Update app.py

app.py

@@ -214,9 +214,28 @@ checkpoint = torch.load("Model_IV.pt")
 # model.load_state_dict(checkpoint)  # Load the saved weights
 # model.eval()  # Set the model to evaluation mode
 
-print(type(checkpoint))
-if isinstance(checkpoint, dict):
-    print(checkpoint.keys())
+# 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
+# })
+
+# print(type(checkpoint))
+# if isinstance(checkpoint, dict):
+#     print(checkpoint.keys())
 
 # Define preprocessing
 transform = T.Compose([
@@ -234,9 +253,8 @@ def predict(image):
     
     # Process output (adjust based on your model's format)
     # return output  # or post-process the results as needed
-    results = model(image)
-    print(results)
-    annotated_img = results.render()[0]
+    results = model(image, save=True)
+    annotated_img = Image.load("")
     return annotated_img
 
 # Gradio interface