upload app.py

app.py

@@ -83,7 +83,10 @@ import gradio as gr
 i = gr.inputs.Image(shape=(112, 112), label="Echocardiogram")
 o = gr.outputs.Image(label="Segmentation Mask")
 
-#examples = [["img1.jpg"], ["img2.jpg"]]
+examples = [["TCGA_CS_5395_19981004_12.png"], 
+            ["TCGA_CS_5395_19981004_14.png"],
+            ["TCGA_DU_5849_19950405_24.png"]]
+
 title = None #"Left Ventricle Segmentation"
 description = "This semantic segmentation model identifies the left ventricle in echocardiogram images."
 # videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020."
@@ -91,4 +94,8 @@ description = "This semantic segmentation model identifies the left ventricle in
 #thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png"
 
 #gr.Interface(segment, i, o, examples=examples, allow_flagging=False, analytics_enabled=False, thumbnail=thumbnail).launch()
-gr.Interface(segment, i, o, allow_flagging=False, analytics_enabled=False).launch()
+gr.Interface(segment, i, o, 
+    allow_flagging = False, 
+    description = description,
+    examples = examples,
+    analytics_enabled = False).launch()