Spaces:
Running
Running
File size: 4,645 Bytes
17f1f54 085d33c 17f1f54 38d4577 e15eab8 38d4577 17f1f54 c398671 17f1f54 52e71ec 17f1f54 8e81ebe 17f1f54 126af4a 17f1f54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | 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()
|