fix: 🐞 gradio fix for space to work.

app.py

@@ -17,11 +17,9 @@ ImageType = TypeVar("ImageType", Image.Image, np.ndarray)
 
 MARKDOWN = """
 # RF-DETR 🔥
-
 [`[code]`](https://github.com/roboflow/rf-detr) 
 [`[blog]`](https://blog.roboflow.com/rf-detr) 
 [`[notebook]`](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb)
-
 RF-DETR is a real-time, transformer-based object detection model architecture developed 
 by [Roboflow](https://roboflow.com/) and released under the Apache 2.0 license.
 """
@@ -51,7 +49,7 @@ create_directory(directory_path=VIDEO_TARGET_DIRECTORY)
 def detect_and_annotate(
         model: RFDETR,
         image: ImageType,
-        confidence: float
+        confidence: float,
 ) -> ImageType:
     detections = model.predict(image, threshold=confidence)
 
@@ -101,7 +99,6 @@ def video_processing_inference(
         confidence: float,
         resolution: int,
         checkpoint: str,
-        progress=gr.Progress(track_tqdm=True)
 ):
     model = load_model(resolution=resolution, checkpoint=checkpoint)
 
@@ -120,7 +117,7 @@ def video_processing_inference(
             annotated_frame = detect_and_annotate(
                 model=model,
                 image=frame,
-                confidence=confidence
+                confidence=confidence,
             )
             annotated_frame = sv.scale_image(annotated_frame, VIDEO_SCALE_FACTOR)
             sink.write_frame(annotated_frame)
@@ -177,8 +174,6 @@ with gr.Blocks() as demo:
                 image_processing_checkpoint_dropdown
             ],
             outputs=image_processing_output_image,
-            cache_examples=True,
-            run_on_click=True
         )
 
         image_processing_submit_button.click(
@@ -234,8 +229,7 @@ with gr.Blocks() as demo:
                 video_processing_resolution_slider,
                 video_processing_checkpoint_dropdown
             ],
-            outputs=video_processing_output_video,
-            run_on_click=True
+            outputs=video_processing_output_video
         )
 
         video_processing_submit_button.click(