEDIUM': 2, 'HIGH': 3, 'CRITICAL': 4}
        return ranks.get((severity or '').upper(), 0)

    def _save_detection_screenshot(self, frame, detection_type, alert_level, confidence, details=None):
        """Save a screenshot of the detection region to database"""
        try:
            from flask import session

            user_id = session.get('user_id')
            if not user_id:
                return None
            
            # Generate unique filename
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
            unique_id = str(uuid.uuid4())[:8]
            filename = f"detection_{detection_type}_{timestamp}_{unique_id}.jpg"
            
            # Create uploads directory if it doesn't exist
            upload_dir = UPLOAD_FOLDER / 'detections'
            upload_dir.mkdir(parents=True, exist_ok=True)
            
            # Save the frame
            filepath = upload_dir / filename
            cv2.imwrite(str(filepath), frame)
            
            # Save to database
            detection_record = DetectionHistory(
                user_id=user_id,
                detection_type=detection_type,
                alert_level=alert_level,
                confidence=confidence,
                image_filename=filename,
                detection_details=json.dumps(details) if details else None,
                detected_at=datetime.now(),
                session_date=date.today()
            )
            db.session.add(detection_record)
            db.session.commit()
            
            return detection_record.id
        except Exception as e:
            pass
            return None

    def _overlay_detections(self, frame, detections):
        """Draw compact detection boxes for key weapon/object detections."""
        colors = {
            'weapon': (0, 0, 255),
            'object': (0, 200, 0),
        }
        for det in detections:
            bbox = det.get('bbox')
            if not bbox or len(bbox) != 4:
                continue

            x1, y1, x2, y2 = [int(v) for v in bbox]
            det_type = det.get('type', 'object')
            label = f"{det.get('class', det_type)} {det.get('confidence', 0):.2f}"
            color = colors.get(det_type, (255, 180, 0))

            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2 if det_type == 'weapon' else 1)
            (text_w, text_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.45, 1)
            text_y1 = max(0, y1 - text_h - 8)
            cv2.rectangle(frame, (x1, text_y1), (x1 + text_w + 8, y1), color, -1)
            cv2.putText(frame, label, (x1 + 4, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 1)

    def _annotate_pose_banner(self, frame, pose_summary):
        """Draw pose risk banner below the top info panel when pose is available."""
        if not pose_summary:
            return

        risk_level = pose_summary.get('risk_level', 'SAFE')
        action = pose_summary.get('action', 'other')
        score = pose_summary.get('risk_score', 0.0)

        if risk_level == 'HIGH_RISK':
            color = (0, 0, 220)
        elif risk_level == 'LOW_RISK':
            color = (0, 140, 255)
        else:
            color = (0, 128, 0)

        text = f"Pose Risk: {risk_level} | Action: {action} | Score: {score:.2f}"
        top = 188
        bottom = min(frame.shape[0] - 1, top + 34)
        cv2.rectangle(frame, (10, top), (frame.shape[1] - 10, bottom), color, -1)
        cv2.putText(frame, text, (20, top + 23), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

    def _build_frame_summary(self, frame_number, results):
        """Create a UI-friendly frame-wise summary."""
        detections = results['detections']
        alerts = results['alerts']
        pose_summary = results.get('pose')

        highest_alert = max(alerts, key=lambda alert: self._severity_rank(alert.get('severity')), default=None)
        weapon_names = sorted({det['class'] for det in detections if det.get('type') == 'weapon'})
        object_names = sorted({det['class'] for det in detections if det.get('type') == 'object'})

        return {
            'frame_number': frame_number,
            'weapon_count': len([det for det in detections if det.get('type') == 'weapon']),
            'object_count': len([det for det in detections if det.get('type') == 'object']),
            'weapon_classes': weapon_names,
            'object_classes': object_names[:6],
            'pose_risk': pose_summary.get('risk_level', 'UNKNOWN') if pose_summary else 'UNAVAILABLE',
            'pose_action': pose_summary.get('action', 'unknown') if pose_summary else 'unknown',
            'pose_score': float(pose_summary.get('risk_score', 0.0)) if pose_summary else 0.0,
            'alert_state': highest_alert.get('severity', 'SAFE') if highest_alert else 'SAFE',
            'alert_message': highest_alert.get('message', 'No active alert') if highest_alert else 'No active alert',
            'detections': detections,
            'alerts': alerts,
        }

    def process_frame(self, frame):
        """Process a single frame with selected models"""
        self.frame_index += 1
        results = {
            'detections': [],
            'alerts': [],
            'annotated_frame': frame.copy(),
            'pose': None,
        }
        frame_result = None
        
        # Weapon Detection (Gun/Knife + Person counting)
        if self.is_model_selected('weapon') and 'weapon' in model_manager.models:
            try:
                weapon_model = model_manager.get_model('weapon')
                frame_result = weapon_model.process_frame(frame)
                
                # Track weapon detections without drawing frame bounding boxes
                for weapon in frame_result.weapons:
                    results['detections'].append({
                        'type': 'weapon',
                        'class': weapon.class_name,
                        'confidence': float(weapon.confidence),
                        'bbox': list(weapon.bbox)
                    })
                
            except Exception as e:
                pass

        # Pose Detection - Always run (needed for behavior analysis panel display)
        if 'pose' in model_manager.models:
            try:
                pose_model = model_manager.get_model('pose')
                pose_result = pose_model.predict(frame)[0]
                movement = pose_model.assess_movement(pose_result)
                results['pose'] = {
                    'risk_level': movement.get('risk_level', 'SAFE'),
                    'action': movement.get('action', 'other'),
                    'risk_score': float(movement.get('risk_score', 0.0)),
                    'details': movement.get('details', {}),
                }
            except Exception as e:
                pass
        
        # YOLO Object Detection (conf threshold matching main_cctv.py)
        if self.is_model_selected('yolo') and 'yolo' in model_manager.models:
            try:
                yolo_model = model_manager.get_model('yolo')
                detections = yolo_model.detect(frame, conf_threshold=DETECTION_THRESHOLDS['yolo'])
                
                for det in detections:
                    results['detections'].append({
                        'type': 'object',
                        'class': det.class_name,
                        'confidence': float(det.confidence),
                        'bbox': list(det.bbox)
                    })
            except Exception as e:
                pass
        
        # Violence Detection (conf threshold matching main_cctv.py)
        if self.is_model_selected('violence') and 'violence' in model_manager.models:
            try:
                violence_model = model_manager.get_model('violence')
                violence_result = violence_model.detect_violence(frame, confidence_threshold=DETECTION_THRESHOLDS['violence'])
                
                if violence_result.is_violence:
                    results['alerts'].append({
                        'type': 'violence',
                        'severity': violence_result.alert_level,
                        'confidence': float(violence_result.confidence),
                        'message': f'Violence detected: {violence_result.class_name}'
                    })
                    # Save screenshot of violence detection
                    self._save_detection_screenshot(
                        frame,
                        'violence',
                        violence_result.alert_level,
                        float(violence_result.confidence),
                        {'violence_class': violence_result.class_name}
                    )
            except Exception as e:
                pass
        
        # Anomaly Detection
        if self.is_model_selected('anomaly') and 'anomaly' in model_manager.models:
            try:
                anomaly_model = model_manager.get_model('anomaly')
                anomaly_result = anomaly_model.predict_frame(frame)
                
                if anomaly_result is not None:
                    results['detections'].append({
                        'type': 'anomaly',
                        'is_anomaly': anomaly_result.is_anomaly,
                        'confidence': float(anomaly_result.confidence),
                        'anomaly_score': float(anomaly_result.anomaly_score),
                        'alert_level': anomaly_result.alert_level,
                    })
                    
                    if anomaly_result.is_anomaly:
                        results['alerts'].append({
                            'type': 'anomaly_detected',
                            'severity': anomaly_result.alert_level,
                            'confidence': float(anomaly_result.confidence),
                            'message': anomaly_result.description,
                            'anomaly_score': float(anomaly_result.anomaly_score),
                        })
                        # Save screenshot of anomaly detection
                        self._save_detection_screenshot(
                            frame,
                            'anomaly',
                            anomaly_result.alert_level,
                            float(anomaly_result.confidence),
                            {'anomaly_score': float(anomaly_result.anomaly_score), 'description': anomaly_result.description}
                        )
            except Exception as e:
                pass

        has_weapon = any(det.get('type') == 'weapon' for det in results['detections'])
        has_person = any(det.get('class') == 'person' for det in results['detections'])
        
        # Track person and weapon detection duration (for statistics)
        if has_person:
            self.person_consecutive_frames += 1
        else:
            self.person_consecutive_frames = 0
        
        if has_weapon:
            self.weapon_consecutive_frames += 1
        else:
            self.weapon_consecutive_frames = 0
        
