Update app.py

app.py

@@ -131,7 +131,7 @@ def compare_models(
         img = img.convert("RGB")
 
     total_steps = len(models) * 2  # phase 1: load, phase 2: inference
-    progress = gr.Progress(total=total_steps)
+    progress = gr.Progress()
 
     # ----- Phase 1: preload weights -----
     detectors: Dict[str, object] = {}
@@ -140,7 +140,7 @@ def compare_models(
             detectors[name] = load_model(name, custom_file)
         except Exception as exc:
             detectors[name] = exc  # store exception for later reporting
-        progress.update(i, desc=f"Loading {name}")
+        progress(i, total=total_steps, desc=f"Loading {name}")
 
     # ----- Phase 2: run inference -----
     results: List[Image.Image] = []
@@ -154,7 +154,7 @@ def compare_models(
             results.append(Image.new("RGB", img.size, (40, 40, 40)))
             emsg = str(detector_or_err)
             legends[name] = "Unavailable (weights not found)" if "No such file" in emsg or "not found" in emsg else f"ERROR: {emsg.splitlines()[0][:120]}"
-            progress.update(step_index, desc=f"Skipped {name}")
+            progress(step_index, total=total_steps, desc=f"Skipped {name}")
             continue
         try:
             annotated, latency = run_single_inference(detector_or_err, img, threshold)
@@ -163,7 +163,7 @@ def compare_models(
         except Exception as exc:
             results.append(Image.new("RGB", img.size, (40, 40, 40)))
             legends[name] = f"ERROR: {str(exc).splitlines()[0][:120]}"
-        progress.update(step_index, desc=f"Inference {name}")
+        progress(step_index, total=total_steps, desc=f"Inference {name}")
 
     yield results, legends  # final output