Add handler, requirements, and update preprocessor config for Inference Endpoints

- Add requirements.txt with timm dependency (fixes ImportError on endpoint startup)
- Add custom handler.py for object detection inference
- Update preprocessor_config.json to use modern DetrImageProcessor format

Co-Authored-By: Claude Opus 4.6

handler.py

@@ -0,0 +1,61 @@
+import base64
+import io
+from typing import Any, Dict, List
+
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        self.processor = AutoImageProcessor.from_pretrained(path)
+        self.model = AutoModelForObjectDetection.from_pretrained(path)
+        self.model.eval()
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        inputs = data.get("inputs", data)
+
+        # Handle base64-encoded image
+        if isinstance(inputs, str):
+            image_bytes = base64.b64decode(inputs)
+            image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        elif isinstance(inputs, bytes):
+            image = Image.open(io.BytesIO(inputs)).convert("RGB")
+        elif isinstance(inputs, Image.Image):
+            image = inputs.convert("RGB")
+        else:
+            raise ValueError(
+                "Unsupported input type. Provide a base64-encoded image string or raw bytes."
+            )
+
+        # Run inference
+        with torch.no_grad():
+            encoded = self.processor(images=image, return_tensors="pt")
+            outputs = self.model(**encoded)
+
+        # Post-process: convert to bounding boxes
+        target_size = torch.tensor([image.size[::-1]])  # (height, width)
+        results = self.processor.post_process_object_detection(
+            outputs, threshold=0.5, target_sizes=target_size
+        )[0]
+
+        detections = []
+        for score, label, box in zip(
+            results["scores"], results["labels"], results["boxes"]
+        ):
+            xmin, ymin, xmax, ymax = box.tolist()
+            detections.append(
+                {
+                    "score": round(score.item(), 4),
+                    "label": self.model.config.id2label[label.item()],
+                    "box": {
+                        "xmin": round(xmin, 2),
+                        "ymin": round(ymin, 2),
+                        "xmax": round(xmax, 2),
+                        "ymax": round(ymax, 2),
+                    },
+                }
+            )
+
+        return detections

preprocessor_config.json

@@ -1,7 +1,7 @@
 {
   "do_normalize": true,
   "do_resize": true,
-  "feature_extractor_type": "DetrFeatureExtractor",
+  "image_processor_type": "DetrImageProcessor",
   "format": "coco_detection",
   "image_mean": [
     0.485,
@@ -13,6 +13,8 @@
     0.224,
     0.225
   ],
-  "max_size": 800,
-  "size": 800
+  "size": {
+    "shortest_edge": 800,
+    "longest_edge": 800
+  }
 }

requirements.txt

@@ -0,0 +1 @@
+timm