Create app.py

app.py

@@ -0,0 +1,49 @@
+
+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("Night to Day painting cyclegan")
+day_inp = st.file_uploader("Night-time image input")
+
+if day_inp is not None:
+  img = Image.open(day_inp)
+  img = img.resize((256, 256))
+  img = np.array(img)
+  pred = model_out('nighttoday2.h5', img)
+  st.image(img, caption="Uploaded Image")
+  st.image(((pred + 1) * 127.5).astype(np.uint8), caption="Generated Day-time Painting")
+
+
+st.header('Which architecture did I use architecture, Resnet-Blocks or Unet architecture?')
+st.write('I have tried both Resnet and unet architecture')
+st.write('But when using the Unet architecture, it produce more clear and understandable images')
+st.write('I use the pix2pix generator from tensorflow examples module and same for the discriminator')
+st.header('What datasets did you use to train your CycleGAN model?')
+st.write('For the dataset, I used Unpaired Day to Night dataset available on kaggle')
+st.header('What hardware I trained it on?')
+st.write('I trained the model on Kaggle notebook on P100 gpu with 13 gigs of ram cuz my pc wouldnt be in a good state if I trained the cyclegan model on Intel HD')
+st.header('How much time did it take')
+st.write('It took aboul 70 epochs each of 20 seconds, DO THE MATH')
+st.header('Why did I make this model?')
+st.subheader('I made this model to extend my experience but mostly for FUNN!!!!')
+st.write("-------------------------------------------------")