Update app.py

app.py

@@ -10,8 +10,7 @@ sys.path.insert(0, "/tmp/HorizonNet")
 
 from huggingface_hub import hf_hub_download
 
-#CKPT = hf_hub_download(repo_id="gum-tech/horizonnet-resnet50-rnn", filename="resnet50_rnn.pth")
-CKPT = hf_hub_download(repo_id="gum-tech/horizonnet-resnet50-rnn", filename="resnet50_rnn__st3d.pth")
+CKPT = hf_hub_download(repo_id="gum-tech/horizonnet-resnet50-rnn", filename="resnet50_rnn.pth")
 print(f"Model checkpoint at: {CKPT}")
 
 def predict(image):
@@ -29,13 +28,18 @@ def predict(image):
     image.convert("RGB").resize((1024, 512)).save(img_path)
 
     # ── Step 1: Preprocess (camera alignment) ─────────────────
-    ret = os.system(
+    os.system(
         f"cd /tmp/HorizonNet && python preprocess.py "
         f"--img_glob {img_path} "
-        f"--output_dir /tmp/hn_pre"
+        f"--output_dir /tmp/hn_pre "
+        f"> /tmp/hn_pre/preprocess_log.txt 2>&1"
     )
-    if ret != 0:
-        return json.dumps({"error": "Preprocessing failed", "code": ret})
+    log = open("/tmp/hn_pre/preprocess_log.txt").read() if os.path.exists("/tmp/hn_pre/preprocess_log.txt") else "no log file"
+    print("=== preprocess log ===\n", log, "\n======================")
+
+    aligned = glob.glob("/tmp/hn_pre/*_aligned_rgb.png")
+    if not aligned:
+        return json.dumps({"error": "Preprocessing failed", "log": log})
 
     # Find aligned image output
     aligned = glob.glob("/tmp/hn_pre/*_aligned_rgb.png")