app.py

import os
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from PIL import Image
import gradio as gr

# Suppress warnings
warnings.filterwarnings("ignore")

# Clone DIS repo if not exists
if not os.path.exists("DIS"):
    os.system("git clone https://github.com/xuebinqin/DIS")

# Move model files
if not os.path.exists("models.py"):
    os.system("mv DIS/IS-Net/* .")

# Project imports
from data_loader_cache import normalize, im_reader, im_preprocess
from models import *

# Setup device
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Prepare saved models folder
if not os.path.exists("saved_models"):
    os.mkdir("saved_models")
    # NOTE: make sure isnet.pth is available locally
    os.system("mv isnet.pth saved_models/")

# --- Helpers ---
class GOSNormalize(object):
    def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
        self.mean = mean
        self.std = std

    def __call__(self, image):
        return normalize(image, self.mean, self.std)

transform = transforms.Compose([
    GOSNormalize([0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
])

def load_image(im_path, hypar):
    im = im_reader(im_path)
    im, im_shp = im_preprocess(im, hypar["cache_size"])
    im = torch.divide(im, 255.0)
    shape = torch.from_numpy(np.array(im_shp))
    return transform(im).unsqueeze(0), shape.unsqueeze(0)

def build_model(hypar, device):
    net = hypar["model"]
    if hypar["model_digit"] == "half":
        net.half()
        for layer in net.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.float()
    net.to(device)
    if hypar["restore_model"] != "":
        net.load_state_dict(torch.load(
            os.path.join(hypar["model_path"], hypar["restore_model"]),
            map_location=device
        ))
    net.eval()
    return net

def predict(net, inputs_val, shapes_val, hypar, device):
    net.eval()
    if hypar["model_digit"] == "full":
        inputs_val = inputs_val.type(torch.FloatTensor)
    else:
        inputs_val = inputs_val.type(torch.HalfTensor)

    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device)
    ds_val = net(inputs_val_v)[0]
    pred_val = ds_val[0][0, :, :, :]
    pred_val = torch.squeeze(F.interpolate(
        torch.unsqueeze(pred_val, 0),
        (shapes_val[0][0], shapes_val[0][1]),
        mode='bilinear'
    ))
    ma = torch.max(pred_val)
    mi = torch.min(pred_val)
    pred_val = (pred_val - mi) / (ma - mi)
    if device == 'cuda':
        torch.cuda.empty_cache()
    return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)

# --- Prepare model ---
hypar = {
    "model_path": "./saved_models",
    "restore_model": "isnet.pth",
    "interm_sup": False,
    "model_digit": "full",
    "seed": 0,
    "cache_size": [1024, 1024],
    "input_size": [1024, 1024],
    "crop_size": [1024, 1024],
    "model": ISNetDIS()
}

net = build_model(hypar, device)

# --- Inference ---
def inference(image):
    image_path = image
    image_tensor, orig_size = load_image(image_path, hypar)
    mask = predict(net, image_tensor, orig_size, hypar, device)
    pil_mask = Image.fromarray(mask).convert('L')

    im_rgb = Image.open(image_path).convert("RGB")
    im_rgba = im_rgb.copy()
    im_rgba.putalpha(pil_mask)

    return [im_rgba, pil_mask]

# --- Custom CSS to hide footer ---
css_hide_footer = """
footer {display: none !important;}
#component-12 {display: none !important;}
#huggingface-space-header {display: none !important;}
button[data-testid="ShareButton"] {display: none !important;}
"""

# --- Gradio Interface ---
interface = gr.Interface(
    fn=inference,
    inputs=gr.Image(type='filepath', height=300, width=300),
    outputs=[
        gr.Image(type='filepath', format="png"),
        gr.Image(type='filepath', format="png", visible=False)
    ],
    flagging_mode="never",
    cache_mode="lazy",
    css=css_hide_footer   # ✅ CSS here inside Interface, not launch
)

interface.launch(
    show_error=False,
    show_api=False,
    share=False
)