Restore complete app.py with ultra-minimal Gradio config

- Add back full application code that was accidentally wiped
- Use minimal gr.Interface with string shortcuts to avoid schema bugs
- Single text output to prevent complex component issues
- Should finally resolve the persistent TypeError

app.py

@@ -1 +1,77 @@
- 
+import os
+import cv2
+import numpy as np
+import gradio as gr
+
+# 檢查檔案是否存在
+model_path = "tsflow/models/best_model.pt"
+config_path = "tsflow/results/test_results.json"
+
+# 如果是在SignView2.0目錄下運行，調整路徑
+if not os.path.exists(model_path):
+    model_path = "../tsflow/models/best_model.pt"
+    config_path = "../tsflow/results/test_results.json"
+
+try:
+    from realtime_sign_prediction import RealtimeSignPredictor
+    
+    # 初始化預測器
+    print("🚀 正在初始化手語辨識系統...")
+    predictor = RealtimeSignPredictor(
+        model_path=model_path,
+        config_path=config_path,
+        sequence_length=50,
+        use_segmentation=True
+    )
+    print("✅ 手語辨識系統初始化完成！")
+    MODEL_LOADED = True
+    
+except Exception as e:
+    print(f"⚠️ 模型載入失敗: {e}")
+    print("🔄 使用模擬模式運行...")
+    MODEL_LOADED = False
+
+def predict_sign_language(image):
+    """簡化的預測函數，返回單一字串結果"""
+    if image is None:
+        return "請上傳影像"
+    
+    if not MODEL_LOADED:
+        return "⚠️ 模型未載入，無法進行預測"
+    
+    try:
+        # 處理畫面
+        results, keypoints, flow_features = predictor.process_frame(image)
+        
+        # 獲取預測結果
+        top_predictions = predictor.get_top_predictions(top_k=3)
+        
+        # 格式化預測結果為簡單字串
+        if top_predictions:
+            result_text = "🎯 手語辨識結果:\n\n"
+            for i, (label, confidence) in enumerate(top_predictions, 1):
+                result_text += f"{i}. {label}: {confidence:.2%}\n"
+            
+            result_text += f"\n📊 序列進度: {len(predictor.keypoint_sequence)}/{predictor.sequence_length}"
+        else:
+            result_text = "📡 正在收集動作序列...\n請確保手語動作清晰可見"
+        
+        return result_text
+        
+    except Exception as e:
+        return f"❌ 處理錯誤: {str(e)}"
+
+# 使用最基本的Interface配置，完全避免複雜參數
+demo = gr.Interface(
+    fn=predict_sign_language,
+    inputs="image",  # 使用字串而不是元件物件
+    outputs="text",   # 使用字串而不是元件物件
+    title="🤟 SignView2.0 - 手語辨識系統",
+    description="支援34種手語詞彙的即時辨識系統，準確率達94.25%\n\n上傳影像即可開始辨識手語動作"
+)
+
+if __name__ == "__main__":
+    print("🎉 SignView2.0 手語辨識系統已啟動！")
+    
+    # 使用最安全的launch配置
+    demo.launch()