Update app.py

app.py

@@ -7,11 +7,11 @@ from sahi.utils.cv import visualize_object_predictions, read_image
 from ultralyticsplus import YOLO
 
 def yolov8_inference(
-    image: gr.Image = None,
-    model_path: str = "foduucom/Tyre-Quality-Classification-AI",
-    image_size: int = 640,
-    conf_threshold: float = 0.25,
-    iou_threshold: float = 0.45,
+     image: gr.Image = None,
+    model_path: gr.Dropdown = None,
+    image_size: gr.Slider = 640,
+    conf_threshold: gr.Slider = 0.25,
+    iou_threshold: gr.Slider = 0.45,
 ):
     """
     YOLOv8 inference function
@@ -21,22 +21,15 @@ def yolov8_inference(
         image_size: Image size
         conf_threshold: Confidence threshold
         iou_threshold: IOU threshold
-    
+    Returns:
+        Rendered image
     """
-    # Load your model using the specified model_path (You should adjust this part based on your model loading logic)
     model = YOLO(model_path)
     model.overrides['conf'] = conf_threshold
-    model.overrides['iou'] = iou_threshold
+    model.overrides['iou']= iou_threshold
     model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000
-
-    # Preprocess your image as needed (You should adjust this part based on your preprocessing logic)
-    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-    image_rgb = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
-
-    # Perform inference with your model (You should adjust this part based on your inference logic)
-    results = model(image_cv)
-
+    model.overrides['max_det'] = 1000 
+    image = read_image(image)
     # Observe results (You should adjust this part based on your result extraction logic)
     top_class_index = torch.argmax(results[0].probs).item()
     Class1 = model.names[top_class_index]
@@ -44,14 +37,15 @@ def yolov8_inference(
     return Class1
 
 # Define Gradio input and output components
+
 inputs = [
     gr.Image(type="filepath", label="Input Image"),
-    gr.Textbox(label="Model Path", default="foduucom/Tyre-Quality-Classification-AI"),
-    gr.Number(default=640, label="Image Size"),
-    gr.Slider(minimum=0.0, maximum=1.0, default=0.25, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, default=0.45, label="IOU Threshold"),
+    gr.Dropdown(["foduucom/Tyre-Quality-Classification-AI"], 
+                default="foduucom/Tyre-Quality-Classification-AI", label="Model"),
+    gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
 ]
-
 outputs = gr.Textbox(label="Result")
 
 title = "AI-Powered Tire Quality Inspection: YOLOv8s Enhanced Classification"