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import streamlit as st
from huggingface_hub import hf_hub_download
import torch
from PIL import Image
from torchvision import transforms
from skimage.color import rgb2lab, lab2rgb
import numpy as np
import matplotlib.pyplot as plt
from io import BytesIO
# Download the model from Hugging Face Hub
repo_id = "Hammad712/GAN-Colorization-Model"
model_filename = "generator.pt"
model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
# Define the generator model (same architecture as used during training)
from fastai.vision.learner import create_body
from torchvision.models import resnet34
from fastai.vision.models.unet import DynamicUnet
def build_generator(n_input=1, n_output=2, size=256):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
backbone = create_body(resnet34(), pretrained=True, n_in=n_input, cut=-2)
G_net = DynamicUnet(backbone, n_output, (size, size)).to(device)
return G_net
# Initialize and load the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
G_net = build_generator(n_input=1, n_output=2, size=256)
G_net.load_state_dict(torch.load(model_path, map_location=device))
G_net.eval()
# Preprocessing function
def preprocess_image(img_path):
img = Image.open(img_path).convert("RGB")
img = transforms.Resize((256, 256), Image.BICUBIC)(img)
img = np.array(img)
img_to_lab = rgb2lab(img).astype("float32")
img_to_lab = transforms.ToTensor()(img_to_lab)
L = img_to_lab[[0], ...] / 50. - 1.
return L.unsqueeze(0).to(device)
# Inference function
def colorize_image(img_path, model):
L = preprocess_image(img_path)
with torch.no_grad():
ab = model(L)
L = (L + 1.) * 50.
ab = ab * 110.
Lab = torch.cat([L, ab], dim=1).permute(0, 2, 3, 1).cpu().numpy()
rgb_imgs = []
for img in Lab:
img_rgb = lab2rgb(img)
rgb_imgs.append(img_rgb)
return np.stack(rgb_imgs, axis=0)
# Custom CSS
def set_css(style):
st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)
# Combined dark mode styles
combined_css = """
.main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
.block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); }
.stButton>button, .stDownloadButton>button { background: linear-gradient(135deg, #ff7e5f, #feb47b); color: white; border: none; padding: 10px 24px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; margin: 4px 2px; cursor: pointer; border-radius: 5px; }
.stSpinner { color: #4CAF50; }
.title {
font-size: 3rem;
font-weight: bold;
display: flex;
align-items: center;
justify-content: center;
}
.colorful-text {
background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.black-white-text {
color: black;
}
.small-input .stTextInput>div>input {
height: 2rem;
font-size: 0.9rem;
}
.small-file-uploader .stFileUploader>div>div {
height: 2rem;
font-size: 0.9rem;
}
.custom-text {
font-size: 1.2rem;
color: #feb47b;
text-align: center;
margin-top: -20px;
margin-bottom: 20px;
}
"""
# Streamlit application
st.set_page_config(layout="wide")
st.markdown(f"<style>{combined_css}</style>", unsafe_allow_html=True)
st.markdown('<div class="title"><span class="colorful-text">Image</span> <span class="black-white-text">Colorization</span></div>', unsafe_allow_html=True)
st.markdown('<div class="custom-text">Convert black and white images to color using AI</div>', unsafe_allow_html=True)
# Input for image URL or file upload
with st.expander("Input Options", expanded=True):
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png", "webp"], key="upload_file", help="Upload an image file to convert")
# Run inference button
if st.button("Colorize"):
if uploaded_file is not None:
with st.spinner('Processing...'):
try:
colorized_images = colorize_image(uploaded_file, G_net)
colorized_image = colorized_images[0]
# Display original and colorized images side by side
st.markdown("### Result")
col1, col2 = st.columns(2)
with col1:
st.image(uploaded_file, caption='Original Image', use_column_width=True)
with col2:
st.image(colorized_image, caption='Colorized Image', use_column_width=True)
# Provide a download button for the colorized image
img_byte_arr = BytesIO()
Image.fromarray((colorized_image * 255).astype(np.uint8)).save(img_byte_arr, format='JPEG')
img_byte_arr = img_byte_arr.getvalue()
st.download_button(
label="Download Colorized Image",
data=img_byte_arr,
file_name="colorized_image.jpg",
mime="image/jpeg"
)
st.success("Image processed successfully!")
except Exception as e:
st.error(f"An error occurred: {e}")
logging.error("Error during inference", exc_info=True)
else:
st.error("Please upload an image file.")
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