Update app.py

app.py

@@ -29,8 +29,8 @@ def yolov8_inference(
     
     # observe results
     top_class_index = torch.argmax(results[0].probs).item()
-    Class = model.names[top_class_index]
-    print(Class)
+    Class1 = model.names[top_class_index]
+    globals(Class1)
         
 
 inputs = [
@@ -42,7 +42,7 @@ inputs = [
     gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
 ]
 
-outputs = gr.Image(type="filepath", label="Output Image")
+outputs = gr.Text(Class1)
 title = "AI-Powered Tire Quality Inspection: YOLOv8s Enhanced Classification"
 
 
@@ -54,7 +54,7 @@ Welcome to our 🤖 AI-Powered Tire Quality Inspection Space – a cutting-edge
 About This Space: """
 This interactive platform empowers you to classify tires with unparalleled precision, utilizing a fine-tuned YOLOv8s model 🎯 specifically developed for identifying defects in tire manufacturing. By submitting an image of a tire, you can instantly determine whether it meets the rigorous quality standards required in the industry, helping to ensure safety and reliability in automotive products.
 """
-examples = [['samples/1.jpeg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45], ['samples/2.jpg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45]]
+examples = [['Sample/Bald tyre.jpg', 'Tyre-Quality-Classification-AI', 640, 0.25, 0.45], ['Sample/Good tyre.png', 'Tyre-Quality-Classification-AI', 640, 0.25, 0.45]]
 demo_app = gr.Interface(
     fn=yolov8_inference,
     inputs=inputs,