Update app.py

app.py

@@ -14,23 +14,24 @@ def predict(image):
     height = int(image.height * ratio)
     image = image.resize((width, height))
 
-    detections = model_pipeline(image, threshold=0.1)
+    detections = model_pipeline(image, threshold=0.9)
 
     draw = ImageDraw.Draw(image)
     table_rows = []
-
+    
     for det in detections:
         box = det["box"]
         label = det["label"]
         score = round(det["score"], 4)
-        table_rows.append({
-            "Class": label,
-            "Confidence": f"{score:.2%}",
-            "Xmin": int(box["xmin"]),
-            "Ymin": int(box["ymin"]),
-            "Xmax": int(box["xmax"]),
-            "Ymax": int(box["ymax"]),
-        })
+        table_rows.append([
+            label,
+            f"{score:.2%}",
+            int(box["xmin"]),
+            int(box["ymin"]),
+            int(box["xmax"]),
+            int(box["ymax"])
+        ])
+        table_rows.sort(key=lambda x: float(x[1].strip('%')), reverse=True)
 
         draw.rectangle(
             [(box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])],