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.gitattributes

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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+Yolo_Model/yolov8n.pt filter=lfs diff=lfs merge=lfs -text
+yolov8n.pt filter=lfs diff=lfs merge=lfs -text

handler.py

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+# handler.py
+from typing import Dict, Any, List
+import base64, io, os, json
+from PIL import Image
+import numpy as np
+
+# Ultralytics YOLO
+from ultralytics import YOLO
+
+# ---- Utilities ----
+def load_image_from_payload(payload: Dict[str, Any]) -> Image.Image:
+    """
+    Supports:
+      - base64 image: payload["inputs"] = "data:image/...;base64,...." or raw base64 string
+      - image URL:    payload["image_url"] = "https://..."
+    """
+    if "inputs" in payload and isinstance(payload["inputs"], str):
+        s = payload["inputs"]
+        # handle optional data URL prefix
+        if s.startswith("data:image"):
+            s = s.split(",", 1)[1]
+        img_bytes = base64.b64decode(s)
+        return Image.open(io.BytesIO(img_bytes)).convert("RGB")
+
+    if "image_url" in payload:
+        # Not recommended in restricted egress; but works if endpoint has outbound internet
+        import requests
+        r = requests.get(payload["image_url"], timeout=10)
+        r.raise_for_status()
+        return Image.open(io.BytesIO(r.content)).convert("RGB")
+
+    raise ValueError("No image provided. Use 'inputs' (base64) or 'image_url'.")
+
+def yolo_to_coco(result) -> List[Dict[str, Any]]:
+    """
+    Convert Ultralytics result to COCO-ish detections:
+      [{"bbox":[x,y,w,h], "score":float, "category_id":int, "category_name":str}]
+    """
+    detections = []
+    names = result.names  # id->name
+    for b in result.boxes:
+        xywh = b.xywh.cpu().numpy().tolist()[0]   # [x_center,y_center,w,h]
+        # convert to top-left x,y,w,h
+        x, y, w, h = xywh
+        x0 = x - w/2
+        y0 = y - h/2
+        conf = float(b.conf.cpu().item())
+        cls_id = int(b.cls.cpu().item())
+        detections.append({
+            "bbox": [float(x0), float(y0), float(w), float(h)],
+            "score": conf,
+            "category_id": cls_id,
+            "category_name": names.get(cls_id, str(cls_id)),
+        })
+    return detections
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        """
+        `path` points to the repo files checked out on the endpoint.
+        If yolov8n.pt is present in the repo, we load it directly.
+        Otherwise we fallback to downloading 'yolov8n.pt'.
+        """
+        weights_path = os.path.join(path, "yolov8n.pt")
+        if not os.path.exists(weights_path):
+            # Fallback: auto-download from Ultralytics/HF cache
+            weights_path = "yolov8n.pt"
+        self.model = YOLO(weights_path)  # loads on first use
+
+        # Optional: warmup for faster first token (small dummy inference)
+        # Create a tiny blank image to compile the model
+        dummy = Image.new("RGB", (320, 320), (128, 128, 128))
+        _ = self.model.predict(dummy, imgsz=320, conf=0.25, verbose=False)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Expected payload:
+          {
+            "inputs": "<base64-image>"   # OR "image_url": "https://..."
+            "imgsz":  (optional int, default 640)
+            "conf":   (optional float, default 0.25)
+            "iou":    (optional float, default 0.45)
+            "max_det":(optional int, default 300)
+          }
+        Returns:
+          {
+            "detections": [ {bbox, score, category_id, category_name}, ... ],
+            "image_shape": [h,w],
+            "model": "yolov8n"
+          }
+        """
+        imgsz = int(data.get("imgsz", 640))
+        conf = float(data.get("conf", 0.25))
+        iou = float(data.get("iou", 0.45))
+        max_det = int(data.get("max_det", 300))
+
+        img = load_image_from_payload(data)
+        w, h = img.size
+
+        results = self.model.predict(
+            img, imgsz=imgsz, conf=conf, iou=iou, max_det=max_det, verbose=False
+        )
+        detections = yolo_to_coco(results[0])
+
+        return {
+            "detections": detections,
+            "image_shape": [h, w],
+            "model": "yolov8n"
+        }

requirements.txt

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+ultralytics>=8.2.0
+opencv-python-headless>=4.9.0
+pillow>=10.0.0
+numpy>=1.24.0

yolov8n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796