        # Only increment detection counters after 3+ consecutive frames (temporal threshold)
        if self.person_consecutive_frames >= 3 and self.person_consecutive_frames == 3:
            self.person_detection_frames += 1
        
        if self.weapon_consecutive_frames >= 3 and self.weapon_consecutive_frames == 3:
            self.weapon_detection_frames += 1
        
        # Add detection duration info to results
        results['person_detected'] = has_person
        results['weapon_detected'] = has_weapon
        results['person_consecutive_frames'] = self.person_consecutive_frames
        results['weapon_consecutive_frames'] = self.weapon_consecutive_frames
        
        pose_summary = results.get('pose')
        unsafe_pose = pose_summary and pose_summary.get('risk_level') in {'LOW_RISK', 'HIGH_RISK'}

        # WEAPON ALERT: Trigger alert if 3+ cumulative weapon detections in entire video
        if self.weapon_detection_frames >= self.alert_hit_threshold and not self.weapon_alert_triggered:
            self.weapon_alert_triggered = True
            results['alerts'].append({
                'type': 'weapon_detected',
                'severity': 'HIGH',
                'message': f'Weapon detected {self.weapon_detection_frames} times in video',
                'person_count': frame_result.person_count if frame_result else 0,
                'total_weapon_detections': self.weapon_detection_frames,
            })
            # Save screenshot of weapon detection
            weapon_det = next((d for d in results['detections'] if d.get('type') == 'weapon'), None)
            if weapon_det:
                self._save_detection_screenshot(
                    frame,
                    'weapon',
                    'HIGH',
                    weapon_det.get('confidence', 0.0),
                    {'weapon_class': weapon_det.get('class', 'unknown')}
                )

        if has_weapon and unsafe_pose:
            self.unsafe_hit_streak, self.unsafe_alert_latched, unsafe_should_alert = self._update_alert_streak(
                True,
                self.unsafe_hit_streak,
                self.unsafe_alert_latched,
            )
            if unsafe_should_alert:
                severity = 'CRITICAL' if pose_summary.get('risk_level') == 'HIGH_RISK' else 'HIGH'
                results['alerts'].append({
                    'type': 'weapon_unsafe_pose',
                    'severity': severity,
                    'message': (
                        f"Weapon and unsafe pose confirmed after {self.alert_hit_threshold} hits: "
                        f"{pose_summary.get('action', 'unknown')} ({pose_summary.get('risk_level')})"
                    ),
                    'pose_action': pose_summary.get('action', 'unknown'),
                    'pose_risk': pose_summary.get('risk_level', 'SAFE'),
                    'pose_score': float(pose_summary.get('risk_score', 0.0)),
                    'hit_count': self.unsafe_hit_streak,
                })
                # Save screenshot of risky pose + weapon
                self._save_detection_screenshot(
                    frame,
                    'risk',
                    severity,
                    pose_summary.get('risk_score', 0.0),
                    {
                        'action': pose_summary.get('action', 'unknown'),
                        'risk_level': pose_summary.get('risk_level', 'SAFE'),
                        'has_weapon': True
                    }
                )
        else:
            self.unsafe_hit_streak = 0
            self.unsafe_alert_latched = False

        # Show all frame insights in one compact top panel (2x2 small boxes)
        self._overlay_detections(results['annotated_frame'], results['detections'])
        self._draw_person_bounding_boxes(results['annotated_frame'], results['detections'])
        summary = self._build_overlay_summary(results)
        self._draw_top_info_panel(results['annotated_frame'], summary)
        # Store pose info for display in behavior analysis panel instead of on frame
        self.last_pose_summary = results.get('pose')
        
        # Process LSTM for temporal behavior analysis
        if self.is_model_selected('lstm') and 'lstm' in model_manager.models:
            try:
                lstm_result = self._process_lstm_frame(results['annotated_frame'])
                if lstm_result:
                    results['lstm_prediction'] = lstm_result
                    # Add LSTM annotation to frame
                    self._annotate_lstm_result(results['annotated_frame'], lstm_result)
            except Exception as e:
                pass
        
        results['frame_summary'] = self._build_frame_summary(self.frame_index, results)
        
        return results
    
    def _process_lstm_frame(self, frame):
        """Process frame through LSTM model for temporal behavior analysis."""
        try:
            # Resize frame to model's expected size: 64x64
            h, w = frame.shape[:2]
            target_size = (64, 64)
            resized = cv2.resize(frame, target_size)
            
            # Convert BGR to RGB if needed
            if len(resized.shape) == 3 and resized.shape[2] == 3:
                rgb_frame = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
            else:
                rgb_frame = resized
            
            # Normalize to [0, 1]
            normalized = rgb_frame.astype(np.float32) / 255.0
            
            # Add frame to buffer (don't expand dims yet, just add the frame)
            self.lstm_frame_buffer.append(normalized)
            
            # Keep buffer to exactly 16 frames for model input
            if len(self.lstm_frame_buffer) > 16:
                self.lstm_frame_buffer.pop(0)
            
            # Only predict when we have full sequence (16 frames)
            if len(self.lstm_frame_buffer) == 16:
                lstm_model = model_manager.get_model('lstm')
                if lstm_model:
                    try:
                        # Stack frames into sequence: (16, 64, 64, 3)
                        sequence = np.array(self.lstm_frame_buffer)  # Shape: (16, 64, 64, 3)
                        
                        # Add batch dimension: (1, 16, 64, 64, 3)
                        batch_sequence = np.expand_dims(sequence, axis=0)
                        
                        # Predict
                        predictions = lstm_model.predict(batch_sequence, verbose=0)
                        
                        # Extract prediction
                        if isinstance(predictions, np.ndarray):
                            pred_value = float(np.max(predictions))
                            pred_class = int(np.argmax(predictions))
                            
                            # Determine behavior label
                            behavior_labels = ['normal', 'anomalous', 'violent', 'suspicious']
                            behavior = behavior_labels[min(pred_class, len(behavior_labels)-1)]
                            
                            # Cache the prediction
                            self.lstm_prediction_cache = {
                                'behavior': behavior,
                                'confidence': min(pred_value, 1.0),
                                'class': pred_class,
                                'buffer_size': len(self.lstm_frame_buffer)
                            }
                            
                            return self.lstm_prediction_cache
                    except Exception as lstm_pred_error:
                        pass
        except Exception as e:
            pass
        
        return None if not self.lstm_prediction_cache else self.lstm_prediction_cache
    
    def _annotate_lstm_result(self, frame, lstm_result):
        """Draw LSTM prediction on frame."""
        if not lstm_result:
            return
        
        behavior = lstm_result.get('behavior', 'unknown')
        confidence = lstm_result.get('confidence', 0.0)
        
        # Color coding: green for normal, orange for anomalous, red for violent
        color_map = {
            'normal': (0, 255, 0),
            'anomalous': (0, 165, 255),
            'violent': (0, 0, 255),
            'suspicious': (0, 165, 255),
        }
        
        color = color_map.get(behavior, (128, 128, 128))
        
        # Draw LSTM result at bottom of frame
        h, w = frame.shape[:2]
        text = f"LSTM: {behavior.upper()} ({confidence:.2f})"
        text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
        
        x = max(0, w - text_size[0] - 10)
        y = min(h, h - 10)
        
        cv2.rectangle(frame, (x-5, y-text_size[1]-5), (x+text_size[0]+5, y+5), color, -1)
        cv2.putText(frame, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)


video_processor = VideoProcessor()

# Background video analysis job tracking
analysis_jobs = {}  # job_id -> state dict (in-memory, transient)

# Camera stream generator
camera_lock = threading.Lock()
current_camera = None
selected_camera_index = 0  # Default to camera 0

# Video recording
recording_lock = threading.Lock()
is_recording = False
video_writer = None
recording_filename = None
recording_start_time = None
last_recorded_frame = None

def enumerate_cameras(max_cameras=10):
    """Detect all available cameras"""
    available_cameras = []
    
    for i in range(max_cameras):
        cap = cv2.VideoCapture(i)
        if cap.isOpened():
            # Get camera properties
            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            fps = cap.get(cv2.CAP_PROP_FPS)
            
            camera_info = {
                'index': i,
                'name': f"Camera {i}",
                'resolution': f"{width}x{height}",
                'fps': round(fps, 1),
                'available': True
            }
            available_cameras.append(camera_info)
            cap.release()
    
    return available_cameras

def generate_camera_frames():
    """Generate frames from camera using VideoCapture with motion detection (matches main_cctv.py)"""
    global current_camera, selected_camera_index

    with camera_lock:
        if current_camera is None:
            # Try to open the selected camera
            current_camera = VideoCapture(selected_camera_index, use_motion_detection=True)
            if not current_camera.start(verbose=False):
                # If selected camera fails, try to find any available camera
                available = enumerate_cameras()
                if available:
                    # Try the first available camera
                    for cam in available:
                        current_camera = VideoCapture(cam['index'], use_motion_detection=True)
                        if current_camera.start(verbose=False):
                            selected_camera_index = cam['index']
                            break
                        current_camera = None

                if current_camera is None:
                    return

            # Cap camera resolution to 640x480 for lighter streaming
            if current_camera.cap is not None:
                current_camera.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
                current_camera.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

            video_processor.reset_session_state()

    frame_counter = 0
    process_every_n = 3  # Run heavy AI models every 3rd frame for performance
    last_results = None

    try:
        for original, rois, fg_mask in current_camera.stream_rois():
            frame_counter += 1

