app.py

#importing the libraries
import os, sys, re
import streamlit as st
from PIL import Image
import cv2
import numpy as np
import uuid

# Import torch libraries
import fastai
import torch

# Import util functions from app_utils
from app_utils import download
from app_utils import generate_random_filename
from app_utils import clean_me
from app_utils import clean_all
from app_utils import create_directory
from app_utils import get_model_bin
from app_utils import convertToJPG

# Import util functions from deoldify
# NOTE:  This must be the first call in order to work properly!
from deoldify import device
from deoldify.device_id import DeviceId
#choices:  CPU, GPU0...GPU7
device.set(device=DeviceId.CPU)
from deoldify.visualize import *


####### INPUT PARAMS ###########
model_folder = 'models/'
max_img_size = 800
################################

@st.cache(allow_output_mutation=True)
def load_model(model_dir, option):
    if option.lower() == 'artistic':
        model_url = 'https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth'
        get_model_bin(model_url, os.path.join(model_dir, "ColorizeArtistic_gen.pth"))
        colorizer = get_image_colorizer(artistic=True)
    elif option.lower() == 'stable':
        model_url = "https://www.dropbox.com/s/usf7uifrctqw9rl/ColorizeStable_gen.pth?dl=0"
        get_model_bin(model_url, os.path.join(model_dir, "ColorizeStable_gen.pth"))
        colorizer = get_image_colorizer(artistic=False)

    return colorizer

def resize_img(input_img, max_size):
    img = input_img.copy()
    img_height, img_width = img.shape[0],img.shape[1]

    if max(img_height, img_width) > max_size:
        if img_height > img_width:
            new_width = img_width*(max_size/img_height)
            new_height = max_size
            resized_img = cv2.resize(img,(int(new_width), int(new_height)))
            return resized_img

        elif img_height <= img_width:
            new_width = img_height*(max_size/img_width)
            new_height = max_size
            resized_img = cv2.resize(img,(int(new_width), int(new_height)))
            return resized_img

    return img

def get_image_download_link(img,filename,text):
    button_uuid = str(uuid.uuid4()).replace('-', '')
    button_id = re.sub('\d+', '', button_uuid)
    
    custom_css = f""" 
        <style>
            #{button_id} {{
                background-color: rgb(255, 255, 255);
                color: rgb(38, 39, 48);
                padding: 0.25em 0.38em;
                position: relative;
                text-decoration: none;
                border-radius: 4px;
                border-width: 1px;
                border-style: solid;
                border-color: rgb(230, 234, 241);
                border-image: initial;

            }} 
            #{button_id}:hover {{
                border-color: rgb(246, 51, 102);
                color: rgb(246, 51, 102);
            }}
            #{button_id}:active {{
                box-shadow: none;
                background-color: rgb(246, 51, 102);
                color: white;
                }}
        </style> """

    buffered = BytesIO()
    img.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    href =  custom_css + f'<a href="data:file/txt;base64,{img_str}" id="{button_id}" download="{filename}">{text}</a>'
    return href


# General configuration
st.set_page_config(layout="centered")
st.set_option('deprecation.showfileUploaderEncoding', False)
st.markdown('''
<style>
    .uploadedFile {display: none}
<style>''',
unsafe_allow_html=True)

# Main window configuration
st.title("Black and white colorizer")
st.markdown("This app puts color into your black and white pictures")
title_message = st.empty()

title_message.markdown("**Model loading, please wait** ⌛")

# # Sidebar
color_option = st.sidebar.selectbox('Select colorizer mode',
                                    ('Artistic', 'Stable'))
                                    
# st.sidebar.title('Model parameters')
# det_conf_thres = st.sidebar.slider("Detector confidence threshold", 0.1, 0.9, value=0.5, step=0.1)
# det_nms_thres = st.sidebar.slider("Non-maximum supression IoU", 0.1, 0.9, value=0.4, step=0.1)

# Load models
colorizer = load_model(model_folder, color_option)

title_message.markdown("**To begin, please upload an image** 👇")

#Choose your own image
uploaded_file = st.file_uploader("Upload a black and white photo", type=['png', 'jpg', 'jpeg'])

# show = st.image(use_column_width='auto')
input_img_pos = st.empty()
output_img_pos = st.empty()

if uploaded_file is not None:
    img_name = uploaded_file.name

    pil_img = Image.open(uploaded_file)
    img_rgb = np.array(pil_img)

    resized_img_rgb = resize_img(img_rgb, max_img_size)
    resized_pil_img = Image.fromarray(resized_img_rgb)

    title_message.markdown("**Processing your image, please wait** ⌛")

    output_pil_img = colorizer.plot_transformed_pil_image(resized_pil_img, render_factor=35, compare=False)
    
    title_message.markdown("**To begin, please upload an image** 👇")

    # Plot images
    input_img_pos.image(resized_pil_img, 'Input image', use_column_width=True)
    output_img_pos.image(output_pil_img, 'Output image', use_column_width=True)

    st.markdown(get_image_download_link(output_pil_img, img_name, 'Download '+img_name), unsafe_allow_html=True)