Update app.py

app.py

@@ -214,24 +214,24 @@ checkpoint = torch.load("Model_IV.pt")
 # model.load_state_dict(checkpoint)  # Load the saved weights
 # model.eval()  # Set the model to evaluation mode
 
-# from ultralytics import settings
-# # Update multiple settings
-# settings.update({
-#     "names": {0: 'A', 1: 'B',
-#               2: 'C', 3: 'D', 
-#               4: 'E', 5: 'F', 
-#               6: 'G',  7: 'H', 
-#               8: 'I', 9: 'J', 
-#               10: 'K', 11: 'L', 
-#               12: 'M', 13: 'N', 
-#               14: 'O', 15: 'P', 
-#               16: 'Q', 17: 'R', 
-#               18: 'S', 19: 'T', 
-#               20: 'U', 21: 'V', 
-#               22: 'W', 23: 'X', 
-#               24: 'Y', 25: 'Z'},
-#     "tensorboard": False
-# })
+from ultralytics import settings
+# Update multiple settings
+settings.update({
+    "names": {0: 'A', 1: 'B',
+              2: 'C', 3: 'D', 
+              4: 'E', 5: 'F', 
+              6: 'G',  7: 'H', 
+              8: 'I', 9: 'J', 
+              10: 'K', 11: 'L', 
+              12: 'M', 13: 'N', 
+              14: 'O', 15: 'P', 
+              16: 'Q', 17: 'R', 
+              18: 'S', 19: 'T', 
+              20: 'U', 21: 'V', 
+              22: 'W', 23: 'X', 
+              24: 'Y', 25: 'Z'},
+    save_dir: ''
+})
 
 # print(type(checkpoint))
 # if isinstance(checkpoint, dict):
@@ -254,7 +254,7 @@ def predict(image):
     # Process output (adjust based on your model's format)
     # return output  # or post-process the results as needed
     results = model(image, save=True)
-    annotated_img = Image.load("B111.JPG")
+    annotated_img = Image.open("B111.JPG")
     return annotated_img
 
 # Gradio interface