Update handler.py

handler.py

@@ -38,39 +38,39 @@ class EndpointHandler:
         return image_tensor
 
     def __call__(self, data):
-    """
-    Process incoming raw binary image data.
-    """
-    try:
-        if "body" not in data:
-            return {"error": "No image data provided in request."}
+        """
+        Process incoming raw binary image data.
+        """
+        try:
+            if "body" not in data:
+                return {"error": "No image data provided in request."}
 
-        image_bytes = data["body"] 
-        image_tensor = self.preprocess_frame(image_bytes)
+            image_bytes = data["body"] 
+            image_tensor = self.preprocess_frame(image_bytes)
 
-        with torch.no_grad():
-            predictions = self.model(image_tensor)
+            with torch.no_grad():
+                predictions = self.model(image_tensor)
 
-        boxes = predictions[0]["boxes"].cpu().tolist()
-        labels = predictions[0]["labels"].cpu().tolist()
-        scores = predictions[0]["scores"].cpu().tolist()
+            boxes = predictions[0]["boxes"].cpu().tolist()
+            labels = predictions[0]["labels"].cpu().tolist()
+            scores = predictions[0]["scores"].cpu().tolist()
 
-        results = []
-        for box, label, score in zip(boxes, labels, scores):
-            if score >= CONFIDENCE_THRESHOLD:
-                x1, y1, x2, y2 = map(int, box) 
-                label_text = self.label_map.get(label, "unknown") 
-                results.append({
-                    "label": label_text,
-                    "score": round(score, 2), 
-                    "box": {
-                        "xmin": x1,
-                        "ymin": y1,
-                        "xmax": x2,
-                        "ymax": y2
-                    }
-                })
+            results = []
+            for box, label, score in zip(boxes, labels, scores):
+                if score >= CONFIDENCE_THRESHOLD:
+                    x1, y1, x2, y2 = map(int, box) 
+                    label_text = self.label_map.get(label, "unknown") 
+                    results.append({
+                        "label": label_text,
+                        "score": round(score, 2), 
+                        "box": {
+                            "xmin": x1,
+                            "ymin": y1,
+                            "xmax": x2,
+                            "ymax": y2
+                        }
+                    })
 
-        return results 
-    except Exception as e:
-        return {"error": str(e)}
+            return results 
+        except Exception as e:
+            return {"error": str(e)}