            # Run AI every Nth frame; redraw cached overlay on the rest (fast)
            if frame_counter % process_every_n == 0 or last_results is None:
                results = video_processor.process_frame(original)
                last_results = results
                annotated_frame = results['annotated_frame']
            else:
                annotated_frame = original.copy()
                if last_results:
                    video_processor._overlay_detections(annotated_frame, last_results['detections'])
                    video_processor._draw_person_bounding_boxes(annotated_frame, last_results['detections'])
                    summary = video_processor._build_overlay_summary(last_results)
                    video_processor._draw_top_info_panel(annotated_frame, summary)

            # Handle video recording if enabled
            global is_recording, video_writer, recording_filename, last_recorded_frame

            with recording_lock:
                if is_recording and annotated_frame is not None:
                    # Initialize video writer on first frame
                    if video_writer is None:
                        video_dir = UPLOAD_FOLDER / 'videos'
                        video_dir.mkdir(parents=True, exist_ok=True)

                        height, width = annotated_frame.shape[:2]
                        fourcc, ext = get_video_codec()

                        # Create filename with timestamp
                        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
                        recording_filename = f"video_{timestamp}{ext}"
                        filepath = video_dir / recording_filename

                        video_writer = cv2.VideoWriter(
                            str(filepath),
                            fourcc,
                            15.0,  # 15 FPS matches our every-3rd-frame AI processing
                            (width, height)
                        )

                        global recording_start_time
                        recording_start_time = datetime.now()

                    # Write frame to video
                    if video_writer.isOpened():
                        video_writer.write(annotated_frame)
                        last_recorded_frame = annotated_frame.copy()

            # Encode at 75% JPEG quality — significantly reduces bandwidth/CPU
            ret, buffer = cv2.imencode('.jpg', annotated_frame, [cv2.IMWRITE_JPEG_QUALITY, 75])
            if not ret:
                continue
            frame_bytes = buffer.tobytes()

            yield (b'--frame\r\n'
                   b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
    except GeneratorExit:
        pass


# ===================== Routes =====================

@app.route('/')
def home():
    """Public homepage with project information"""
    return render_template('home.html')


@app.route('/login')
def login_page():
    """Login/Register page"""
    if 'user_id' in session:
        return redirect(url_for('dashboard'))
    return render_template('login.html')


@app.route('/index')
def index():
    """Redirect old index route to login"""
    return redirect(url_for('login_page'))


@app.route('/register', methods=['POST'])
def register():
    """Register new user"""
    try:
        data = request.json
        username = data.get('username')
        email = data.get('email')
        password = data.get('password')
        
        # Validate input
        if not username or not email or not password:
            return jsonify({'success': False, 'message': 'All fields are required'}), 400
        
        # Check if user exists
        if User.query.filter_by(username=username).first():
            return jsonify({'success': False, 'message': 'Username already exists'}), 400
        
        if User.query.filter_by(email=email).first():
            return jsonify({'success': False, 'message': 'Email already registered'}), 400
        
        # Create new user
        user = User(username=username, email=email)
        user.set_password(password)
        db.session.add(user)
        db.session.commit()
        
        return jsonify({'success': True, 'message': 'Registration successful! Please login.'})
    
    except Exception as e:
        return jsonify({'success': False, 'message': str(e)}), 500


@app.route('/login', methods=['POST'])
def login():
    """Login user"""
    try:
        data = request.json
        username = data.get('username')
        password = data.get('password')
        
        user = User.query.filter_by(username=username).first()
        
        if user and user.check_password(password):
            session.permanent = True  # Make session persistent across requests
            session['user_id'] = user.id
            session['username'] = user.username
            return jsonify({'success': True, 'message': 'Login successful!'})
        else:
            return jsonify({'success': False, 'message': 'Invalid username or password'}), 401
    
    except Exception as e:
        return jsonify({'success': False, 'message': str(e)}), 500


@app.route('/logout')
def logout():
    """Logout user"""
    session.clear()
    return redirect(url_for('home'))


@app.route('/dashboard')
def dashboard():
    """Main dashboard after login"""
    if 'user_id' not in session:
        return redirect(url_for('login_page'))
    
    return render_template('dashboard.html', username=session.get('username'))


@app.route('/live-camera')
def live_camera():
    """Live camera analysis page"""
    if 'user_id' not in session:
        return redirect(url_for('login_page'))
    
    return render_template('live_camera.html')


@app.route('/video-analysis')
def video_analysis():
    """Video upload and analysis page"""
    if 'user_id' not in session:
        return redirect(url_for('login_page'))
    
    return render_template('video_analysis.html')


@app.route('/camera_feed')
def camera_feed():
    """Video streaming route"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    return Response(generate_camera_frames(),
                   mimetype='multipart/x-mixed-replace; boundary=frame')


@app.route('/processed/<path:filename>')
def processed_file(filename):
    """Serve processed previews and videos."""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    return send_from_directory(app.config['PROCESSED_FOLDER'], filename)


@app.route('/upload_video', methods=['POST'])
def upload_video():
    """Upload video for analysis"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        if 'video' not in request.files:
            return jsonify({'success': False, 'message': 'No video file uploaded'}), 400
        
        file = request.files['video']
        if file.filename == '':
            return jsonify({'success': False, 'message': 'No file selected'}), 400
        
        # Save file
        filename = secure_filename(file.filename)
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        filename = f"{timestamp}_{filename}"
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(filepath)
        
        # Process video
        results = process_video_file(filepath)
        
        # Save to history with all details
        history = AnalysisHistory(
            user_id=session['user_id'],
            analysis_type='video',
            filename=filename,
            original_filename=file.filename,
            detections=json.dumps(results.get('detections', [])),
            alert_count=len(results.get('alerts', [])),
            detection_count=len(results.get('detections', [])),
            models_used=json.dumps(session.get('selected_models', [])),
            processed_video_url=results.get('output_url'),
            preview_image_url=results.get('preview_url'),
            emergency_frames=json.dumps(results.get('emergency_frames', [])),
            total_frames=results.get('total_frames', 0),
            processed_frames=results.get('processed_frames', 0),
            frame_summaries=json.dumps(results.get('frame_summaries', []))
        )
        db.session.add(history)
        db.session.commit()
        
        return jsonify({
            'success': True,
            'message': 'Video processed successfully',
            'analysis_id': history.id,
            'results': results
        })
    
    except Exception as e:
        return jsonify({'success': False, 'message': str(e)}), 500


def process_video_file(filepath):
    """Process an uploaded video file with selected models and capture emergency frames"""
    cap = cv2.VideoCapture(filepath)
    all_detections = []
    all_alerts = []
    frame_count = 0
    processed_frames = 0
    frame_summaries = []
    emergency_frames = []  # Track captured emergency frames
    
    # Set selected models if specified in session
    selected_models = session.get('selected_models', [])
    if selected_models:
        video_processor.set_selected_models(selected_models)

    input_path = Path(filepath)
    processed_name = f"{input_path.stem}_processed.mp4"
    preview_name = f"{input_path.stem}_preview.jpg"
    processed_path = Path(app.config['PROCESSED_FOLDER']) / processed_name
    preview_path = Path(app.config['PROCESSED_FOLDER']) / preview_name
    
    # Create emergency frames directory
    emergency_frames_dir = Path(app.config['PROCESSED_FOLDER']) / 'emergency_frames'
    emergency_frames_dir.mkdir(parents=True, exist_ok=True)

