model movement

tasks/image.py

@@ -5,6 +5,7 @@ import numpy as np
 from sklearn.metrics import accuracy_score, precision_score, recall_score
 import random
 import os
+from pathlib import Path
 from ultralytics import YOLO  
 from .utils.evaluation import ImageEvaluationRequest
 from .utils.emissions import tracker, clean_emissions_data, get_space_info
@@ -100,7 +101,9 @@ async def evaluate_image(request: ImageEvaluationRequest):
     # Update the code below to replace the random baseline with your model inference
     #--------------------------------------------------------------------------------------------   
     
-    model = YOLO("../models/best.pt")
+    model_path = Path("tasks", "models")
+    model_name = "best.pt"
+    model = YOLO(Path(model_path, model_name), task="detect")
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model = model.to(device)    
     predictions = []