Update app.py

app.py

@@ -1,12 +1,9 @@
 import gradio as gr
 import torch
+import numpy as np
 from huggingface_hub import hf_hub_download
 from depth_anything_v2.dpt import DepthAnythingV2
 
-def dummy_infer(img):
-    return 255 - img
-
-# --- LOAD THE MODEL, BUT DON'T USE IT ---
 DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
 model_configs = {
     'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
@@ -21,7 +18,13 @@ model_path = hf_hub_download(
 state_dict = torch.load(model_path, map_location="cpu")
 model.load_state_dict(state_dict)
 model = model.to(DEVICE).eval()
-# --- END MODEL LOADING ---
 
-iface = gr.Interface(fn=dummy_infer, inputs=gr.Image(type="numpy"), outputs=gr.Image())
+def infer(img):
+    with torch.no_grad():
+        depth = model.infer_image(img[:, :, ::-1])  # BGR to RGB if needed
+        # Normalize to 0-255 and convert to uint8
+        depth_norm = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        return depth_norm.astype(np.uint8)
+
+iface = gr.Interface(fn=infer, inputs=gr.Image(type="numpy"), outputs=gr.Image())
 iface.launch()