First commit

.gitignore

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+# Virtual environment
+venv
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Cache and similar
+catboost_info
+learning_rate_0.05
+learning_rate_0.5
+default
+catboost_model.bin
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# DotEnv configuration
+.env
+
+# Database
+*.db
+*.rdb
+
+# Pycharm
+.idea
+
+# VS Code
+.vscode/
+
+# Spyder
+.spyproject/
+
+# Jupyter NB Checkpoints
+.ipynb_checkpoints/
+
+# exclude data from source control by default
+/data/
+
+# Mac OS-specific storage files
+.DS_Store
+
+# vim
+*.swp
+*.swo
+
+# Mypy cache
+.mypy_cache/
+

.slugignore

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+docs
+references
+README.md

Procfile

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+web: sh setup.sh && streamlit run app.py

README.md

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+# Setup and Installation
+## Install Requirements
+```shell
+pip install -r requirements.txt
+```
+### Local Package Install
+```shell
+pip install -e .
+```
+### Run app locally
+```shell
+streamlit run app.py
+```

app.py

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+# IMPORT LIBRARIES & FUNCTIONS
+# External Libraries
+import streamlit as st
+
+# Project Functions
+# - Model
+from src.model.model import load_model, stylize_content_image
+from src.model.utils import use_low_resource_settings, suppress_warnings
+
+# - Data
+from src.data.data import upload_image
+from src.data.utils import remove_source_images
+
+use_low_resource_settings("Yes")
+
+suppress_warnings("Yes")
+
+st.write("# 🖼️Neural🎨Style🖌️Transfer🖼️")
+
+# LOAD MODEL
+with st.spinner("🖌️Loading Model"):
+    model = load_model()
+st.success("🖌️Model Loaded")
+
+content_image_def, style_image_def = st.columns(2)
+with content_image_def:
+    st.write("🖼️ Content Image: Image to style")
+with style_image_def:
+    st.write("🎨 Style Image: Style to transfer to content image")
+
+content_image, style_image = st.columns(2)
+
+# UPLOAD IMAGES
+with content_image:
+    ContentColumnTitle = "## 🖼️ Content Image 🖼️"
+    ContentImageSelectionPrompt = "Pick a Content image"
+    content_image_file = upload_image(
+        ContentColumnTitle, ContentImageSelectionPrompt, "Content", "content"
+    )
+
+with style_image:
+    StyleColumnTitle = "## 🎨 Style Image 🎨"
+    StyleImageSelectionPrompt = "Pick a Style image"
+    style_image_file = upload_image(
+        StyleColumnTitle, StyleImageSelectionPrompt, "Style", "style"
+    )
+
+# STYLIZE CONTENT IMAGE
+stylize_image = st.button("🖼️🖌️🎨 Start Neural Style Transfer 🖼️🖌️🎨")
+
+if stylize_image:
+    final_image = stylize_content_image(model, content_image_file, style_image_file)
+    st.write("# Styled Image:")
+    st.image(final_image)
+    try:
+        remove_source_images()
+    except:
+        pass

references/References.md

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+# References & Inspiration
+## Project Structure
+[Cookiecutter Data Science](https://drivendata.github.io/cookiecutter-data-science/)
+
+## Model
+[Fast arbitrary image style transfer.](https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2)
+
+## Repositories
+[kairavkkp/Neural-Style-Transfer-Streamlit](https://github.com/kairavkkp/Neural-Style-Transfer-Streamlit)

requirements.txt

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+streamlit>=1.7.0
+tensorflow-hub==0.12.0
+tensorflow-cpu ==2.5.0

setup.py

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+from setuptools import find_packages, setup
+
+setup(
+    name='src',
+    packages=find_packages(),
+    version='0.1.0',
+    description='Neutral Style Transfer Tool',
+    author='Author',
+    license='MIT',
+)

setup.sh

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+#!/bin/bash
+
+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+headless = true\n\
+port = $PORT\n\
+enableCORS = false\n\
+\n\
+" > ~/.streamlit/config.toml

