chore: reformat code and delete assets

app.py

@@ -1,12 +1,10 @@
 import streamlit as st
 import tensorflow as tf
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Setting random seed to obtain reproducible results.
 tf.random.set_seed(42)
 
-
 # Initialize global variables.
 AUTO = tf.data.AUTOTUNE
 BATCH_SIZE = 1
@@ -129,6 +127,7 @@ def map_fn(pose):
     )
     return (rays_flat, t_vals)
 
+
 def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
     """Generates the RGB image and depth map from model prediction.
 
@@ -182,7 +181,6 @@ def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
         depth_map = tf.reduce_sum(weights * t_vals[:, None, None], axis=-1)
     return (rgb, depth_map)
 
-nerf_loaded = tf.keras.models.load_model("nerf", compile=False)
 
 def get_translation_t(t):
     """Get the translation matrix for movement in t."""
@@ -243,15 +241,20 @@ def show_rendered_image(r,theta,phi):
     )
     return(rgb[0], depth[0])
 
+
 # app.py text matter starts here
 st.title('NeRF:3D volumetric rendering with NeRF')
 st.markdown("Authors: [Aritra Roy Gosthipathy](https://twitter.com/ariG23498) and [Ritwik Raha](https://twitter.com/ritwik_raha)")
 st.markdown("## Description")
 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.")
 st.markdown("## Interactive Demo")
+
+# load the pre-trained model
+nerf_loaded = tf.keras.models.load_model("nerf", compile=False)
+
 # set the values of r theta phi
 r = 4.0
-theta = st.slider('Enter a value for theta',min_value = 0.0,max_value = 360.0)
+theta = st.slider("Enter a value for Θ:", min_value=0.0, max_value=360.0)
 phi = -30.0
 color, depth = show_rendered_image(r, theta, phi)
 
@@ -259,13 +262,11 @@ col1, col2= st.columns(2)
 
 with col1:
     color = tf.keras.utils.array_to_img(color)
-
-    st.image(color, caption = "Color",clamp = True, width = 300)
-
+    st.image(color, caption="Color Image", clamp=True, width=300)
 
 with col2:
     depth = tf.keras.utils.array_to_img(depth[..., None])
-    st.image(depth, caption = "Depth",clamp = True, width = 300)
+    st.image(depth, caption="Depth Map", clamp=True, width=300)
 
 st.markdown("## Tutorials")  
 st.markdown("- [Keras](https://keras.io/examples/vision/nerf/)")
@@ -276,9 +277,3 @@ st.markdown("- [PyImageSearch NeRF 3](https://www.pyimagesearch.com/2021/11/24/c
 st.markdown("## Credits")  
 st.markdown("- [PyImageSearch](https://www.pyimagesearch.com/)")
 st.markdown("- [JarvisLabs.ai GPU credits](https://jarvislabs.ai/)")
-
-
-
-
-
-