Update app.py

app.py

@@ -7,30 +7,35 @@ from geti_sdk.utils import show_image_with_annotation_scene
 deployment = Deployment.from_folder("deployment")
 deployment.load_inference_models(device="CPU")
 
-def resize_image(image, max_dimension):
+
+def resize_image(image, target_dimension):
     height, width = image.shape[:2]
-    if max(height, width) <= max_dimension:
-        return image
-    if height > width:
-        new_height = max_dimension
-        new_width = int(width * (max_dimension / height))
-    else:
-        new_width = max_dimension
-        new_height = int(height * (max_dimension / width))
+    max_dimension = max(height, width)
+    scale_factor = target_dimension / max_dimension
+    new_width = int(width * scale_factor)
+    new_height = int(height * scale_factor)
     resized_image = cv2.resize(image, (new_width, new_height))
     return resized_image
 
+
 def infer(image=None):
     if image is None:
-        return None
+        return [None,'no image']
+    
+    image = resize_image(image, 1200)
+
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     prediction = deployment.infer(image)
     output = show_image_with_annotation_scene(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), prediction, show_results=False)
     output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-    # output = cv2.resize(output, (224, 224))
-    return output
+    return [output, prediction.overview]
 
-gr.Interface(fn=infer, 
+
+interface = gr.Interface(fn=infer, 
              inputs='image',
-             outputs='image',
-             examples=["eggsample1.png", "eggsample2.png"]).launch()
+             outputs=['image','text'],
+             allow_flagging='manual',
+             flagging_dir='flagged',
+             examples=["eggsample1.png", "eggsample2.png"])
+
+interface.launch()