CI: deploy Docker/PDM Space

services/app_service/deim_model.py

@@ -24,7 +24,7 @@ def _parse_version(version_str: str) -> Tuple[int, ...]:
 
 
 class DeimHgnetV2MDrone(BaseModel):
-    def __init__(self, device: str = "cuda", version: str = "v2"):
+    def __init__(self, device: str, version: str = "v2"):
         self.device = device
         repo_root = Path(__file__).resolve().parents[1]
         default_rel = (

services/app_service/pages/bushland_beacon.py

@@ -4,6 +4,7 @@ import time
 from pathlib import Path
 
 import streamlit as st
+import torch
 from deim_model import DeimHgnetV2MDrone
 from PIL import Image
 
@@ -57,12 +58,12 @@ with st.sidebar:
     conf_thr = st.slider("Minimum confidence threshold", 0.05, 0.95, 0.50, 0.01)
 
 
-  DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-  @st.cache_resource
-  def load_model(device: str = DEVICE):
-      return DeimHgnetV2MDrone(device=device)
 
+@st.cache_resource
+def load_model(device: str = DEVICE):
+    return DeimHgnetV2MDrone(device=device)
 
 
 # =============== Detection helpers ===============

services/app_service/pages/lost_at_sea.py

@@ -4,6 +4,7 @@ import time
 from pathlib import Path
 
 import streamlit as st
+import torch
 from deim_model import DeimHgnetV2MDrone
 from PIL import Image
 
@@ -58,12 +59,12 @@ with st.sidebar:
     conf_thr = st.slider("Minimum confidence threshold", 0.05, 0.95, 0.50, 0.01)
 
 
-  DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-  @st.cache_resource
-  def load_model(device: str = DEVICE):
-      return DeimHgnetV2MDrone(device=device)
 
+@st.cache_resource
+def load_model(device: str = DEVICE):
+    return DeimHgnetV2MDrone(device=device)
 
 
 # =============== Detection helpers ===============
@@ -77,7 +78,7 @@ def run_image_detection(uploaded_file, conf_thr: float = 0.5):
         return
 
     try:
-        model = load_model(device="cuda")
+        model = load_model()
         with st.spinner("Running detection..."):
             annotated = model.predict_image(img, min_confidence=conf_thr)
 

services/app_service/pages/signal_watch.py

@@ -5,6 +5,7 @@ from pathlib import Path
 import cv2
 import numpy as np
 import streamlit as st
+import torch
 from deim_model import DeimHgnetV2MDrone
 from PIL import Image
 
@@ -54,9 +55,11 @@ def _discover_model_entries() -> list[tuple[str, str]]:
 
 MODEL_ENTRIES = _discover_model_entries()
 
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
 
 @st.cache_resource
-def load_model(model_key: str, device: "cuda"):
+def load_model(model_key: str, device: str = DEVICE):
     if model_key == "deim":
         return DeimHgnetV2MDrone(device=device)
     if not model_key.startswith("yolo:"):
@@ -196,7 +199,7 @@ def run_video_feed_detection(
     preview_max_width: int,
 ):
     try:
-        model = load_model(model_key, device="cpu")  # Use CPU for Hugging Face Spaces
+        model = load_model(model_key, device=DEVICE)
     except Exception as exc:
         st.error(str(exc))
         return