    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) or 640
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) or 480
    fps = cap.get(cv2.CAP_PROP_FPS) or 20.0
    fourcc, ext = get_video_codec()
    # Update processed filename to use correct extension
    processed_name = f"{input_path.stem}_processed{ext}"
    processed_path = Path(app.config['PROCESSED_FOLDER']) / processed_name
    writer = cv2.VideoWriter(
        str(processed_path),
        fourcc,
        fps,
        (width, height),
    )

    video_processor.reset_session_state()
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        # Process every Nth frame to speed up (from PROCESSING_PARAMS)
        if frame_count % PROCESSING_PARAMS['frame_skip'] == 0:
            results = video_processor.process_frame(frame)
            all_detections.extend(results['detections'])
            all_alerts.extend(results['alerts'])
            frame_summaries.append(results['frame_summary'])
            writer.write(results['annotated_frame'])
            processed_frames += 1
            if processed_frames == 1:
                cv2.imwrite(str(preview_path), results['annotated_frame'])
            
            # Capture emergency frames (HIGH or CRITICAL alerts, or weapons detected)
            has_critical_alert = any(alert.get('severity') in {'HIGH', 'CRITICAL'} for alert in results['alerts'])
            has_weapon = any(det.get('type') == 'weapon' for det in results['detections'])
            has_violence = any(alert.get('type') == 'violence' for alert in results['alerts'])
            has_anomaly_alert = any(alert.get('type') == 'anomaly_detected' for alert in results['alerts'])
            
            if has_critical_alert or has_weapon or has_violence or has_anomaly_alert:
                # Save emergency frame with timestamp
                emergency_filename = f"emergency_frame_{processed_frames}_t{frame_count}.jpg"
                emergency_path = emergency_frames_dir / emergency_filename
                cv2.imwrite(str(emergency_path), results['annotated_frame'])
                emergency_frames.append({
                    'filename': emergency_filename,
                    'frame_number': processed_frames,
                    'timestamp_frame': frame_count,
                    'alert_type': 'CRITICAL' if has_critical_alert else ('WEAPON' if has_weapon else ('VIOLENCE' if has_violence else 'ANOMALY')),
                    'has_weapon': has_weapon,
                    'has_violence': has_violence,
                    'has_anomaly': has_anomaly_alert
                })
        
        frame_count += 1
    
    cap.release()
    writer.release()

    risk_counts = {'SAFE': 0, 'LOW_RISK': 0, 'HIGH_RISK': 0, 'UNAVAILABLE': 0}
    for summary in frame_summaries:
        risk_counts[summary['pose_risk']] = risk_counts.get(summary['pose_risk'], 0) + 1

    output_url = url_for('processed_file', filename=processed_name) if processed_frames else None
    preview_url = url_for('processed_file', filename=preview_name) if processed_frames else None
    output_mime = mimetypes.guess_type(processed_name)[0] or 'video/mp4'
    
    return {
        'total_frames': frame_count,
        'processed_frames': processed_frames,
        'detections': all_detections,
        'alerts': all_alerts,
        'frame_summaries': frame_summaries,
        'output_url': output_url,
        'output_mime': output_mime,
        'preview_url': preview_url,
        'emergency_frames': emergency_frames,
        'summary': {
            'total_detections': len(all_detections),
            'total_alerts': len(all_alerts),
            'emergency_frames_count': len(emergency_frames),
            'weapon_alert_frames': sum(1 for frame in frame_summaries if frame['weapon_count'] > 0),
            'unsafe_pose_frames': sum(1 for frame in frame_summaries if frame['pose_risk'] in {'LOW_RISK', 'HIGH_RISK'}),
            'critical_frames': sum(1 for frame in frame_summaries if frame['alert_state'] == 'CRITICAL'),
            'pose_risk_counts': risk_counts,
        }
    }


@app.route('/api/models')
def list_models():
    """List all available models"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    models = model_manager.list_models()
    return jsonify({'models': models})


@app.route('/api/available-models')
def get_available_models():
    """Get detailed information about available models"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    models = model_manager.get_model_details()
    return jsonify({'models': models})


@app.route('/api/set-models', methods=['POST'])
def set_active_models():
    """Set which models to use for analysis"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        data = request.get_json()
        selected_models = data.get('models', [])
        
        # Store in session
        session['selected_models'] = selected_models
        session.modified = True
        
        # Also update the video processor
        video_processor.set_selected_models(selected_models)
        
        return jsonify({
            'success': True,
            'message': f'Models updated: {selected_models if selected_models else "All"}',
            'selected': selected_models
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/get-selected-models')
def get_selected_models():
    """Get currently selected models for the user"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    selected = session.get('selected_models', [])
    return jsonify({'selected_models': selected})


@app.route('/api/live-stats')
def get_live_stats():
    """Get live monitoring statistics"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    pose_data = {}
    if video_processor.last_pose_summary:
        pose_data = {
            'risk_level': video_processor.last_pose_summary.get('risk_level', 'SAFE'),
            'action': video_processor.last_pose_summary.get('action', 'unknown'),
            'risk_score': video_processor.last_pose_summary.get('risk_score', 0.0)
        }
    
    return jsonify({
        'detection_count': session.get('total_detections', 0),
        'alert_count': session.get('total_alerts', 0),
        'person_detections': video_processor.person_detection_frames,
        'weapon_detections': video_processor.weapon_detection_frames,
        'person_visible': video_processor.person_consecutive_frames >= 3,
        'weapon_visible': video_processor.weapon_consecutive_frames >= 3,
        'pose_analysis': pose_data
    })


@app.route('/api/start-camera', methods=['POST'])
def start_camera_session():
    """Start a camera monitoring session with selected models"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        data = request.get_json()
        selected_models = data.get('selectedModels', [])
        
        # Store session metadata
        session['monitoring_session_start'] = datetime.utcnow().isoformat()
        session['monitoring_models'] = selected_models
        session.modified = True
        
        # Reset statistics counters for new session
        video_processor.reset_session_state()
        
        return jsonify({
            'success': True,
            'message': 'Camera monitoring session started',
            'models': selected_models
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/cameras', methods=['GET'])
def get_available_cameras():
    """Get list of all available cameras"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        cameras = enumerate_cameras()
        return jsonify({
            'success': True,
            'cameras': cameras,
            'selected': selected_camera_index
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/select-camera', methods=['POST'])
def select_camera():
    """Select which camera to use"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    global current_camera, selected_camera_index
    
    try:
        data = request.get_json()
        camera_index = data.get('camera_index', 0)
        
        # Stop current camera
        with camera_lock:
            if current_camera is not None:
                current_camera.stop()
                current_camera = None
            
            selected_camera_index = camera_index
        
        return jsonify({
            'success': True,
            'message': f'Camera {camera_index} selected',
            'selected': camera_index
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


# ===================== Helper Functions =====================
def parse_iso_timestamp(timestamp_str):
    """Parse ISO format timestamp, handling 'Z' suffix for UTC"""
    if isinstance(timestamp_str, str):
        # Remove 'Z' suffix if present (indicates UTC)
        if timestamp_str.endswith('Z'):
            timestamp_str = timestamp_str[:-1]
        try:
            return datetime.fromisoformat(timestamp_str)
        except (ValueError, TypeError):
            return datetime.utcnow()
    elif isinstance(timestamp_str, datetime):
        return timestamp_str
    return datetime.utcnow()


@app.route('/api/save-monitoring-session', methods=['POST'])
def save_monitoring_session():
    """Save monitoring session results to database with optional video recording"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        data = request.get_json()
        
        # Check if recording filename is provided (from live camera session)
        recording_filename = data.get('recordingFilename')
        video_filename = None
        preview_filename = None
        
        if recording_filename:
            # Move the recorded video from uploads/videos/ to processed/sessions/
            try:
                sessions_dir = PROCESSED_FOLDER / 'sessions'
                sessions_dir.mkdir(parents=True, exist_ok=True)

                timestamp = datetime.utcnow().strftime('%Y%m%d_%H%M%S')
                user_folder = sessions_dir / f"user_{user_id}"
                user_folder.mkdir(parents=True, exist_ok=True)

                # Source video file
                source_path = UPLOAD_FOLDER / 'videos' / recording_filename
                if source_path.exists():
                    # Copy to session folder with new name
                    video_filename = f"session_{timestamp}_{uuid.uuid4().hex[:8]}.mp4"
                    dest_path = user_folder / video_filename
                    shutil.copy2(str(source_path), str(dest_path))

                    preview_filename = None
                    preview_path = None

                    # Extract preview frame from video
                    try:
                        cap = cv2.VideoCapture(str(dest_path))
                        ret, frame = cap.read()
                        if ret:
                            preview_filename = f"preview_{timestamp}_{uuid.uuid4().hex[:8]}.jpg"
                            preview_path = user_folder / preview_filename
                            cv2.imwrite(str(preview_path), frame)
                        cap.release()
                    except Exception:
                        pass

