Upload initial files

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from neural_style_transfer import generate_img
+
+def display_img(org_img, style_img):
+    img = generate_img(org_img, style_img)
+    st.image(img, caption='Result after Neural Style Transfer', use_column_width=True)
+
+def main():
+    markdown = ""
+    with open('README.md', 'r') as f:
+        markdown = f.read()
+    st.markdown(markdown, unsafe_allow_html=True)    
+    with st.container(border=True):
+        col1, col2 = st.columns(2)
+        with col1:
+            st.header('Original Image')
+            org_img = st.file_uploader(label='Upload original image', type=['png', 'jpg', 'jpeg'])
+        with col2:
+            st.header('Style Image')
+            style_img = st.file_uploader(label='Upload style image', type=['png', 'jpg', 'jpeg'])
+
+        if org_img is not None and style_img is not None:
+            if st.button("Submit"):
+                col1, col2, col3 = st.columns([1,2.5,1])
+                with col2:
+                    display_img(org_img.read(), style_img.read())
+
+
+if __name__ == '__main__':
+    main()

neural_style_transfer/__init__.py

@@ -0,0 +1,21 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2 as cv
+import tensorflow as tf
+import tensorflow_hub as hub
+
+model = hub.load('https://www.kaggle.com/models/google/arbitrary-image-stylization-v1/tensorFlow1/256/2')
+
+def load_image(img_bytes):
+    img = tf.image.decode_image(img_bytes, channels=3)
+    img = tf.image.resize(img, [256, 256])
+    img = tf.image.convert_image_dtype(img, tf.float32)
+    img = tf.expand_dims(img/255.0, axis=0)
+    return img
+
+def generate_img(org_img, style_img):
+    org_img = load_image(org_img)
+    style_img = load_image(style_img)
+    gen_img = model(tf.constant(org_img), tf.constant(style_img))[0]
+    img_arr = (np.array(gen_img)*255).astype(np.uint8)[0]
+    return img_arr

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+matplotlib
+opencv-python
+tensorflow
+tensorflow_hub