Update app.py

app.py

@@ -64,7 +64,6 @@ def process_image(image):
     return image_segmentation, depth_colormap, dist_image, plot_fig
 
 
-
 @spaces.GPU  # Requests GPU for depth estimation
 def test_process_img(image):
     image = utils.resize(image)
@@ -87,7 +86,6 @@ def process_video(vid_path=None):
     return None
 
 
-
 def update_segmentation_options(options):
     img_seg.is_show_bounding_boxes = True if 'Show Boundary Box' in options else False
     img_seg.is_show_segmentation = True if 'Show Segmentation Region' in options else False
@@ -234,8 +232,14 @@ def get_detection_data(image_data):
         if isinstance(image_data, dict):
             nested_dict = image_data.get("image", {}).get("image", {})
             full_data_url = nested_dict.get("data", "")
+            # get model size and confidence threshold
+            model_size = image_data.get("model_size", "Small - Better performance and less accuracy")
+            confidence_threshold = image_data.get("confidence_threshold", 0.6)  # Default to 60%
         else:
             full_data_url = image_data
+            
+            model_size = "Small - Better performance and less accuracy"  # Fallback default
+            confidence_threshold = 0.6  # Fallback default
 
         if not full_data_url:
             return {"error": "No base64 data found in input."}
@@ -250,6 +254,11 @@ def get_detection_data(image_data):
         img = np.array(img)
         img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
 
+        # Apply model size and confidence threshold
+        print(f"Applying settings - Model Size: {model_size}, Confidence Threshold: {confidence_threshold}")
+        model_selector(model_size)  # Update models based on input
+        update_confidence_threshold(confidence_threshold * 100)  # Scale to 0-100 for consistency
+
         #image = utils.resize(img)
         resized_image = utils.resize(img) #depth requires resizing 
         print(f"Debug - Resized image shape: {resized_image.shape}")