                    # Create LiveSession record (paths relative to PROCESSED_FOLDER)
                    live_session = LiveSession(
                        user_id=user_id,
                        session_name=data.get('sessionName', f"Live Session {timestamp}"),
                        video_filename=str(dest_path.relative_to(PROCESSED_FOLDER)),
                        preview_image=str(preview_path.relative_to(PROCESSED_FOLDER)) if preview_path else None,
                        detection_count=data.get('totalDetections', 0),
                        alert_count=data.get('totalAlerts', 0),
                        threat_count=data.get('threatCount', 0),
                        duration=int(data.get('duration', 0)),
                        total_frames=data.get('totalFrames', 0),
                        models_used=json.dumps(data.get('selectedModels', [])),
                        detections_summary=json.dumps(data.get('detectionsSummary', {})),
                        alerts_summary=json.dumps(data.get('alertsSummary', [])),
                        emergency_frames=json.dumps(data.get('emergencyFrames', [])),
                        created_at=parse_iso_timestamp(data.get('startTime')),
                        ended_at=parse_iso_timestamp(data.get('endTime')),
                        is_critical=data.get('isCritical', False)
                    )
                    db.session.add(live_session)
                    db.session.commit()

                    return jsonify({
                        'success': True,
                        'message': 'Live session saved successfully',
                        'session_id': live_session.id,
                        'video_url': url_for('processed_file', filename=str(dest_path.relative_to(PROCESSED_FOLDER)))
                    })
            except Exception:
                pass
                # Continue without video if save fails
        
        # Check if base64 video data is provided
        video_data = data.get('videoData')
        if video_data:
            # Create sessions directory if it doesn't exist
            sessions_dir = PROCESSED_FOLDER / 'sessions'
            sessions_dir.mkdir(parents=True, exist_ok=True)

            # Generate unique filename
            timestamp = datetime.utcnow().strftime('%Y%m%d_%H%M%S')
            user_folder = sessions_dir / f"user_{user_id}"
            user_folder.mkdir(parents=True, exist_ok=True)

            video_filename = f"session_{timestamp}_{uuid.uuid4().hex[:8]}.mp4"
            video_path = user_folder / video_filename

            # Decode base64 video data and save
            try:
                # Remove data URL prefix if present
                if video_data.startswith('data:'):
                    video_data = video_data.split(',', 1)[1]

                video_bytes = base64.b64decode(video_data)
                with open(video_path, 'wb') as f:
                    f.write(video_bytes)

                preview_filename = None
                preview_path = None

                # Extract preview frame from video
                try:
                    cap = cv2.VideoCapture(str(video_path))
                    ret, frame = cap.read()
                    if ret:
                        preview_filename = f"preview_{timestamp}_{uuid.uuid4().hex[:8]}.jpg"
                        preview_path = user_folder / preview_filename
                        cv2.imwrite(str(preview_path), frame)
                    cap.release()
                except Exception:
                    pass

                # Create LiveSession record (paths relative to PROCESSED_FOLDER)
                live_session = LiveSession(
                    user_id=user_id,
                    session_name=data.get('sessionName', f"Session {timestamp}"),
                    video_filename=str(video_path.relative_to(PROCESSED_FOLDER)),
                    preview_image=str(preview_path.relative_to(PROCESSED_FOLDER)) if preview_path else None,
                    detection_count=data.get('totalDetections', 0),
                    alert_count=data.get('totalAlerts', 0),
                    threat_count=data.get('threatCount', 0),
                    duration=int(data.get('duration', 0)),
                    total_frames=data.get('totalFrames', 0),
                    models_used=json.dumps(data.get('selectedModels', [])),
                    detections_summary=json.dumps(data.get('detectionsSummary', {})),
                    alerts_summary=json.dumps(data.get('alertsSummary', [])),
                    emergency_frames=json.dumps(data.get('emergencyFrames', [])),
                    created_at=parse_iso_timestamp(data.get('startTime')),
                    ended_at=parse_iso_timestamp(data.get('endTime')),
                    is_critical=data.get('isCritical', False)
                )
                db.session.add(live_session)
                db.session.commit()
                
                return jsonify({
                    'success': True,
                    'message': f'Live session saved successfully',
                    'session_id': live_session.id,
                    'video_url': f"/processed/sessions/user_{user_id}/{video_filename}"
                })
            except Exception as e:
                pass
                # Continue without video if save fails
        
        # Fallback: Create AnalysisHistory record (for backward compatibility)
        history = AnalysisHistory(
            user_id=user_id,
            analysis_type='live',
            detection_count=data.get('totalDetections', 0),
            alert_count=data.get('totalAlerts', 0),
            duration=data.get('duration', 0),
            models_used=str(data.get('selectedModels', [])),
            created_at=parse_iso_timestamp(data.get('startTime')),
            ended_at=parse_iso_timestamp(data.get('endTime')),
            detections=str(data.get('detections', []))
        )
        
        db.session.add(history)
        db.session.commit()
        
        # Clear session monitoring data
        session.pop('monitoring_session_start', None)
        session.pop('monitoring_models', None)
        session.modified = True
        
        return jsonify({
            'success': True,
            'message': f'Monitoring session saved. Detection Count: {data.get("totalDetections", 0)}, Alerts: {data.get("totalAlerts", 0)}',
            'session_id': history.id
        })
    except Exception as e:
        pass
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/save-person-detection', methods=['POST'])
def save_person_detection():
    """Save person detection data to database"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        data = request.get_json()
        user_id = session['user_id']
        
        # Create person detection record
        person_detection = PersonDetection(
            user_id=user_id,
            person_count=data.get('person_count', 0),
            is_present=data.get('is_present', False),
            confidence=data.get('confidence', 0.0),
            detection_details=data.get('detection_details'),
            detected_at=datetime.utcnow(),
            session_date=datetime.utcnow().date()
        )
        
        db.session.add(person_detection)
        db.session.commit()
        
        return jsonify({
            'success': True,
            'message': 'Person detection saved',
            'detection_id': person_detection.id
        })
    except Exception as e:
        db.session.rollback()
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/person-detection-history', methods=['GET'])
def get_person_detection_history():
    """Get person detection history for current user"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        
        # Get all person detection records for today
        today = datetime.utcnow().date()
        detections = PersonDetection.query.filter_by(
            user_id=user_id,
            session_date=today
        ).all()
        
        if not detections:
            return jsonify({
                'success': True,
                'data': {
                    'total_detections': 0,
                    'currently_present': False,
                    'detection_records': []
                }
            })
        
        # Calculate statistics
        total_detections = sum(d.person_count for d in detections)
        latest_detection = detections[-1]
        currently_present = latest_detection.is_present
        
        return jsonify({
            'success': True,
            'data': {
                'total_detections': total_detections,
                'currently_present': currently_present,
                'latest_confidence': latest_detection.confidence,
                'detection_count': len(detections),
                'detection_records': [
                    {
                        'id': d.id,
                        'person_count': d.person_count,
                        'is_present': d.is_present,
                        'confidence': d.confidence,
                        'detected_at': d.detected_at.isoformat()
                    }
                    for d in detections
                ]
            }
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/detection-history', methods=['GET'])
def get_detection_history():
    """Get threat detection history (weapons, unusual activity, risks) and live sessions"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        limit = request.args.get('limit', 20, type=int)
        detection_type = request.args.get('type', None)  # Filter by type: weapon, unusual, risk, session
        include_sessions = request.args.get('include_sessions', 'true').lower() == 'true'
        
        result_data = []
        
        # Get detection history
        if not detection_type or detection_type != 'session':
            query = DetectionHistory.query.filter_by(user_id=user_id)
            
            if detection_type:
                query = query.filter_by(detection_type=detection_type)
            
            detections = query.order_by(DetectionHistory.detected_at.desc()).limit(limit).all()
            
            for d in detections:
                result_data.append({
                    'id': d.id,
                    'type': 'detection',
                    'detection_type': d.detection_type,
                    'alert_level': d.alert_level,
                    'confidence': d.confidence,
                    'image_filename': d.image_filename,
                    'detected_at': d.detected_at.isoformat(),
                    'details': json.loads(d.detection_details) if d.detection_details else {}
                })
        
        # Get live sessions if requested
        if include_sessions and (not detection_type or detection_type == 'session'):
            sessions = LiveSession.query.filter_by(user_id=user_id).order_by(LiveSession.created_at.desc()).limit(limit).all()
            
            for live_sess in sessions:
                result_data.append({
                    'id': live_sess.id,
                    'type': 'session',
                    'session_name': live_sess.session_name,
                    'detection_count': live_sess.detection_count,
                    'alert_count': live_sess.alert_count,
                    'threat_count': live_sess.threat_count,
                    'duration': live_sess.duration,
                    'video_filename': live_sess.video_filename,
                    'preview_image': live_sess.preview_image,
                    'is_critical': live_sess.is_critical,
                    'created_at': live_sess.created_at.isoformat(),
                    'details': {
                        'models_used': json.loads(live_sess.models_used) if live_sess.models_used else [],
                        'emergency_frames': json.loads(live_sess.emergency_frames) if live_sess.emergency_frames else []
                    }
                })
        
