app.py

import os
import gradio as gr
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
from PIL import Image
import timm
import io
import sys

# --- 1. 初始化模型 ---
model_id = "briaai/RMBG-2.0"
print(f"正在載入模型: {model_id} ...")

hf_token = os.getenv("HF_TOKEN")

if not hf_token:
    print("⚠️ 警告: 未偵測到 HF_TOKEN，如果是 Gated Model 可能會失敗")

try:
    model = AutoModelForImageSegmentation.from_pretrained(
        model_id, 
        trust_remote_code=True, 
        token=hf_token
    )
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()
    print(f"✅ 模型載入成功！使用裝置: {device}")
except Exception as e:
    print(f"❌ 模型載入失敗: {e}")

# --- 2. 圖像處理 ---
def process_image(input_image):
    if input_image is None:
        return None
    
    # 處理邏輯
    image_size = (1024, 1024)
    transform_image = transforms.Compose([
        transforms.Resize(image_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    
    input_images = transform_image(input_image).unsqueeze(0).to(device)
    
    with torch.no_grad():
        preds = model(input_images)[-1].sigmoid().cpu()
    
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(input_image.size)
    
    image = input_image.convert("RGBA")
    image.putalpha(mask)
    return image

# --- 3. 介面 ---
with gr.Blocks(title="RMBG-2.0 AI") as app:
    gr.Markdown("## ✂️ 去背服務測試")
    
    with gr.Row():
        input_img = gr.Image(type="pil", label="Input")
        output_img = gr.Image(type="pil", label="Output", format="png") # 確保輸出 PNG 才有透明度

    btn = gr.Button("Remove Background")
    btn.click(fn=process_image, inputs=input_img, outputs=output_img)

# --- 4. 啟動 ---
if __name__ == "__main__":
    # 新版 Gradio 預設 API 開放 CORS，不需要 cors_allowed_origins
    app.launch(server_name="0.0.0.0", server_port=7860)