app.py

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.")