changed default parameter values, removed mp4 vid

app.py

@@ -34,7 +34,7 @@ def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
     print("Inputs Received:", image_name, c_width, n_top, n_gif_imgs)    
 
     image, model_and_data = get_image_data(image_name)
-    # print("model_and_data", model_and_data)
+    
     # Unpack datax
     xtest = model_and_data["xtest"]
     ytest = model_and_data["ytest"]
@@ -79,8 +79,6 @@ def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
     return create_gif(rout['blr'], image_name, segments, instance, prediction[0], n_gif_imgs, n_top)
 
 if __name__ == "__main__":
-    # gradio's image inputs look like this: <PIL.Image.Image image mode=RGB size=305x266 at 0x7F3D01C91FA0>
-    # need to learn how to handle image inputs, or deal with file inputs or just file path strings
     inp = gr.inputs.Image(label="Input Image (Or select an example)", type="pil")
     out = [gr.outputs.HTML(label="Output GIF"), gr.outputs.Textbox(label="Prediction")]
 
@@ -88,9 +86,9 @@ if __name__ == "__main__":
         segmentation_generation, 
         [
             inp,
-            gr.inputs.Slider(minimum=0.01, maximum=0.8, step=0.01, default=0.1, label="cred_width", optional=False),
+            gr.inputs.Slider(minimum=0.01, maximum=0.8, step=0.01, default=0.01, label="cred_width", optional=False),
             gr.inputs.Slider(minimum=1, maximum=10, step=1, default=5, label="n_top_segs", optional=False),
-            gr.inputs.Slider(minimum=10, maximum=50, step=1, default=20, label="n_gif_images", optional=False), 
+            gr.inputs.Slider(minimum=10, maximum=100, step=1, default=30, label="n_gif_images", optional=False), 
         ], 
         outputs=out, 
         examples=[["./imagenet_diego.png", 0.01, 7, 50],