Update app.py

app.py

@@ -31,10 +31,6 @@ def show_rendered_image(r,theta,phi):
     
 st.title('3D volumetric rendering with NeRF - A concrete example, Ficus Dataset')
 
-from PIL import Image
-
-image = Image.open('./training(3).gif')
-
 import base64
 
 file = open(r'./training(3).gif', 'rb')
@@ -48,7 +44,12 @@ st.markdown(
 )
 
 st.markdown("[NeRF](https://arxiv.org/abs/2003.08934) proposes an ingenious way to synthesize novel views of a scene by modelling the volumetric scene function through a neural network. The network learns to model the volumetric scene, thus generating novel views (images) of the 3D scene that the model was not shown at training time.")
-st.markdown("![](https://github.com/alesteba/training_NeRF/blob/e89da9448b3993117c78532c14c7142970f0d8df/training(3).gif)")
+# st.markdown("![](https://github.com/alesteba/training_NeRF/blob/e89da9448b3993117c78532c14c7142970f0d8df/training(3).gif)")
+
+st.markdown(
+    f'<img src="data:image/gif;base64,{data_url}" alt="cat gif" width=100%>',
+    unsafe_allow_html=True,
+)
 st.image(image, caption='Training Steps')
 st.markdown("## Interactive Demo")
 
@@ -61,8 +62,8 @@ nerf_loaded = from_pretrained_keras("Alesteba/NeRF_ficus")
 
 # set the values of r theta phi
 r = 4.0
-theta = st.slider("",min_value=0.0, max_value=360.0, label_visibility="hidden")
-phi = st.slider("", min_value=0.0, max_value=360.0, label_visibility="hidden")
+theta = st.slider("key_1",min_value=0.0, max_value=360.0, label_visibility="hidden")
+phi = st.slider("key_2", min_value=0.0, max_value=360.0, label_visibility="hidden")
 # phi = -30.0
 color, depth = show_rendered_image(r, theta, phi)