Update app.py

app.py

@@ -24,12 +24,12 @@ else:
     torch.save(model.state_dict(), model_path)
 
 # Optimize model for speed
-model.conf = 0.3  # Lower confidence threshold
-model.iou = 0.3   # Non-Maximum Suppression IoU threshold
-model.classes = None  # Detect all 80+ COCO classes
+model.conf = 0.3 
+model.iou = 0.3  
+model.classes = None  
 
 if device.type == "cuda":
-    model.half()  # Use FP16 for faster inference
+    model.half()  
 else:
     torch.set_num_threads(os.cpu_count())
 
@@ -161,8 +161,8 @@ css = """
 }
 """
 
-with gr.Blocks(css=css, title="Real-Time YOLOv5 Video & Image Object Detection") as demo:
-    gr.Markdown("""# Real-Time YOLOv5 Object Detection""", elem_id="title")
+with gr.Blocks(css=css, title="Video & Image Object Detection by YOLOv5") as demo:
+    gr.Markdown("""# YOLOv5 Object Detection""", elem_id="title")
     
     with gr.Tabs():
         with gr.TabItem("Video Detection", elem_classes="tab-item"):
@@ -232,7 +232,7 @@ with gr.Blocks(css=css, title="Real-Time YOLOv5 Video & Image Object Detection")
     
     gr.Markdown("""
     ### Powered by YOLOv5.
-    This application allows real-time object detection using the YOLOv5 model.
+    This application enables seamless object detection using the YOLOv5 model, allowing users to analyze images and videos with high accuracy and efficiency.
     """, elem_classes="footer")
 
 demo.launch()