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import streamlit as st
from PIL import Image
import torch
from model import ModelColorization
from utils import process_gs_image, inverse_transform_cs
# Custom CSS for styling
st.markdown(
"""
<style>
.main {
background-color: #f9f9f9;
}
.title {
color: #ffffff;
font-size: 2.5em;
text-align: center;
margin-bottom: 0.5em;
}
.subheader {
color: #5a5a5a;
font-size: 1.1em;
text-align: center;
margin-bottom: 2em;
}
.upload-box {
background-color: #ffffff;
border-radius: 10px;
padding: 2em;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
margin-bottom: 2em;
}
.result-box {
background-color: #ffffff;
border-radius: 10px;
padding: 2em;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
margin-top: 2em;
}
.stButton>button {
background-color: #4CAF50;
color: white;
border-radius: 5px;
padding: 0.5em 1em;
font-size: 1em;
width: 100%;
}
.stButton>button:hover {
background-color: #45a049;
}
.group-banner {
text-align: center;
font-size: 1.5em;
color: #ffffff;
font-weight: bold;
margin-top: 2em;
}
</style>
""",
unsafe_allow_html=True
)
# Load model
model = ModelColorization().from_pretrained("sebastiansarasti/AutoEncoderImageColorization")
# App header
st.markdown('<p class="title">🎨 Neural Image Colorizer</p>', unsafe_allow_html=True)
st.markdown('<p class="subheader">Bring black & white photos to life with AI</p>', unsafe_allow_html=True)
# Upload section
with st.container():
st.markdown("### 📤 Upload Your Image")
uploaded_file = st.file_uploader(
"Choose a black & white photo...",
type=["jpg", "jpeg", "png"],
label_visibility="collapsed"
)
# Processing section
if uploaded_file is not None:
with st.container():
col1, col2 = st.columns(2)
with col1:
st.markdown("### ⬆ Original")
original_img = Image.open(uploaded_file)
st.image(original_img, use_container_width=True)
with col2:
st.markdown("### 🎨 Colorized")
if st.button("✨ Colorize Image", type="primary"):
with st.spinner("Colorizing your image..."):
# Process image
image, original_size = process_gs_image(original_img)
# Run model
model.eval()
with torch.no_grad():
result = model(image)
# Get colorized image
colorized_image = inverse_transform_cs(result.squeeze(0), original_size)
# Display result
st.image(colorized_image, use_container_width=True)
st.success("Colorization complete!")
# Footer
st.markdown(
'<p class="group-banner">Developed with by Group 9 | Computer Vision Project</p>',
unsafe_allow_html=True
) |