Update app.py

app.py

@@ -4,8 +4,12 @@ from PIL import Image
 import numpy as np
 import cv2
 
-# Load the YOLOv8 model (ensure this path is correct)
-model = YOLO('yolov8n.pt')
+# Load the YOLOv8 model (make sure the path is correct and model is downloaded)
+try:
+    model = YOLO('yolov8n.pt')
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
 
 def identify_disease(image):
     # Convert the image to RGB if it's not
@@ -13,12 +17,17 @@ def identify_disease(image):
         image = image.convert('RGB')
     
     # Perform inference
-    results = model(image)
-    predictions = results[0]
+    try:
+        results = model(image)
+        predictions = results[0]
+        print("Inference completed.")
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        return image, [{"Disease": "Error", "Confidence": "N/A"}]
     
     # Check if there are any detections
     if len(predictions.boxes) == 0:
-        # No detections, return the image with a message
+        print("No detections found.")
         annotated_image = np.array(image)
         cv2.putText(annotated_image, "No disease detected", (10, 30), 
                     cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
@@ -27,10 +36,10 @@ def identify_disease(image):
     
     # Extract predictions
     boxes = predictions.boxes
-    labels = boxes.cls.cpu().numpy()
-    scores = boxes.conf.cpu().numpy()
+    labels = boxes.cls.cpu().numpy() if boxes.cls is not None else []
+    scores = boxes.conf.cpu().numpy() if boxes.conf is not None else []
     class_names = model.names
-    
+
     # Annotate image with bounding boxes and labels
     annotated_image = np.array(image)
     for box, label, score in zip(boxes.xyxy.cpu().numpy(), labels, scores):