Create app.py

app.py

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+'''NEURAL STYLE TRANSFER'''
+
+
+"""##Importing Libraries"""
+
+
+import gradio as gr
+import tensorflow as tf
+# import os
+import PIL
+from PIL import Image,ImageOps
+import numpy as np
+# import time
+# import requests   
+import cv2
+from cv2 import *
+
+# !mkdir nstmodel 
+# !wget -c https://storage.googleapis.com/tfhub-modules/google/magenta/arbitrary-image-stylization-v1-256/2.tar.gz -O - | tar -xz -C /nstmodel
+# import tensorflow.keras
+
+# from PIL import Image, ImageOps
+#import requests
+#import tarfile
+'''
+url = "https://storage.googleapis.com/tfhub-modules/google/magenta/arbitrary-image-stylization-v1-256/2.tar.gz"
+response = requests.get(url,stream=True)
+path_input="./"
+urllib.request.urlretrieve(url, filename=path_input)
+file = tarfile.open(fileobj=response.raw, mode="r|gz")
+file.extractall(path="./nst_model")
+'''
+MODEL_PATH='Nst model'
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = tf.keras.models.load_model(MODEL_PATH)
+
+def tensor_to_image(tensor):
+  tensor = tensor*255
+  tensor = np.array(tensor, dtype=np.uint8)
+  if np.ndim(tensor)>3:
+    assert tensor.shape[0] == 1
+    tensor = tensor[0]
+  return PIL.Image.fromarray(tensor)
+
+"""##Saving unscaled Tensor images."""
+
+def save_image(image, filename):
+  """
+    Saves unscaled Tensor Images.
+    Args:
+      image: 3D image tensor. [height, width, channels]
+      filename: Name of the file to save to.
+  """
+  if not isinstance(image, Image.Image):
+    image = tf.clip_by_value(image, 0, 255)
+    image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
+  image.save("%s.jpg" % filename)
+  print("Saved as %s.jpg" % filename)
+
+"""## Grayscaling image for testing purpose to check if we could get better results."""
+
+
+
+def gray_scaled(inp_img):
+  gray = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY)
+  gray_img = np.zeros_like(inp_img)
+  gray_img[:,:,0] = gray
+  gray_img[:,:,1] = gray
+  gray_img[:,:,2] = gray
+  return gray_img
+
+def transform_mymodel(content_image,style_image):
+  # Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]
+  content_image=gray_scaled(content_image)
+  content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.0
+  style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.0
+ 
+  #Resizing image
+  style_image = tf.image.resize(style_image, (256, 256))
+
+  # Stylize image
+  outputs = model(tf.constant(content_image), tf.constant(style_image))
+  stylized_image = outputs[0]
+
+  # stylized = tf.image.resize(stylized_image, (356, 356))
+  stylized_image =tensor_to_image(stylized_image)
+  save_image(stylized_image,'stylized')
+  return stylized_image
+
+def gradio_intrface(mymodel):
+# Initializing the input component 
+  image1 = gr.inputs.Image() #CONTENT IMAGE
+  image2 = gr.inputs.Image() #STYLE IMAGE
+  stylizedimg=gr.outputs.Image() 
+  gr.Interface(fn=mymodel, inputs= [image1,image2] , outputs= stylizedimg,title='Style Transfer').launch()
+
+"""The function will be launched both  Inline and Outline  where u need to add a content and style image."""
+
+
+gradio_intrface(transform_mymodel)
+
+
+