upload model.pt and app.py

app.py

@@ -0,0 +1,36 @@
+import cv2
+import torch
+import numpy as np
+from ultralytics import YOLO
+from fastapi import FastAPI
+
+app = FastAPI()
+model = YOLO('model.pt')
+#device = torch.device('cuda')
+#model.to(device)
+@app.post("/detect/")
+def detect(img):
+    try:
+        objects_dict = {}
+        img_arr = cv2.imread(img)
+        img_arr = cv2.resize(img_arr, (416, 416))
+        img_arr = np.array([img_arr.transpose(2, 0, 1)])
+        #img_arr = torch.from_numpy(img_arr).float().to(device)
+        results = model(img_arr)
+
+        for r in results:
+            n = len(r.boxes.cls)
+
+            for i in range(n):
+                cls = int(r.boxes.cls[i].cpu())
+                temp_obj = [int(r.boxes.conf[i].cpu()), r.boxes.xyxy[i].cpu().numpy()] #уверенность модели, координаты прямоугольника
+
+                if cls not in objects_dict:
+                    objects_dict[cls] = [temp_obj]
+                else:
+                    objects_dict[cls].append(temp_obj)
+        return objects_dict
+
+    except Exception as e:
+        print(str(e))
+        return {}

model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0f1fe9c588ffb298ae4ae37110f3bc6645cc6a00579203efeaf37c224c7923f
+size 311056786