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config/__init__.py

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+"""
+Configuration Module
+Centralized settings and model configuration
+"""
+
+from .settings import *
+from .model_config import *

config/model_config.py

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+"""
+Model Configuration
+Centralizes all model paths and loading configurations
+"""
+
+from pathlib import Path
+
+# Project root
+PROJECT_ROOT = Path(__file__).parent.parent
+
+# Model directory (consolidated to ai_models/)
+MODELS_DIR = PROJECT_ROOT / "ai_models"
+
+# Model paths - Organized by detection type
+MODEL_PATHS = {
+    'violence': {
+        'model': MODELS_DIR / "violence_detection" / "violence_model.h5",
+        'labels': MODELS_DIR / "violence_detection" / "lb.pickle",
+        'description': 'Violence Detection Model'
+    },
+    'yolo': {
+        'candidates': [
+            MODELS_DIR / "object_detection" / "yolov8n.pt",
+            MODELS_DIR / "object_detection" / "yolov8s.pt",
+        ],
+        'description': 'YOLO Object Detection Model'
+    },
+    'weapon': {
+        'gun_model_candidates': [
+            MODELS_DIR / "weapon_detection" / "GunDetector.pt",
+            MODELS_DIR / "weapon_detection" / "best.pt",
+        ],
+        'person_model_candidates': [
+            MODELS_DIR / "object_detection" / "yolov8n.pt",
+            MODELS_DIR / "object_detection" / "yolov8s.pt",
+        ],
+        'description': 'Weapon & Person Detection Model'
+    },
+    'pose': {
+        'candidates': [
+            MODELS_DIR / "pose_detection" / "yolo11n-pose.pt",
+        ],
+        'description': 'Pose Detection Model'
+    },
+    'anomaly': {
+        'candidates': [
+            MODELS_DIR / "anomaly_detection" / "best.bin",
+        ],
+        'description': 'Anomaly Detection Model'
+    },
+    'analysis': {
+        'candidates': [
+            MODELS_DIR / "analysis_models" / "fight_detection_model.h5",
+            MODELS_DIR / "analysis_models" / "CustomCNN150.h5",
+            MODELS_DIR / "analysis_models" / "CustomCNN100.h5",
+            MODELS_DIR / "analysis_models" / "CustomCNN.h5",
+        ],
+        'description': 'Advanced Analysis Model (Fight/Behavior Detection)'
+    }
+}
+
+# Detection thresholds
+DETECTION_THRESHOLDS = {
+    'yolo': 0.25,
+    'violence': 0.6,
+    'weapon': 0.5,
+    'anomaly': 0.5,
+}
+
+# Processing parameters
+PROCESSING_PARAMS = {
+    'frame_skip': 10,  # Process every 10th frame in video analysis
+    'alert_threshold': 3,  # Consecutive frames before alert
+    'max_upload_size': 500 * 1024 * 1024,  # 500MB
+}
+
+
+def get_model_path(model_name: str, model_type: str = None):
+    """
+    Get model path by name
+    
+    Args:
+        model_name: 'violence', 'yolo', 'weapon', 'pose', 'anomaly'
+        model_type: specific model variant (if applicable) - for weapon, use 'gun' or 'person'
+    
+    Returns:
+        Path to model file or None if not found
+    """
+    if model_name not in MODEL_PATHS:
+        return None
+    
+    config = MODEL_PATHS[model_name]
+    
+    # Handle single model file case
+    if 'model' in config:
+        return config['model'] if config['model'].exists() else None
+    
+    # Handle weapon model special case with gun and person models
+    if model_name == 'weapon':
+        if model_type == 'gun' and 'gun_model_candidates' in config:
+            for candidate in config['gun_model_candidates']:
+                if candidate.exists():
+                    return candidate
+        elif model_type == 'person' and 'person_model_candidates' in config:
+            for candidate in config['person_model_candidates']:
+                if candidate.exists():
+                    return candidate
+        elif not model_type:
+            # Return gun model by default for weapon
+            for candidate in config.get('gun_model_candidates', []):
+                if candidate.exists():
+                    return candidate
+        return None
+    
+    # Handle standard candidates case
+    if 'candidates' in config:
+        for candidate in config['candidates']:
+            if candidate.exists():
+                return candidate
+    
+    return None
+
+
+def get_all_available_models():
+    """
+    Get list of all available models
+    
+    Returns:
+        dict: {model_name: is_available}
+    """
+    available = {}
+    for model_name in MODEL_PATHS.keys():
+        path = get_model_path(model_name)
+        available[model_name] = path is not None
+    
+    return available

config/settings.py

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+"""
+Settings Configuration
+Application-wide settings and configuration
+"""
+
+from pathlib import Path
+
+# Flask Settings
+FLASK_ENV = 'development'
+DEBUG = True
+SECRET_KEY = 'netra-surveillance-secret-key-2024'
+
+# Paths
+PROJECT_ROOT = Path(__file__).parent.parent
+INSTANCE_FOLDER = PROJECT_ROOT / 'instance'
+UPLOAD_FOLDER = PROJECT_ROOT / 'webapp' / 'uploads'
+PROCESSED_FOLDER = UPLOAD_FOLDER / 'processed'
+
+# Ensure all folders exist
+INSTANCE_FOLDER.mkdir(parents=True, exist_ok=True)
+UPLOAD_FOLDER.mkdir(parents=True, exist_ok=True)
+PROCESSED_FOLDER.mkdir(parents=True, exist_ok=True)
+
+# Database - use absolute path, properly formatted for SQLite on Windows
+DB_FILE = INSTANCE_FOLDER / 'netra.db'
+# Convert absolute path to URL format (3 slashes + drive letter + rest of path)
+DB_URL = str(DB_FILE).replace('\\', '/')
+DATABASE_URI = f'sqlite:///{DB_URL}'
+SQLALCHEMY_TRACK_MODIFICATIONS = False
+
+# File Upload
+MAX_CONTENT_LENGTH = 500 * 1024 * 1024  # 500MB
+
+# Video Processing
+VIDEO_CODEC = 'mp4v'
+VIDEO_FPS = 20.0
+
+# Logging
+LOG_LEVEL = 'INFO'
+LOG_FILE = Path(__file__).parent.parent / 'netra.log'
+
+# CORS Settings
+CORS_ENABLED = False
+
+# Session
+PERMANENT_SESSION_LIFETIME = 3600  # 1 hour
+
+# Model Loading
+AUTO_LOAD_MODELS = True
+GRACEFUL_MODEL_FALLBACK = True