        # Sort by timestamp
        result_data.sort(key=lambda x: x.get('detected_at') or x.get('created_at'), reverse=True)
        
        return jsonify({
            'success': True,
            'data': result_data
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/detection-image/<filename>')
def get_detection_image(filename):
    """Get detection screenshot image"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        # Security check: verify filename format
        user_id = session['user_id']
        
        # Find the detection record to verify ownership
        detection = DetectionHistory.query.filter_by(
            user_id=user_id,
            image_filename=filename
        ).first()
        
        if not detection:
            return jsonify({'error': 'Image not found'}), 404
        
        # Serve the image
        upload_dir = UPLOAD_FOLDER / 'detections'
        filepath = upload_dir / filename

        if filepath.exists():
            return send_from_directory(str(upload_dir), filename)
        else:
            return jsonify({'error': 'Image file not found'}), 404
    except Exception as e:
        return jsonify({'error': str(e)}), 400


@app.route('/stop_camera', methods=['POST'])
def stop_camera():
    """Stop the camera stream"""
    global current_camera
    
    with camera_lock:
        if current_camera is not None:
            current_camera.stop()
            current_camera = None
    
    return jsonify({'success': True})


@app.route('/api/start-recording', methods=['POST'])
def start_recording():
    """Start video recording"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    global is_recording
    
    with recording_lock:
        if is_recording:
            return jsonify({'success': False, 'error': 'Recording already in progress'}), 400
        
        is_recording = True
    
    return jsonify({
        'success': True,
        'message': 'Recording started',
        'timestamp': datetime.utcnow().isoformat()
    })


@app.route('/api/stop-recording', methods=['POST'])
def stop_recording():
    """Stop video recording"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    global is_recording, video_writer, recording_filename, recording_start_time
    
    with recording_lock:
        if not is_recording:
            return jsonify({'success': False, 'error': 'No recording in progress'}), 400
        
        # Finalize video writer
        if video_writer is not None:
            video_writer.release()
            video_writer = None
        
        is_recording = False
        
        duration = 0
        if recording_start_time:
            duration = (datetime.now() - recording_start_time).total_seconds()
            recording_start_time = None
    
    return jsonify({
        'success': True,
        'message': 'Recording stopped',
        'filename': recording_filename,
        'duration': round(duration, 2),
        'timestamp': datetime.utcnow().isoformat()
    })


@app.route('/api/recording-status', methods=['GET'])
def get_recording_status():
    """Get current recording status"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    duration = 0
    if is_recording and recording_start_time:
        duration = (datetime.now() - recording_start_time).total_seconds()
    
    return jsonify({
        'success': True,
        'is_recording': is_recording,
        'filename': recording_filename,
        'duration': round(duration, 2),
        'start_time': recording_start_time.isoformat() if recording_start_time else None
    })


@app.route('/api/videos', methods=['GET'])
def get_videos():
    """Get list of recorded videos"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        video_dir = UPLOAD_FOLDER / 'videos'
        if not video_dir.exists():
            return jsonify({'success': True, 'videos': []})

        videos = []
        all_videos = sorted(
            list(video_dir.glob('*.mp4')) + list(video_dir.glob('*.avi')),
            key=lambda f: f.stat().st_mtime, reverse=True
        )
        for video_file in all_videos:
            stat = video_file.stat()
            videos.append({
                'filename': video_file.name,
                'size': round(stat.st_size / 1024 / 1024, 2),  # MB
                'created': datetime.fromtimestamp(stat.st_mtime).isoformat(),
                'path': f'/api/download-video/{video_file.name}'
            })

        return jsonify({'success': True, 'videos': videos})
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/images', methods=['GET'])
def get_images():
    """Get list of captured images"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        image_dir = UPLOAD_FOLDER / 'detections'
        if not image_dir.exists():
            return jsonify({'success': True, 'images': []})

        images = []
        for image_file in sorted(image_dir.glob('*.jpg'), reverse=True):
            stat = image_file.stat()
            images.append({
                'filename': image_file.name,
                'size': round(stat.st_size / 1024, 2),  # KB
                'created': datetime.fromtimestamp(stat.st_mtime).isoformat(),
                'path': f'/api/detection-image/{image_file.name}'
            })

        return jsonify({'success': True, 'images': images})
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/download-video/<filename>', methods=['GET'])
def download_video(filename):
    """Download a recorded video"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        # Security: validate filename format
        if not filename.endswith(('.mp4', '.avi')) or '..' in filename:
            return jsonify({'error': 'Invalid filename'}), 400

        video_dir = UPLOAD_FOLDER / 'videos'
        filepath = video_dir / filename

        if not filepath.exists():
            return jsonify({'error': 'Video not found'}), 404

        return send_from_directory(str(video_dir), filename, as_attachment=True)
    except Exception as e:
        return jsonify({'error': str(e)}), 400


@app.route('/api/download-image/<filename>', methods=['GET'])
def download_image(filename):
    """Download a captured image"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    
    try:
        # Security: validate filename format
        if not filename.endswith('.jpg') or '..' in filename:
            return jsonify({'error': 'Invalid filename'}), 400

        image_dir = UPLOAD_FOLDER / 'detections'
        filepath = image_dir / filename

        if not filepath.exists():
            return jsonify({'error': 'Image not found'}), 404

        return send_from_directory(str(image_dir), filename, as_attachment=True)
    except Exception as e:
        return jsonify({'error': str(e)}), 400


@app.route('/api/sessions/<int:session_id>', methods=['DELETE'])
def delete_session(session_id):
    """Delete a live monitoring session and its associated files"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    try:
        user_id = session['user_id']
        live_sess = LiveSession.query.filter_by(id=session_id, user_id=user_id).first()
        if not live_sess:
            return jsonify({'success': False, 'error': 'Session not found'}), 404

        # Delete associated files
        for attr, base_dir in [('video_filename', PROCESSED_FOLDER), ('preview_image', PROCESSED_FOLDER)]:
            fname = getattr(live_sess, attr, None)
            if fname:
                fpath = base_dir / fname
                if fpath.exists():
                    fpath.unlink()

        db.session.delete(live_sess)
        db.session.commit()
        return jsonify({'success': True})
    except Exception as e:
        db.session.rollback()
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/videos/<filename>', methods=['DELETE'])
def delete_video(filename):
    """Delete a raw recorded video file"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    try:
        if '..' in filename or not filename.endswith('.mp4'):
            return jsonify({'success': False, 'error': 'Invalid filename'}), 400
        fpath = UPLOAD_FOLDER / 'videos' / filename
        if fpath.exists():
            fpath.unlink()
        return jsonify({'success': True})
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/images/<filename>', methods=['DELETE'])
def delete_image(filename):
    """Delete a detection image and its DB record"""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401
    try:
        if '..' in filename or not filename.endswith('.jpg'):
            return jsonify({'success': False, 'error': 'Invalid filename'}), 400
        user_id = session['user_id']
        record = DetectionHistory.query.filter_by(user_id=user_id, image_filename=filename).first()
        if record:
            db.session.delete(record)
            db.session.commit()
        fpath = UPLOAD_FOLDER / 'detections' / filename
        if fpath.exists():
            fpath.unlink()
        return jsonify({'success': True})
    except Exception as e:
        db.session.rollback()
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/video-analysis-history/<int:analysis_id>', methods=['GET'])
def get_video_analysis(analysis_id):
    """Get details of a specific video analysis"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        analysis = AnalysisHistory.query.filter_by(
            id=analysis_id,
            user_id=user_id,
            analysis_type='video'
        ).first()
        
        if not analysis:
            return jsonify({'success': False, 'error': 'Analysis not found'}), 404
        
        return jsonify({
            'success': True,
            'data': {
                'id': analysis.id,
                'original_filename': analysis.original_filename,
                'uploaded_filename': analysis.filename,
                'created_at': analysis.created_at.isoformat(),
                'total_frames': analysis.total_frames,
                'processed_frames': analysis.processed_frames,
                'detection_count': analysis.detection_count,
                'alert_count': analysis.alert_count,
                'processed_video_url': analysis.processed_video_url,
                'preview_image_url': analysis.preview_image_url,
                'models_used': json.loads(analysis.models_used) if analysis.models_used else [],
                'emergency_frames': json.loads(analysis.emergency_frames) if analysis.emergency_frames else [],
                'frame_summaries': json.loads(analysis.frame_summaries) if analysis.frame_summaries else [],
                'detections': json.loads(analysis.detections) if analysis.detections else []
            }
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/video-analysis-list', methods=['GET'])
def get_video_analysis_list():
    """Get list of all video analyses for current user"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        limit = request.args.get('limit', 20, type=int)
        
        analyses = AnalysisHistory.query.filter_by(
            user_id=user_id,
            analysis_type='video'
        ).order_by(AnalysisHistory.created_at.desc()).limit(limit).all()
        
        return jsonify({
            'success': True,
            'data': [
                {
                    'id': a.id,
                    'original_filename': a.original_filename,
                    'created_at': a.created_at.isoformat(),
                    'detection_count': a.detection_count,
                    'alert_count': a.alert_count,
                    'total_frames': a.total_frames,
                    'processed_frames': a.processed_frames,
                    'preview_image_url': a.preview_image_url,
                    'emergency_frames_count': len(json.loads(a.emergency_frames)) if a.emergency_frames else 0
                }
                for a in analyses
            ]
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


