Update app.py

app.py

@@ -233,13 +233,13 @@ def get_detection_data(image_data):
             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%
+            model_size = image_data.get("model_size", "Small - Better performance and less accuracy")
+            confidence_threshold = image_data.get("confidence_threshold", 0.1)  # Default from Lens Studio
         else:
             full_data_url = image_data
             
-            #model_size = "Small - Better performance and less accuracy"  # Fallback default
-            #confidence_threshold = 0.6  # Fallback default
+            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."}
@@ -254,11 +254,17 @@ def get_detection_data(image_data):
         img = np.array(img)
         img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
 
+        
+
         #image = utils.resize(img)
         resized_image = utils.resize(img) #depth requires resizing 
         print(f"Debug - Resized image shape: {resized_image.shape}")
         image = img
         print(f"Debug - Original image shape: {image.shape}")
+
+        # Dynamically update model size and confidence threshold
+        model_selector(model_size, img_seg, depth_estimator)
+        update_confidence_threshold(confidence_threshold, img_seg)
         
         image_segmentation, objects_data = img_seg.predict(resized_image)
         depthmap, depth_colormap = depth_estimator.make_prediction(resized_image)