# utils/pho_detector.py

import cv2
import numpy as np
from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction
import logging
from typing import List, Dict, Any

# --- Basic Configuration ---
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- Module-Specific Configuration ---

# All classes this module can detect.
ALL_SUPPORTED_CLASSES = ['pho', 'shelves', 'pile', 'hole']

# Configuration for the first model (detects 'pho' and 'shelves').
MODEL_1_CONFIG = {
    "name": "pho_model",
    # IMPORTANT: Update this path to your actual model location.
    "weights_path": "models/photovoltaic_model/pho_model/best.pt",
    # Class order must match the model's training IDs (0: 'pho', 1: 'shelves').
    "class_names": ['pho', 'shelves'],
    "confidence_threshold": 0.7
}

# Configuration for the second model (detects 'pile' and 'hole').
MODEL_2_CONFIG = {
    "name": "pile_model",
    # IMPORTANT: Update this path to your actual model location.
    "weights_path": "models/photovoltaic_model/pile_model/best.pt",
    # Class order must match the model's training IDs (0: 'pile', 1: 'hole').
    "class_names": ['pile', 'hole'],
    "confidence_threshold": 0.7
}

# Unified SAHI slicing parameters for all tasks in this module.
SAHI_PARAMS = {
    "slice_height": 800,
    "slice_width": 800,
    "overlap_height_ratio": 0.2,
    "overlap_width_ratio": 0.2
}

# Global dictionary to cache loaded model instances.
detection_models: Dict[str, AutoDetectionModel] = {}

# --- Model Management Functions ---

def initialize_model(model_config: Dict[str, Any]) -> AutoDetectionModel:
    """Initializes a single model based on its configuration if not already loaded."""
    global detection_models
    model_name = model_config["name"]
    
    if model_name not in detection_models:
        logger.info(f"Loading detection model '{model_name}' from {model_config['weights_path']}...")
        try:
            model = AutoDetectionModel.from_pretrained(
                model_type="ultralytics",
                model_path=model_config["weights_path"],
                confidence_threshold=model_config["confidence_threshold"],
            )
            detection_models[model_name] = model
            logger.info(f"Model '{model_name}' loaded successfully.")
        except Exception as e:
            logger.error(f"Error loading model '{model_name}': {e}", exc_info=True)
            raise RuntimeError(f"Could not load model '{model_name}': {e}")
            
    return detection_models[model_name]

def initialize_all_pho_models():
    """Initializes all models defined in this module. Called once at application startup."""
    logger.info("Pre-initializing all photovoltaic-related models...")
    initialize_model(MODEL_1_CONFIG)
    initialize_model(MODEL_2_CONFIG)
    logger.info("All photovoltaic models have been initialized.")

# --- Inference and Core Logic ---

def _run_inference_for_model(image_cv: np.ndarray, model_config: Dict[str, Any]) -> List[Dict]:
    """Internal function to run SAHI prediction for a single specified model."""
    try:
        model = initialize_model(model_config)
    except RuntimeError as e:
        logger.error(f"Cannot run inference; model '{model_config['name']}' failed to initialize: {e}")
        return []

    logger.info(f"Running SAHI prediction with model: {model_config['name']}...")
    try:
        sahi_result = get_sliced_prediction(
            image=image_cv,
            detection_model=model,
            **SAHI_PARAMS,
            verbose=0
        )
    except Exception as e:
        logger.error(f"SAHI prediction failed for model '{model_config['name']}': {e}", exc_info=True)
        return []

    targets_output_list = []
    # Use the specific class list for this model to map IDs to names.
    model_class_names = model_config["class_names"] 

    for pred in sahi_result.object_prediction_list:
        cat_id = int(pred.category.id)
        
        # Core Logic: Map the model's local ID to its correct global class name.
        if 0 <= cat_id < len(model_class_names):
            class_name = model_class_names[cat_id]
            minx, miny, maxx, maxy = pred.bbox.minx, pred.bbox.miny, pred.bbox.maxx, pred.bbox.maxy
            
            targets_output_list.append({
                "type": class_name,
                "size": [int(maxx - minx), int(maxy - miny)],
                "leftTopPoint": [int(minx), int(miny)],
                "score": round(float(pred.score.value), 4)
            })
        else:
            logger.warning(f"Model '{model_config['name']}' detected an out-of-range class ID: {cat_id}. Ignoring.")
                           
    logger.info(f"Model '{model_config['name']}' found {len(targets_output_list)} raw objects.")
    return targets_output_list

def detect_objects_by_class(image_cv: np.ndarray, target_classes: List[str]) -> List[Dict]:
    """
    Main public function. Detects objects by intelligently dispatching to the correct model(s).
    
    Args:
        image_cv: The input OpenCV image.
        target_classes: A list of class names to detect (e.g., ['pho', 'hole']).
        
    Returns:
        A list of detection dictionaries for the successfully found targets.
    """
    final_results = []
    
    # Determine which targets belong to Model 1.
    model_1_targets = [cls for cls in target_classes if cls in MODEL_1_CONFIG["class_names"]]
    # Determine which targets belong to Model 2.
    model_2_targets = [cls for cls in target_classes if cls in MODEL_2_CONFIG["class_names"]]

    # Run Model 1 only if it's needed.
    if model_1_targets:
        logger.info(f"Model 1 ('{MODEL_1_CONFIG['name']}') triggered for targets: {model_1_targets}")
        model_1_results = _run_inference_for_model(image_cv, MODEL_1_CONFIG)
        # Filter the raw results to only include what the user asked for.
        final_results.extend([res for res in model_1_results if res["type"] in model_1_targets])

    # Run Model 2 only if it's needed.
    if model_2_targets:
        logger.info(f"Model 2 ('{MODEL_2_CONFIG['name']}') triggered for targets: {model_2_targets}")
        model_2_results = _run_inference_for_model(image_cv, MODEL_2_CONFIG)
        # Filter the raw results to only include what the user asked for.
        final_results.extend([res for res in model_2_results if res["type"] in model_2_targets])
    
    logger.info(f"Photovoltaic detection complete. Returning {len(final_results)} filtered targets.")
    return final_results

# --- Export for main.py ---
# This list is imported by main.py to know which tasks to delegate to this module.
PHO_DETECTOR_SUPPORTED_CLASSES = ALL_SUPPORTED_CLASSES