Update apptest.py

apptest.py

@@ -6,42 +6,51 @@ from torchvision import transforms
 from PIL import Image
 import timm
 
+# --- 🔍 版本檢查區 (請看這裡) ---
+import sys
+print("="*30)
+print(f"Python version: {sys.version}")
+print(f"Gradio version: {gr.__version__}")
+print(f"Torch version: {torch.__version__}")
+print(f"Timm version: {timm.__version__}")
+print("="*30)
+# -----------------------------
+
 # --- 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")
 
 try:
     model = AutoModelForImageSegmentation.from_pretrained(
-        model_id, trust_remote_code=True, token=hf_token
+        model_id, 
+        trust_remote_code=True, 
+        token=hf_token
     )
-    device = torch.device("cpu")
+    device = torch.device("cpu") 
     model.to(device)
     model.eval()
     print("✅ 模型載入成功！")
 except Exception as e:
     print(f"❌ 模型載入失敗: {e}")
 
-# --- 2. 圖像處理 ---
+# --- 2. 圖像處理 (官方邏輯) ---
 def process_image(input_image):
     if input_image is None:
         return None
-    
     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)
@@ -49,78 +58,21 @@ def process_image(input_image):
     image.putalpha(mask)
     return image
 
-# --- 3. CSS 樣式 (定義成字串，等一下用 HTML 塞進去) ---
-my_style = """
-<style>
-    /* 全域字體與背景 */
-    .gradio-container {
-        font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
-        background-color: #f0f2f5 !important;
-    }
-    
-    /* 隱藏 Footer */
-    footer {display: none !important;}
-    
-    /* 標題美化 */
-    h1 {
-        text-align: center;
-        color: #333;
-        margin-bottom: 20px;
-    }
-
-    /* 按鈕美化 */
-    button {
-        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-        color: white !important;
-        border: none !important;
-        border-radius: 12px !important;
-        font-weight: bold !important;
-        padding: 10px 20px !important;
-        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-    }
-    button:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 6px 12px rgba(0,0,0,0.15);
-    }
+# --- 3. 介面 ---
+# 為了驗證，我們也在網頁上顯示版本
+version_info = f"目前運行版本 - Gradio: {gr.__version__} | Torch: {torch.__version__}"
 
-    /* 圖片容器美化 */
-    .image-container {
-        border-radius: 15px;
-        overflow: hidden;
-        border: 2px solid #fff;
-        box-shadow: 0 4px 6px rgba(0,0,0,0.05);
-        background-color: white;
-    }
-</style>
-"""
-
-# --- 4. 建立介面 ---
-# 重點：這裡不放 css 參數，避開錯誤
-with gr.Blocks() as app:
+with gr.Blocks(title="版本檢查") as app:
     
-    # 【暴力美學】直接用 HTML 把 CSS 和 PWA 設定「印」在網頁裡
-    # 這樣不管 Gradio 哪個版本，瀏覽器看到了就會執行
-    gr.HTML(my_style)
-    gr.HTML("""
-    <div style="display: none;">
-        <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
-        <meta name="apple-mobile-web-app-capable" content="yes">
-        <meta name="theme-color" content="#f0f2f5">
-    </div>
-    """)
-
-    with gr.Column():
-        gr.Markdown("# ✂️ AI 智能去背")
-        
-        with gr.Row():
-            # 左邊：輸入
-            with gr.Column():
-                input_img = gr.Image(type="pil", label="上傳圖片", elem_classes="image-container")
-                btn = gr.Button("🚀 開始去背")
-            
-            # 右邊：輸出
-            with gr.Column():
-                output_img = gr.Image(type="pil", label="去背結果", elem_classes="image-container")
+    gr.Markdown(f"## ✂️ AI 自動去背")
+    gr.Markdown(f"ℹ️ **{version_info}**") # 這裡會直接顯示在網頁上
+    
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="pil", label="上傳圖片")
+            btn = gr.Button("開始去背")
+        with gr.Column():
+            output_img = gr.Image(type="pil", label="去背結果")
 
     btn.click(fn=process_image, inputs=input_img, outputs=output_img)