import os
import json
import logging
import torch
import cv2
import requests
import zipfile
from ultralytics import YOLO

def load_pipeline_node(node_config: dict, models_dir: str) -> dict:
    # Recursively load a model node from configuration.
    model_path = os.path.join(models_dir, node_config["modelFile"])
    if not os.path.exists(model_path):
        logging.error(f"Model file {model_path} not found.")
        raise FileNotFoundError(f"Model file {model_path} not found.")
    logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
    model = YOLO(model_path)
    if torch.cuda.is_available():
        model.to("cuda")
    node = {
        "modelId": node_config["modelId"],
        "modelFile": node_config["modelFile"],
        "triggerClasses": node_config.get("triggerClasses", []),
        "crop": node_config.get("crop", False),
        "minConfidence": node_config.get("minConfidence", None),
        "model": model,
        "branches": []
    }
    for child in node_config.get("branches", []):
        node["branches"].append(load_pipeline_node(child, models_dir))
    return node

def load_pipeline_from_zip(zip_url: str, target_dir: str) -> dict:
    # Download, extract, and load a pipeline configuration from a zip (.mpta) file.
    os.makedirs(target_dir, exist_ok=True)
    zip_path = os.path.join(target_dir, "pipeline.mpta")
    try:
        response = requests.get(zip_url, stream=True)
        if response.status_code == 200:
            with open(zip_path, "wb") as f:
                for chunk in response.iter_content(chunk_size=8192):
                    f.write(chunk)
            logging.info(f"Downloaded .mpta file from {zip_url} to {zip_path}")
        else:
            logging.error(f"Failed to download .mpta file (status {response.status_code})")
            return None
    except Exception as e:
        logging.error(f"Exception downloading .mpta file from {zip_url}: {e}")
        return None

    try:
        with zipfile.ZipFile(zip_path, "r") as zip_ref:
            zip_ref.extractall(target_dir)
        logging.info(f"Extracted .mpta file to {target_dir}")
    except Exception as e:
        logging.error(f"Failed to extract .mpta file: {e}")
        return None
    finally:
        if os.path.exists(zip_path):
            os.remove(zip_path)

    pipeline_json_path = os.path.join(target_dir, "pipeline.json")
    if not os.path.exists(pipeline_json_path):
        logging.error("pipeline.json not found in the .mpta file")
        return None

    try:
        with open(pipeline_json_path, "r") as f:
            pipeline_config = json.load(f)
        return load_pipeline_node(pipeline_config["pipeline"], target_dir)
    except Exception as e:
        logging.error(f"Error loading pipeline.json: {e}")
        return None

def run_pipeline(frame, node: dict, return_bbox: bool = False):
    """
    Processes the frame with the given pipeline node. When return_bbox is True,
    the function returns a tuple (detection, bbox) where bbox is (x1,y1,x2,y2)
    for drawing. Otherwise, returns only the detection.
    """
    try:
        results = node["model"].track(frame, stream=False, persist=True)
        detection = None
        best_box = None
        max_conf = -1

        for r in results:
            for box in r.boxes:
                box_cpu = box.cpu()
                conf = float(box_cpu.conf[0])
                if conf > max_conf and hasattr(box, "id") and box.id is not None:
                    max_conf = conf
                    detection = {
                        "class": node["model"].names[int(box_cpu.cls[0])],
                        "confidence": conf,
                        "id": box.id.item()
                    }
                    best_box = box_cpu

        bbox = None
        if detection and node.get("crop", False) and best_box is not None:
            coords = best_box.xyxy[0]
            x1, y1, x2, y2 = map(int, coords)
            h, w = frame.shape[:2]
            x1, y1 = max(0, x1), max(0, y1)
            x2, y2 = min(w, x2), min(h, y2)
            if x2 > x1 and y2 > y1:
                bbox = (x1, y1, x2, y2)
                frame = frame[y1:y2, x1:x2]

        if detection is not None:
            for branch in node["branches"]:
                if detection["class"] in branch.get("triggerClasses", []):
                    min_conf = branch.get("minConfidence")
                    if min_conf is not None and detection["confidence"] < min_conf:
                        logging.debug(f"Confidence {detection['confidence']} below threshold {min_conf} for branch {branch['modelId']}.")
                        if return_bbox:
                            return detection, bbox
                        return detection
                    res = run_pipeline(frame, branch, return_bbox)
                    if res is not None:
                        if return_bbox:
                            return res
                        return res
            if return_bbox:
                return detection, bbox
            return detection
        if return_bbox:
            return None, None
        return None
    except Exception as e:
        logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
        if return_bbox:
            return None, None
        return None