@app.route('/api/reanalyze-video/<int:analysis_id>', methods=['POST'])
def reanalyze_video(analysis_id):
    """Re-analyze a previously uploaded video with new or same models"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        analysis = AnalysisHistory.query.filter_by(
            id=analysis_id,
            user_id=user_id,
            analysis_type='video'
        ).first()
        
        if not analysis:
            return jsonify({'success': False, 'error': 'Analysis not found'}), 404
        
        # Get original uploaded video file
        upload_dir = Path(app.config['UPLOAD_FOLDER'])
        video_path = upload_dir / analysis.filename
        
        if not video_path.exists():
            return jsonify({'success': False, 'error': 'Original video file not found'}), 404
        
        # Re-process video (models are set from session)
        results = process_video_file(str(video_path))
        
        # Create new analysis record
        new_history = AnalysisHistory(
            user_id=user_id,
            analysis_type='video',
            filename=analysis.filename,
            original_filename=analysis.original_filename,
            detections=json.dumps(results.get('detections', [])),
            alert_count=len(results.get('alerts', [])),
            detection_count=len(results.get('detections', [])),
            models_used=json.dumps(session.get('selected_models', [])),
            processed_video_url=results.get('output_url'),
            preview_image_url=results.get('preview_url'),
            emergency_frames=json.dumps(results.get('emergency_frames', [])),
            total_frames=results.get('total_frames', 0),
            processed_frames=results.get('processed_frames', 0),
            frame_summaries=json.dumps(results.get('frame_summaries', []))
        )
        db.session.add(new_history)
        db.session.commit()
        
        return jsonify({
            'success': True,
            'message': 'Video re-analyzed successfully',
            'analysis_id': new_history.id,
            'results': results
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 500


@app.route('/api/emergency-frames/<int:analysis_id>', methods=['GET'])
def get_emergency_frames(analysis_id):
    """Get emergency frames captured during video analysis"""
    if 'user_id' not in session:
        return jsonify({'success': False, 'error': 'Unauthorized'}), 401
    
    try:
        user_id = session['user_id']
        analysis = AnalysisHistory.query.filter_by(
            id=analysis_id,
            user_id=user_id,
            analysis_type='video'
        ).first()
        
        if not analysis:
            return jsonify({'success': False, 'error': 'Analysis not found'}), 404
        
        emergency_frames = json.loads(analysis.emergency_frames) if analysis.emergency_frames else []
        
        # Build full URLs for emergency frame images
        frames_with_urls = []
        for frame_info in emergency_frames:
            frame_data = frame_info.copy()
            frame_data['image_url'] = url_for('processed_file', filename=f"emergency_frames/{frame_info['filename']}")
            frames_with_urls.append(frame_data)
        
        return jsonify({
            'success': True,
            'data': {
                'analysis_id': analysis_id,
                'total_emergency_frames': len(frames_with_urls),
                'frames': frames_with_urls
            }
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400


# ===================== Async Video Analysis =====================

@app.route('/api/start-video-analysis', methods=['POST'])
def start_video_analysis():
    """Upload a video and queue it for background analysis. Returns a job_id."""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401

    if 'video' not in request.files:
        return jsonify({'success': False, 'message': 'No video file uploaded'}), 400

    file = request.files['video']
    if not file.filename:
        return jsonify({'success': False, 'message': 'No file selected'}), 400

    ext = Path(file.filename).suffix.lower()
    if ext not in {'.mp4', '.avi', '.mov', '.mkv'}:
        return jsonify({'success': False, 'message': 'Unsupported file type'}), 400

    filename = secure_filename(file.filename)
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    save_name = f"{timestamp}_{filename}"
    filepath = UPLOAD_FOLDER / save_name
    file.save(str(filepath))

    selected_models = session.get('selected_models', [])
    user_id = session['user_id']

    job_id = str(uuid.uuid4())
    analysis_jobs[job_id] = {
        'status': 'queued',
        'progress': 0,
        'current_frame': 0,
        'total_frames': 0,
        'alerts': [],
        'high_risk_alerts': [],
        'detection_count': 0,
        'alert_count': 0,
        'results': None,
        'analysis_id': None,
        'error': None,
    }

    thread = threading.Thread(
        target=_process_video_background,
        args=(job_id, str(filepath), user_id, selected_models, file.filename),
        daemon=True,
    )
    thread.start()

    return jsonify({'success': True, 'job_id': job_id})


@app.route('/api/analysis-progress/<job_id>')
def get_analysis_progress(job_id):
    """Poll current progress of a background analysis job."""
    if 'user_id' not in session:
        return jsonify({'error': 'Unauthorized'}), 401

    job = analysis_jobs.get(job_id)
    if not job:
        return jsonify({'success': False, 'error': 'Job not found'}), 404

    return jsonify({
        'success': True,
        'status': job['status'],
        'progress': job['progress'],
        'current_frame': job['current_frame'],
        'total_frames': job['total_frames'],
        'alerts': job['alerts'][-30:],
        'high_risk_alerts': job['high_risk_alerts'],
        'detection_count': job['detection_count'],
        'alert_count': job['alert_count'],
        'results': job['results'] if job['status'] == 'done' else None,
        'analysis_id': job['analysis_id'],
        'error': job.get('error'),
    })


def _process_video_background(job_id, filepath, user_id, selected_models, original_filename):
    """Process a video file in a background thread with real-time job state updates."""
    with app.app_context():
        job = analysis_jobs.get(job_id)
        if not job:
            return

        job['status'] = 'processing'

        try:
            # Dedicated processor per job — avoids conflicts with live camera
            local_proc = VideoProcessor()
            if selected_models:
                local_proc.set_selected_models(selected_models)
            local_proc.reset_session_state()

            cap = cv2.VideoCapture(filepath)
            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) or 1
            fps = cap.get(cv2.CAP_PROP_FPS) or 20.0
            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) or 640
            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) or 480

            job['total_frames'] = total_frames

            input_path = Path(filepath)
            fourcc, ext = get_video_codec()
            processed_name = f"{input_path.stem}_processed{ext}"
            preview_name = f"{input_path.stem}_preview.jpg"
            processed_path = PROCESSED_FOLDER / processed_name
            preview_path = PROCESSED_FOLDER / preview_name

            emergency_dir = PROCESSED_FOLDER / 'emergency_frames'
            emergency_dir.mkdir(parents=True, exist_ok=True)
            det_dir = UPLOAD_FOLDER / 'detections'
            det_dir.mkdir(parents=True, exist_ok=True)

            writer = cv2.VideoWriter(
                str(processed_path),
                fourcc,
                fps,
                (width, height),
            )

            all_detections = []
            all_alerts = []
            frame_summaries = []
            emergency_frames = []
            frame_count = 0
            processed_frames = 0

            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break

                if frame_count % PROCESSING_PARAMS['frame_skip'] == 0:
                    results = local_proc.process_frame(frame)
                    all_detections.extend(results['detections'])
                    all_alerts.extend(results['alerts'])
                    frame_summaries.append(results['frame_summary'])
                    writer.write(results['annotated_frame'])
                    processed_frames += 1

                    if processed_frames == 1:
                        cv2.imwrite(str(preview_path), results['annotated_frame'])

                    job['detection_count'] = len(all_detections)
                    job['alert_count'] = len(all_alerts)

                    has_weapon = any(d.get('type') == 'weapon' for d in results['detections'])
                    has_violence = any(a.get('type') == 'violence' for a in results['alerts'])
                    has_critical = any(a.get('severity') in {'HIGH', 'CRITICAL'} for a in results['alerts'])