src/data/data.py

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+import os
+import streamlit as st
+import tensorflow as tf
+
+from ..image_utils import load_img, imshow, transform_img
+
+
+def upload_image_file(ImageSelectionPrompt, ImageType, image_upload_method):
+    st.write(f"{ImageSelectionPrompt}: {image_upload_method}")
+    image_file = st.file_uploader(
+        f"Upload {ImageType} Image File (png or jpg)", type=("png", "jpg")
+    )
+    try:
+        image_file = image_file.read()
+        return transform_img(image_file)
+    except:
+        pass
+
+
+def upload_image_url(ImageSelectionPrompt, ImageType, image_upload_method):
+    st.write(f"{ImageSelectionPrompt}: {image_upload_method}")
+    url = st.text_input(f"{ImageType} Image URL")
+    try:
+        image_path = tf.keras.utils.get_file(
+            os.path.join(os.getcwd(), f"{ImageType.lower()}.jpg"), url
+        )
+    except:
+        pass
+    try:
+        return load_img(image_path)
+    except:
+        pass
+
+
+def upload_image(ColumnTitle, ImageSelectionPrompt, ImageType, KeyString):
+    st.write(ColumnTitle)
+    image_upload_method = st.radio(
+        label="", options=["📁 File Upload", "🔗 URL"], key=KeyString
+    )
+    if image_upload_method == "📁 File Upload":
+        image_file = upload_image_file(
+            ImageSelectionPrompt, ImageType, image_upload_method
+        )
+    if image_upload_method == "🔗 URL":
+        image_file = upload_image_url(
+            ImageSelectionPrompt, ImageType, image_upload_method
+        )
+    try:
+        st.write(f"{ImageType} Image")
+        st.image(imshow(image_file))
+        return image_file
+    except:
+        pass

src/data/utils.py

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+import os
+
+
+def remove_source_images():
+    os.remove('style.jpg')
+    os.remove('content.jpg')

src/image_utils.py

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+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+
+def load_img(path_to_img):
+    max_dim = 512
+    img = tf.io.read_file(path_to_img)
+    img = tf.image.decode_image(img)
+    img = tf.image.convert_image_dtype(img, tf.float32)
+
+    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
+    long_dim = max(shape)
+    scale = max_dim / long_dim
+
+    new_shape = tf.cast(shape * scale, tf.int32)
+
+    img = tf.image.resize(img, new_shape)
+    img = img[tf.newaxis, :]
+    return img
+
+
+def transform_img(img):
+    max_dim = 512
+    img = tf.image.decode_image(img)
+    img = tf.image.convert_image_dtype(img, tf.float32)
+
+    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
+    long_dim = max(shape)
+    scale = max_dim / long_dim
+
+    new_shape = tf.cast(shape * scale, tf.int32)
+
+    img = tf.image.resize(img, new_shape)
+    img = img[tf.newaxis, :]
+    return img
+
+
+def imshow(image, title=None):
+    if len(image.shape) > 3:
+        image = tf.squeeze(image, axis=0)
+
+    image = np.squeeze(image)
+    return image
+
+
+def tensor_to_image(tensor):
+    tensor = tensor * 255
+    tensor = np.array(tensor, np.uint8)
+    if np.ndim(tensor) > 3:
+        assert tensor.shape[0] == 1
+        tensor = tensor[0]
+    return Image.fromarray(tensor)

src/model/model.py

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+import streamlit as st
+import tensorflow as tf
+import tensorflow_hub as hub
+
+from ..image_utils import tensor_to_image
+
+
+@st.cache
+def load_model():
+    hub_handle = 'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2'
+    # return hub_model
+    return hub.load(hub_handle)
+    
+
+
+def stylize_content_image(model, content_image_file, style_image_file):
+    try:
+        stylized_image = model(tf.constant(
+            content_image_file), tf.constant(style_image_file))[0]
+        return tensor_to_image(stylized_image)  # stylized image
+    except:
+        stylized_image = model(tf.constant(
+            tf.convert_to_tensor(content_image_file[:, :, :, :3])
+        ),
+            tf.constant(
+                tf.convert_to_tensor(style_image_file[:, :, :, :3])
+        )
+        )[0]
+        return tensor_to_image(stylized_image)  # stylized image

src/model/utils.py

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+import os
+
+
+def use_low_resource_settings(option):
+    if option == 'Yes':
+        os.environ['CUDA_VISIBLE_DEVICES'] = ''
+        os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
+
+
+def suppress_warnings(option):
+    if option == 'Yes':
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'