Update

app.py

@@ -10,9 +10,12 @@ import cv2
 import gradio as gr
 import huggingface_hub
 import numpy as np
+import spaces
 import torch
 from huggingface_hub import hf_hub_download
 
+torch.jit.script = lambda f: f
+
 sys.path.insert(0, "face_detection")
 sys.path.insert(0, "face_parsing")
 sys.path.insert(0, "fpage")
@@ -55,7 +58,10 @@ for lfs_model_path in lfs_model_paths:
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-detector = RetinaFacePredictor(threshold=0.8, device=device, model=RetinaFacePredictor.get_model("mobilenet0.25"))
+detector = RetinaFacePredictor(threshold=0.8, device="cpu", model=RetinaFacePredictor.get_model("mobilenet0.25"))
+detector.device = device
+detector.net.to(device)
+
 model = AgeEstimator(
     device=device,
     ckpt=huggingface_hub.hf_hub_download("hysts/ibug", "fpage/models/fpage-resnet50-fcn-14-97.torch"),
@@ -66,6 +72,7 @@ model = AgeEstimator(
 )
 
 
+@spaces.GPU
 def predict(image: np.ndarray, max_num_faces: int) -> np.ndarray:
     colormap = label_colormap(14)
 
@@ -124,7 +131,6 @@ with gr.Blocks(css="style.css") as demo:
         inputs=[image, max_num_faces],
         outputs=result,
         fn=predict,
-        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
     )
     run_button.click(
         fn=predict,