Spaces:
Running
Running
| # App code based on: | |
| # Model based on: | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import os | |
| from datetime import datetime | |
| from PIL import Image | |
| from streamlit_drawable_canvas import st_canvas | |
| from io import BytesIO | |
| from copy import deepcopy | |
| from src.core import process_inpaint | |
| st.title("AI Photo Colorization") | |
| st.image(open("assets/demo.png", "rb").read()) | |
| st.markdown( | |
| """ | |
| Colorizing black & white photo can be expensive and time consuming. We introduce AI that can colorize | |
| grayscale photo in seconds. **Just upload your grayscale image, then click colorize.** | |
| """ | |
| ) | |
| uploaded_file = st.file_uploader("Choose image", accept_multiple_files=False, type=["png", "jpg", "jpeg"]) | |
| if uploaded_file is not None: | |
| bytes_data = uploaded_file.getvalue() | |
| img_input = Image.open(BytesIO(bytes_data)).convert("RGBA") | |
| if uploaded_file is not None and st.button("Colorize!"): | |
| with st.spinner("AI is doing the magic!"): | |
| img_output = """TODO""" | |
| # NOTE: Calm! I'm not logging the input and outputs. | |
| # It is impossible to access the filesystem in spaces environment. | |
| now = datetime.now().strftime("%Y%m%d-%H%M%S-%f") | |
| img_input.convert("RGB").save(f"./output/{now}.jpg") | |
| Image.fromarray(img_output).convert("RGB").save(f"./output/{now}-edited.jpg") | |
| st.write("AI has finished the job!") | |
| st.image(img_output) | |
| # reuse = st.button('Edit again (Re-use this image)', on_click=set_image, args=(inpainted_img, )) | |
| with open(f"./output/{now}-edited.jpg", "rb") as fs: | |
| uploaded_name = os.path.splitext(uploaded_file.name)[0] | |
| st.download_button( | |
| label="Download", | |
| data=fs, | |
| file_name=f'edited_{uploaded_name}.jpg', | |
| ) | |
| st.info("**TIP**: If the result is not perfect, you can download then " | |
| "re-upload the result then remove the artifacts.") | |