update

app.py

@@ -7,7 +7,7 @@ from sahi import AutoDetectionModel
 from PIL import Image
 import plotly.graph_objects as go
 import torch
-import spaces
+#import spaces
 
 import os
 import shutil
@@ -31,7 +31,7 @@ class Detection:
             model_type='yolov8',
             model_path=yolov8_model_path,
             confidence_threshold=0.3,
-            device='cpu'  # Change to 'cuda:0' if you are using a GPU
+            device="cuda:0" # Change to 'cuda:0' if you are using a GPU
         )
 
     def detect_from_image(self, image):
@@ -218,7 +218,7 @@ def upload_image(image):
     """Process the uploaded image (if needed) and display it.""" 
     return image
 
-@spaces.GPU
+#@spaces.GPU
 def apply_detection(image):
     """Run object detection on the uploaded image and return the annotated image.""" 
     # Convert image from PIL to NumPy array
@@ -391,11 +391,11 @@ with gr.Blocks() as demo:
             }
                 
 
-            .gradio-container-5-9-0 .prose * {
+            .gradio-container-5-24-0 .prose * {
                 color: #083484;
             }
 
-            .gradio-container-5-9-0 .prose :first-child {
+            .gradio-container-5-24-0 .prose :first-child {
                 margin-top: 85px
             }
 
@@ -477,7 +477,7 @@ with gr.Blocks() as demo:
                 font-family: 'Montserrat',sans-serif;
             }   
                 
-            .gradio-container-5-9-0 .prose h1, .gradio-container-5-9-0 .prose h2, .gradio-container-5-9-0 .prose h3, .gradio-container-5-9-0 .prose h4, .gradio-container-5-9-0 .prose h5 {
+            .gradio-container-5-24-0 .prose h1, .gradio-container-5-24-0 .prose h2, .gradio-container-5-24-0 .prose h3, .gradio-container-5-24-0 .prose h4, .gradio-container-5-24-0 .prose h5 {
             margin: var(--spacing-xxl) 0 var(--spacing-lg);
             font-weight: var(--prose-header-text-weight);
             line-height: 1.3;
@@ -491,7 +491,7 @@ with gr.Blocks() as demo:
         <header>
             <div class="left">
                 <h1><span>OIS</span><br></h1>
-                <span class="second-line">AI Detection Model</span>
+                <span class="second-line">AI Detection Platform</span>
                 <p>
                     The OIS AI Detection Model enhances manufacturing by using the powerful YOLOv11 algorithm on 
                     a Raspberry Pi for real-time, on-device defect detection. It automates quality control,