                    # Capture emergency frame for any critical event
                    if has_critical or has_weapon or has_violence:
                        ef_name = f"emrg_{processed_frames}_t{frame_count}.jpg"
                        cv2.imwrite(str(emergency_dir / ef_name), results['annotated_frame'])
                        alert_type = ('CRITICAL' if has_critical else
                                      'WEAPON' if has_weapon else 'VIOLENCE')
                        emergency_frames.append({
                            'filename': ef_name,
                            'frame_number': processed_frames,
                            'timestamp_frame': frame_count,
                            'alert_type': alert_type,
                            'has_weapon': has_weapon,
                            'has_violence': has_violence,
                            'has_anomaly': False,
                        })

                    # Process each alert — persist HIGH/CRITICAL ones and push to frontend
                    for alert in results['alerts']:
                        sev = alert.get('severity', 'LOW')
                        alert_entry = {
                            'frame': processed_frames,
                            'type': alert.get('type', 'unknown'),
                            'severity': sev,
                            'message': alert.get('message', ''),
                            'confidence': float(alert.get('confidence', 0.0)),
                        }
                        job['alerts'].append(alert_entry)

                        if sev in ('HIGH', 'CRITICAL'):
                            job['high_risk_alerts'].append(alert_entry)

                            atype = alert.get('type', '')
                            if 'weapon' in atype:
                                det_type = 'weapon'
                            elif 'violence' in atype:
                                det_type = 'violence'
                            else:
                                det_type = 'risk'

                            # Save screenshot + DB record
                            try:
                                uid8 = str(uuid.uuid4())[:8]
                                ts_str = datetime.now().strftime('%Y%m%d_%H%M%S')
                                det_filename = f"det_{det_type}_{ts_str}_{uid8}.jpg"
                                cv2.imwrite(str(det_dir / det_filename), results['annotated_frame'])

                                record = DetectionHistory(
                                    user_id=user_id,
                                    detection_type=det_type,
                                    alert_level=sev,
                                    confidence=float(alert.get('confidence', 0.0)),
                                    image_filename=det_filename,
                                    detection_details=json.dumps({
                                        'alert_type': alert.get('type'),
                                        'message': alert.get('message'),
                                        'frame_number': processed_frames,
                                        'source': 'video_analysis',
                                    }),
                                    detected_at=datetime.now(),
                                    session_date=date.today(),
                                )
                                db.session.add(record)
                                db.session.commit()
                            except Exception:
                                db.session.rollback()

                frame_count += 1
                job['current_frame'] = frame_count
                if total_frames > 0:
                    job['progress'] = min(95, int((frame_count / total_frames) * 95))

            cap.release()
            writer.release()

            output_url = f"/processed/{processed_name}" if processed_frames else None
            preview_url = f"/processed/{preview_name}" if processed_frames else None

            risk_counts = {}
            for s in frame_summaries:
                risk_counts[s['pose_risk']] = risk_counts.get(s['pose_risk'], 0) + 1

            # Strip per-frame detections/alerts from summaries — they're captured in
            # all_detections / all_alerts and would bloat the JSON response significantly.
            slim_summaries = [
                {k: v for k, v in s.items() if k not in ('detections', 'alerts')}
                for s in frame_summaries
            ]

            results_data = {
                'total_frames': frame_count,
                'processed_frames': processed_frames,
                'detections': all_detections,
                'alerts': all_alerts,
                'frame_summaries': slim_summaries,
                'output_url': output_url,
                'output_mime': 'video/mp4',
                'preview_url': preview_url,
                'emergency_frames': emergency_frames,
                'summary': {
                    'total_detections': len(all_detections),
                    'total_alerts': len(all_alerts),
                    'emergency_frames_count': len(emergency_frames),
                    'weapon_alert_frames': sum(1 for f in frame_summaries if f['weapon_count'] > 0),
                    'unsafe_pose_frames': sum(1 for f in frame_summaries if f['pose_risk'] in {'LOW_RISK', 'HIGH_RISK'}),
                    'critical_frames': sum(1 for f in frame_summaries if f['alert_state'] == 'CRITICAL'),
                    'pose_risk_counts': risk_counts,
                },
            }

            try:
                history = AnalysisHistory(
                    user_id=user_id,
                    analysis_type='video',
                    filename=Path(filepath).name,
                    original_filename=original_filename,
                    detections=json.dumps(all_detections),
                    alert_count=len(all_alerts),
                    detection_count=len(all_detections),
                    models_used=json.dumps(selected_models),
                    processed_video_url=output_url,
                    preview_image_url=preview_url,
                    emergency_frames=json.dumps(emergency_frames),
                    total_frames=frame_count,
                    processed_frames=processed_frames,
                    frame_summaries=json.dumps(slim_summaries),
                )
                db.session.add(history)
                db.session.commit()
                job['analysis_id'] = history.id
            except Exception:
                db.session.rollback()

            # Ensure all values in results_data are JSON-safe (converts any residual numpy types)
            try:
                json.dumps(results_data)
            except TypeError:
                results_data = json.loads(json.dumps(results_data, default=lambda o: float(o) if hasattr(o, '__float__') else str(o)))

            job['results'] = results_data
            job['status'] = 'done'
            job['progress'] = 100

        except Exception as e:
            job['status'] = 'error'
            job['error'] = str(e)
            try:
                db.session.rollback()
            except Exception:
                pass


# ===================== Initialize Database =====================
def _migrate_db():
    """Add any missing columns to existing tables (safe for repeated runs)."""
    from sqlalchemy import text
    migrations = [
        "ALTER TABLE analysis_history ADD COLUMN original_filename VARCHAR(200)",
        "ALTER TABLE analysis_history ADD COLUMN processed_video_url VARCHAR(500)",
        "ALTER TABLE analysis_history ADD COLUMN preview_image_url VARCHAR(500)",
        "ALTER TABLE analysis_history ADD COLUMN emergency_frames TEXT",
        "ALTER TABLE analysis_history ADD COLUMN total_frames INTEGER DEFAULT 0",
        "ALTER TABLE analysis_history ADD COLUMN processed_frames INTEGER DEFAULT 0",
        "ALTER TABLE analysis_history ADD COLUMN frame_summaries TEXT",
    ]
    for stmt in migrations:
        try:
            db.session.execute(text(stmt))
            db.session.commit()
        except Exception:
            db.session.rollback()


with app.app_context():
    db.create_all()
    _migrate_db()


if __name__ == '__main__':
    import os
    
    # Print NETRA ASCII banner with styling
    banner = """
    
    ╔══════════════════════════════════════════════════════════════════╗
    ║                                                                  ║
    ║   ███╗   ██╗███████╗████████╗██████╗  █████╗                     ║
    ║   ████╗  ██║██╔════╝╚══██╔══╝██╔══██╗██╔══██╗                    ║
    ║   ██╔██╗ ██║█████╗     ██║   ██████╔╝███████║                    ║
    ║   ██║╚██╗██║██╔══╝     ██║   ██╔══██╗██╔══██║                    ║
    ║   ██║ ╚████║███████╗   ██║   ██║  ██║██║  ██║                    ║
    ║   ╚═╝  ╚═══╝╚══════╝   ╚═╝   ╚═╝  ╚═╝╚═╝  ╚═╝                    ║
    ║                                                                  ║
    ║         AI-Powered Video Surveillance & Analysis System          ║
    ║                                                                  ║
    ╚══════════════════════════════════════════════════════════════════╝
    """
    print(banner)
    print("\n" + "═"*70)
    print(f"  📊 Available Models: {model_manager.list_models()}")
    print("═"*70)
    
    # Detect environment
    is_hf_spaces = os.getenv('SYSTEM') == 'spaces' or os.path.exists('/.dockerenv')
    
    if is_hf_spaces:
        # Hugging Face Spaces environment
        host = '0.0.0.0'
        port = 7860
        debug = False
        use_reloader = False
        print(f"\n  🚀 Running on Hugging Face Spaces")
    else:
        # Local development
        host = '0.0.0.0'
        port = int(os.getenv('PORT', 5001))
        debug = True
        use_reloader = True
        print(f"\n  💻 Running in Local Development Mode")
    
    print(f"\n  🌐 Access Application:")
    print(f"     → http://localhost:{port}")
    print(f"     → http://127.0.0.1:{port}")
    print(f"\n  ⚙️  Configuration:")
    print(f"     → Debug Mode: {'ON' if debug else 'OFF'}")
    print(f"     → Host: {host} (all interfaces)")
    print(f"     → Port: {port}")
    print(f"     → Environment: {'Hugging Face Spaces' if is_hf_spaces else 'Local Development'}")
    print(f"\n  📁 Project Root: {PROJECT_ROOT}")
    print("\n" + "═"*70)
    print("\n  ✅ Server is RUNNING")
    print("  Press CTRL+C to stop the server\n")
    print("═"*70 + "\n")
    
    app.run(
        debug=debug, 
        host=host, 
        port=port, 
        threaded=True,
        use_reloader=use_reloader
    )