Upload gradio_app.py with huggingface_hub

gradio_app.py

@@ -11,9 +11,7 @@ except ImportError:
 import torch
 
 from monopriors.relative_depth_models import (
-    DepthAnythingV2Predictor,
     RelativeDepthPrediction,
-    UniDepthRelativePredictor,
     get_relative_predictor,
     RELATIVE_PREDICTORS,
 )
@@ -37,9 +35,10 @@ model_load_status: str = "Models loaded and ready to use!"
 DEVICE: Literal["cuda"] | Literal["cpu"] = (
     "cuda" if torch.cuda.is_available() else "cpu"
 )
+MODELS_TO_SKIP: list[str] = []
 if gr.NO_RELOAD:
-    MODEL_1 = DepthAnythingV2Predictor(device=DEVICE)
-    MODEL_2 = UniDepthRelativePredictor(device=DEVICE)
+    MODEL_1 = get_relative_predictor("DepthAnythingV2Predictor")(device=DEVICE)
+    MODEL_2 = get_relative_predictor("Metric3DRelativePredictor")(device=DEVICE)
 
 
 def predict_depth(
@@ -53,6 +52,7 @@ def predict_depth(
 if IN_SPACES:
     predict_depth = spaces.GPU(predict_depth)
     # remove any model that fails on zerogpu spaces
+    MODELS_TO_SKIP.extend(["Metric3DRelativePredictor"])
 
 
 def load_models(
@@ -72,11 +72,15 @@ def load_models(
     progress(0, desc="Loading Models please wait...")
 
     models: list[int] = [model_1, model_2]
+    # check if the models are in the list of models to skip
+    if any(model in MODELS_TO_SKIP for model in models):
+        raise gr.Error(
+            f"Model not supported on ZeroGPU, please try another model: {MODELS_TO_SKIP}"
+        )
     loaded_models = []
 
     for model in models:
         loaded_models.append(get_relative_predictor(model)(device=DEVICE))
-
         progress(0.5, desc=f"Loaded {model}")
 
     progress(1, desc="Models Loaded")