Update app.py

app.py

@@ -23,34 +23,27 @@ model = YOLO('foduucom/stockmarket-future-prediction')
 
 #############################################################Image Inference############################################################
 def yolov8_img_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45,
+    image: gr.Input(type="filepath", label="Input Image"),
+    model_path: str = 'foduucom/stockmarket-future-prediction',  # Provide a default value
+    image_size: gr.Input(type="number", minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    conf_threshold: gr.Input(type="number", minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    iou_threshold: gr.Input(type="number", minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
 ):
-    """
-    YOLOv8 inference function
-    Args:
-        image: Input image
-        model_path: Path to the model
-        image_size: Image size
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
+    # Ensure that model_path is not None before creating the YOLO model
+    if model_path is None:
+        model_path = 'foduucom/stockmarket-future-prediction'
+    
     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 
-    # image = read_image(image)
     results = model.predict(image)
     render = render_result(model=model, image=image, result=results[0])
     
     return render
 
+
     
 inputs_image = [
     gr.inputs.Image(type="filepath", label="Input Image"),