yolo

app.py

@@ -240,13 +240,12 @@ def infer(image: Image.Image, model_id: str, confidence_threshold: float = 0.75)
             "Label": f"Error: {str(e)}"
         }
 
-def full_prediction(img, confidence_threshold, augment_methods, rotate_degrees, noise_level, sharpen_strength):
+def full_prediction(img, confidence_threshold, rotate_degrees, noise_level, sharpen_strength):
     """Full prediction run, with a team of ensembles and agents.
 
     Args:
         img (url: str, Image.Image, np.ndarray): The input image to classify.
         confidence_threshold (float, optional): The confidence threshold for classification. Defaults to 0.75.
-        augment_methods (list, optional): The augmentation methods to use.
         rotate_degrees (int, optional): The degrees to rotate the image.
         noise_level (int, optional): The noise level to use.
         sharpen_strength (int, optional): The sharpen strength to use.
@@ -283,9 +282,8 @@ def full_prediction(img, confidence_threshold, augment_methods, rotate_degrees,
     context_agent = ContextualIntelligenceAgent()
     anomaly_agent = ForensicAnomalyDetectionAgent()
     health_agent.monitor_system_health()
-
-    if augment_methods:
-        img_pil, _ = augment_image(img, augment_methods, rotate_degrees, noise_level, sharpen_strength)
+    if rotate_degrees or noise_level or sharpen_strength:
+        img_pil, _ = augment_image(img, ["rotate", "add_noise", "sharpen"], rotate_degrees, noise_level, sharpen_strength)
     else:
         img_pil = img
     img_np_og = np.array(img)
@@ -429,7 +427,6 @@ detection_model_eval_playground = gr.Interface(
     inputs=[
         gr.Image(label="Upload Image to Analyze", sources=['upload', 'webcam'], type='filepath'),
         gr.Slider(0.0, 1.0, value=0.7, step=0.05, label="Confidence Threshold"),
-        gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], label="Augmentation Methods"),
         gr.Slider(0, 45, value=0, step=1, label="Rotate Degrees", visible=False),
         gr.Slider(0, 50, value=0, step=1, label="Noise Level", visible=False),
         gr.Slider(0, 50, value=0, step=1, label="Sharpen Strength", visible=False)