Update app.py

app.py

@@ -2,32 +2,35 @@ import gradio as gr
 import torch
 from ultralytics import YOLO
 
-# ✅ Allowlist required classes for safe loading
+# ✅ Import modules for unpickling
 import ultralytics.nn.tasks
+import ultralytics.nn.modules.conv
 import torch.nn.modules.container
 
+# 🧠 Add all required classes to the safe globals list
 torch.serialization.add_safe_globals([
     ultralytics.nn.tasks.DetectionModel,
-    torch.nn.modules.container.Sequential
+    torch.nn.modules.container.Sequential,
+    ultralytics.nn.modules.conv.Conv,
 ])
 
-# ✅ Load the YOLOv8 model (from trusted source like Roboflow)
+# ✅ Load YOLOv8 model
 try:
     model = YOLO("best.pt")
 except Exception as e:
-    raise RuntimeError(f"Failed to load YOLOv8 model: {e}")
+    raise RuntimeError(f"❌ Failed to load YOLOv8 model:\n{e}")
 
-# 🔍 Inference function
+# 🧠 Prediction function
 def detect_objects(image):
     results = model(image)
     annotated_image = results[0].plot()
     return annotated_image
 
-# 🎛️ Gradio UI
+# 🎛️ Gradio interface
 description = """
 📉 **Panel Fault D C Detection App**  
-Detect panel faults using a YOLOv8 model trained on Roboflow.  
-🔗 [Dataset Link](https://universe.roboflow.com/naveen-kumar-9emxl/my-project-1-scnhw)
+Upload an image to detect panel faults using your YOLOv8 model from Roboflow.  
+[Roboflow Dataset](https://universe.roboflow.com/naveen-kumar-9emxl/my-project-1-scnhw)
 """
 
 demo = gr.Interface(
@@ -40,3 +43,5 @@ demo = gr.Interface(
 
 if __name__ == "__main__":
     demo.launch()
+
+