Update app.py

app.py

@@ -2,22 +2,13 @@ import streamlit as st
 import tensorflow as tf
 import numpy as np
 
+from config import *
 from transformations import *
 from rendering import *
 
 # Setting random seed to obtain reproducible results.
 tf.random.set_seed(42)
 
-# Initialize global variables.
-AUTO = tf.data.AUTOTUNE
-BATCH_SIZE = 1
-NUM_SAMPLES = 32
-POS_ENCODE_DIMS = 16
-EPOCHS = 30
-H = 25
-W = 25
-focal = 0.6911112070083618
-
 def show_rendered_image(r,theta,phi):
     # Get the camera to world matrix.
     c2w = pose_spherical(theta, phi, r)
@@ -35,23 +26,18 @@ def show_rendered_image(r,theta,phi):
 
 # 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")
 
 # download the model:
-
-# from huggingface_hub import snapshot_download
-# snapshot_download(repo_id="Alesteba/your-model-name", local_dir="./nerf")
+# from my own model repo
 
 from huggingface_hub import from_pretrained_keras
-
 nerf_loaded = from_pretrained_keras("Alesteba/NeRF_ficus")
 
-# 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 Θ:", min_value=0.0, max_value=360.0)
@@ -68,12 +54,6 @@ with col2:
     depth = tf.keras.utils.array_to_img(depth[..., None])
     st.image(depth, caption="Depth Map", clamp=True, width=300)
 
-st.markdown("## Tutorials")  
-st.markdown("- [Keras](https://keras.io/examples/vision/nerf/)")
-st.markdown("- [PyImageSearch NeRF 1](https://www.pyimagesearch.com/2021/11/10/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-1/)")
-st.markdown("- [PyImageSearch NeRF 2](https://www.pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/)")
-st.markdown("- [PyImageSearch NeRF 3](https://www.pyimagesearch.com/2021/11/24/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-3/)")
-
 st.markdown("## Credits")  
 st.markdown("- [PyImageSearch](https://www.pyimagesearch.com/)")
 st.markdown("- [JarvisLabs.ai GPU credits](https://jarvislabs.ai/)")