import os
import sys
import threading
from typing import Dict, Any, List

# Add backend to path so we can import sign_bridge_inference
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
if CURRENT_DIR not in sys.path:
    sys.path.append(CURRENT_DIR)

try:
    from .sign_bridge_inference import SignBridgeInference
except (ImportError, ValueError):
    from sign_bridge_inference import SignBridgeInference

# For Hugging Face Spaces / Docker, we'll store weights in a local weights directory
MODEL_ROOT = os.path.join(os.path.dirname(CURRENT_DIR), "weights")

class SignModel:
    """
    Singleton wrapper for the SignBridgeInference engine.
    Handles thread-safe inference and model lifecycle.
    """
    _instance = None
    _lock = threading.Lock()

    def __new__(cls):
        with cls._lock:
            if cls._instance is None:
                cls._instance = super(SignModel, cls).__new__(cls)
                cls._instance._initialized = False
            return cls._instance

    def __init__(self):
        if self._initialized:
            return
            
        print("Initializing SignBridge Model (Real Integration)...")
        self.engine = None
        self.is_loaded = False
        self._load_error = None
        self._initialized = True
        
        # Load the model in a background thread to avoid blocking FastAPI startup
        threading.Thread(target=self._load_model_async, daemon=True).start()

    def _load_model_async(self):
        try:
            # 1. Ensure weight directory exists
            os.makedirs(MODEL_ROOT, exist_ok=True)
            weight_path = os.path.join(MODEL_ROOT, "best.ckpt")

            # 2. Check if weights need to be downloaded
            if not os.path.exists(weight_path):
                print(f"Standard Weights not found at {weight_path}. Attempting download from Hub...")
                from huggingface_hub import hf_hub_download
                
                repo_id = os.environ.get("HF_REPO_ID_HQ", "ExploWebsite/SignBridge-Weights")
                token = os.environ.get("HF_TOKEN")
                
                print(f"Downloading Standard Weights from {repo_id}...")
                downloaded_file = hf_hub_download(
                    repo_id=repo_id,
                    filename="standard.ckpt", # Expected filename on Hub for standard model
                    local_dir=MODEL_ROOT,
                    token=token
                )
                # Rename the downloaded standard.ckpt to best.ckpt so the original engine finds it
                os.rename(downloaded_file, weight_path)
                print(f"✅ Standard Download complete: {weight_path}")

            # 3. Initialize the inference engine
            self.engine = SignBridgeInference(MODEL_ROOT)
            self.is_loaded = True
            print("✅ SignBridge Model loaded and ready for inference.")
        except Exception as e:
            self._load_error = str(e)
            print(f"❌ Failed to load SignBridge Model: {e}")
            import traceback
            traceback.print_exc()

    def inference(self, text: str) -> Dict[str, Any]:
        """
        Performs inference on the provided text.
        """
        if not self.is_loaded:
            if self._load_error:
                raise RuntimeError(f"Model failed to load: {self._load_error}")
            raise RuntimeError("Model is still loading. Please try again in 30 seconds.")

        print(f"Inference Request: '{text}'")
        
        try:
            # we use 60 sampling steps (tuned to prevent over-smoothing which caused stillness at 100)
            skeletons = self.engine.translate(text, sampling_steps=60)
            
            import uuid
            from video_renderer import render_skeleton_to_video
            
            filename = f"gen_{uuid.uuid4().hex[:8]}.mp4"
            output_dir = os.path.join(CURRENT_DIR, "output")
            os.makedirs(output_dir, exist_ok=True)
            output_path = os.path.join(output_dir, filename)
            
            render_skeleton_to_video(skeletons, output_path, mode="standard")
            
            space_base_url = os.environ.get("HF_SPACE_URL", "https://explowebsite-sign-idd-inference.hf.space")
            video_url = f"{space_base_url}/static/{filename}"
            
            return {
                "skeletons": None,
                "video_url": video_url,
                "glosses": self.engine.text_to_glosses(text)
            }
        except Exception as e:
            print(f"Inference error: {e}")
            raise e

# Global singleton instance
sign_model = SignModel()