Create handler.py

handler.py

@@ -0,0 +1,67 @@
+from huggingface_hub import hf_hub_download
+from typing import Dict, List, Any
+from ultralytics import YOLO
+import json
+import urllib.request
+import cv2
+from io import BytesIO
+import numpy as np
+class EndpointHandler():
+    def __init__(self, path=""):
+        hf_hub_download(repo_id="Drazcat-AI/flejes", filename="flejes-16/runs/detect/train/weights/best.pt")
+        self.model = YOLO(hf_hub_download(repo_id="Drazcat-AI/flejes", filename="flejes-16/runs/detect/train/weights/best.pt", local_files_only=True))
+    
+    def predict_objects(self, image_path, image_size_m):
+        results = self.model(image_path, imgsz=[1280, 960])
+        predictions = []
+        for box in results[0].boxes:
+            class_id = results[0].names[box.cls[0].item()]
+            cords = box.xywh[0].tolist()
+            #cords = [round(x) for x in cords]
+            conf = box.conf[0].item()
+            prediction = {
+            "x": round(cords[0]*image_size_m[0]),
+            "y": round(cords[1]*image_size_m[1]),
+            "width": round(cords[2]*image_size_m[0]),
+            "height": round(cords[3]*image_size_m[1]),
+            "confidence": conf,
+            "class": class_id
+            }
+            predictions.append(prediction)
+        predictions_array = {"predictions": predictions}   
+
+        return predictions_array
+
+    def __call__(self, event):
+        if "inputs" not in event:
+            return {
+                "statusCode": 400,
+                "body": json.dumps("Error: Please provide an 'inputs' parameter."),
+            }
+
+        image_path = event["inputs"]
+
+        try:
+            with urllib.request.urlopen(image_path) as response:
+                image_content = np.asarray(bytearray(response.read()), dtype=np.uint8)
+            image = cv2.imdecode(image_content, cv2.IMREAD_COLOR)
+            """
+            image_size = image.shape
+            if image.shape[0]>image.shape[0]:
+                x, y = 1280, 960
+            else:
+                y, x = 1280, 960
+            image = cv2.resize(image, (x, y))
+            
+            predictions = self.predict_objects(image, [image_size[0]/x,image_size[1]/y])
+            """
+            predictions = self.predict_objects(image, (1,1))
+            return {
+                "statusCode": 200,
+                "body": json.dumps(predictions),
+            }
+        except Exception as e:
+            return {
+                "statusCode": 500,
+                "body": json.dumps(f"Error: {str(e)}"),
+            }