Update app.py

app.py

@@ -123,7 +123,7 @@ with gr.Blocks(css="style.css") as demo:
         with gr.Column():
             with gr.Row():
                 with gr.Column(scale=3, variant="panel"):
-                    text = gr.Markdown(value="This is a live demo of our work on cardiac ultrasound video generation. The model is trained on 4-chamber cardiac ultrasound videos and can generate realistic 4-chamber videos given a target Left Ventricle Ejection Fraction. Please, start by sampling a random frame from the pool of 100 images taken from the EchoNet-Dynamic dataset, which will act as the conditional image, representing the anatomy of the video. Then, set the target LVEF, and click the button to generate a video. The process takes 30s to 60s. The model running here corresponds to the 1SCM from the paper. **Click on the video to play it.** ")
+                    text = gr.Markdown(value="This is a live demo of our work on cardiac ultrasound video generation. The model is trained on 4-chamber cardiac ultrasound videos and can generate realistic 4-chamber videos given a target Left Ventricle Ejection Fraction. Please, start by sampling a random frame from the pool of 100 images taken from the EchoNet-Dynamic dataset, which will act as the conditional image, representing the anatomy of the video. Then, set the target LVEF, and click the button to generate a video. The process takes 30s to 60s. The model running here corresponds to the 1SCM from the paper. **Click on the video to play it.** [Code is available here](https://github.com/HReynaud/EchoDiffusion) ")
                 with gr.Column(scale=1, min_width="226"):
                     image = gr.Image(interactive=True)
                 with gr.Column(scale=1, min_width="226"):