Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import tensorflow_addons as tfa
+
+import tensorflow as tf
+from tensorflow.keras.utils import custom_object_scope
+
+# Define a function to create the InstanceNormalization layer
+def create_in():
+    return tfa.layers.InstanceNormalization()
+
+
+def model_out(model_path,img):
+  with custom_object_scope({'InstanceNormalization': create_in}):
+    model = tf.keras.models.load_model(model_path)
+  img = (img-127.5)/127.5
+  img = np.expand_dims(img, 0)
+  pred = model.predict(img)
+  pred = np.asarray(pred)
+  return pred[0]
+
+st.title("GrayScale to Colorized Image Pix2Pix")
+day_inp = st.file_uploader("Grayscale image input")
+
+if day_inp is not None:
+  img = cv2.imread(day_inp)
+  img = cv2.resize(img, (256, 256))
+  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+  img = np.array(img)
+  pred = model_out('colorizer.h5', img)
+  st.image(img, caption="Uploaded Image")
+  st.image(((pred + 1) * 127.5).astype(np.uint8), caption="Generated Colorized Painting")