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
import requests
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):
    img = img.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, model):
    L = preprocess_image(img)
    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="black-white-text">Image</span> <span class="colorful-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")
    url_input = st.text_input("Or enter an image URL", key="url_input", help="Enter the URL of an image to convert")

# Run inference button
if st.button("Colorize"):
    img = None

    if uploaded_file is not None:
        img = Image.open(uploaded_file)
    elif url_input:
        try:
            response = requests.get(url_input)
            img = Image.open(BytesIO(response.content))
        except Exception as e:
            st.error(f"Error fetching the image from URL: {e}")

    if img is not None:
        with st.spinner('Processing...'):
            try:
                colorized_images = colorize_image(img, 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(img, 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}")
    else:
        st.error("Please upload an image file or provide a valid URL.")