diff --git a/app/build.gradle.kts b/app/build.gradle.kts
index 699e52e..8494af8 100644
--- a/app/build.gradle.kts
+++ b/app/build.gradle.kts
@@ -36,5 +36,5 @@ dependencies {
     implementation("com.google.mlkit:face-detection:16.1.7")
     implementation("org.jetbrains.kotlinx:kotlinx-coroutines-android:1.9.0")
     implementation("org.jetbrains.kotlinx:kotlinx-coroutines-play-services:1.9.0")
-    implementation("org.tensorflow:tensorflow-lite:2.16.1")
+    implementation("com.google.ai.edge.litert:litert:2.0.3")
 }
diff --git a/app/src/main/assets/facenet_512.tflite b/app/src/main/assets/facenet_512.tflite
deleted file mode 100644
index 8e48044..0000000
Binary files a/app/src/main/assets/facenet_512.tflite and /dev/null differ
diff --git a/app/src/main/assets/facenet_512_int_quantized.tflite b/app/src/main/assets/facenet_512_int_quantized.tflite
deleted file mode 100644
index d933171..0000000
Binary files a/app/src/main/assets/facenet_512_int_quantized.tflite and /dev/null differ
diff --git a/app/src/main/assets/facenet_int_quantized.tflite b/app/src/main/assets/facenet_int_quantized.tflite
deleted file mode 100644
index efa25d5..0000000
Binary files a/app/src/main/assets/facenet_int_quantized.tflite and /dev/null differ
diff --git a/app/src/main/assets/glint360k_r50.onnx b/app/src/main/assets/glint360k_r50.onnx
deleted file mode 100644
index f521a0d..0000000
Binary files a/app/src/main/assets/glint360k_r50.onnx and /dev/null differ
diff --git a/app/src/main/assets/glint360k_r50_float16.tflite b/app/src/main/assets/glint360k_r50_float16.tflite
deleted file mode 100644
index e27d4f4..0000000
Binary files a/app/src/main/assets/glint360k_r50_float16.tflite and /dev/null differ
diff --git a/app/src/main/assets/glint360k_r50_float32.tflite b/app/src/main/assets/glint360k_r50_float32.tflite
deleted file mode 100644
index 3f77702..0000000
Binary files a/app/src/main/assets/glint360k_r50_float32.tflite and /dev/null differ
diff --git a/app/src/main/assets/glint360k_r50_tensor_correspondence_report.json b/app/src/main/assets/glint360k_r50_tensor_correspondence_report.json
deleted file mode 100644
index 71976b9..0000000
--- a/app/src/main/assets/glint360k_r50_tensor_correspondence_report.json
+++ /dev/null
@@ -1,6555 +0,0 @@
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-        "831",
-        "Conv_117_output_nhwc"
-      ]
-    },
-    {
-      "identifier": 119,
-      "onnx_op_type": "PRelu",
-      "onnx_op_name": "PRelu_118",
-      "onnx_output_index": 0,
-      "onnx_output_name": "668",
-      "resolved_tflite_tensor_name": "Conv_119_input_nhwc",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 145,
-      "producer_op_type": "PRELU",
-      "assume_channel_last_layout": true,
-      "status": "rewritten",
-      "is_rewritten": true,
-      "rewrite_events": [
-        {
-          "event_index": 50,
-          "kind": "rename_tensor",
-          "from": "668",
-          "to": "Conv_119_input_nhwc"
-        }
-      ],
-      "rewrite_name_chain": [
-        "668",
-        "Conv_119_input_nhwc"
-      ]
-    },
-    {
-      "identifier": 120,
-      "onnx_op_type": "Conv",
-      "onnx_op_name": "Conv_119",
-      "onnx_output_index": 0,
-      "onnx_output_name": "834",
-      "resolved_tflite_tensor_name": "Conv_119_output_nhwc",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 146,
-      "producer_op_type": "CONV_2D",
-      "assume_channel_last_layout": true,
-      "status": "rewritten",
-      "is_rewritten": true,
-      "rewrite_events": [
-        {
-          "event_index": 287,
-          "kind": "replace_input",
-          "from": "834",
-          "to": "Conv_119_output_nhwc"
-        }
-      ],
-      "rewrite_name_chain": [
-        "834",
-        "Conv_119_output_nhwc"
-      ]
-    },
-    {
-      "identifier": 121,
-      "onnx_op_type": "Add",
-      "onnx_op_name": "Add_120",
-      "onnx_output_index": 0,
-      "onnx_output_name": "671",
-      "resolved_tflite_tensor_name": "671",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 2,
-      "producer_operator_index": 147,
-      "producer_op_type": "ADD",
-      "assume_channel_last_layout": false,
-      "status": "direct",
-      "is_rewritten": false,
-      "rewrite_events": [],
-      "rewrite_name_chain": [
-        "671"
-      ]
-    },
-    {
-      "identifier": 122,
-      "onnx_op_type": "BatchNormalization",
-      "onnx_op_name": "BatchNormalization_121",
-      "onnx_output_index": 0,
-      "onnx_output_name": "672",
-      "resolved_tflite_tensor_name": "Conv_122_input_nhwc",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 149,
-      "producer_op_type": "ADD",
-      "assume_channel_last_layout": true,
-      "status": "rewritten",
-      "is_rewritten": true,
-      "rewrite_events": [
-        {
-          "event_index": 207,
-          "kind": "rename_tensor",
-          "from": "672",
-          "to": "Conv_122_input_nhwc"
-        }
-      ],
-      "rewrite_name_chain": [
-        "672",
-        "Conv_122_input_nhwc"
-      ]
-    },
-    {
-      "identifier": 123,
-      "onnx_op_type": "Conv",
-      "onnx_op_name": "Conv_122",
-      "onnx_output_index": 0,
-      "onnx_output_name": "837",
-      "resolved_tflite_tensor_name": "Conv_122_output_nhwc",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 150,
-      "producer_op_type": "CONV_2D",
-      "assume_channel_last_layout": true,
-      "status": "rewritten",
-      "is_rewritten": true,
-      "rewrite_events": [
-        {
-          "event_index": 51,
-          "kind": "replace_input",
-          "from": "837",
-          "to": "Conv_122_output_nhwc"
-        }
-      ],
-      "rewrite_name_chain": [
-        "837",
-        "Conv_122_output_nhwc"
-      ]
-    },
-    {
-      "identifier": 124,
-      "onnx_op_type": "PRelu",
-      "onnx_op_name": "PRelu_123",
-      "onnx_output_index": 0,
-      "onnx_output_name": "676",
-      "resolved_tflite_tensor_name": "Conv_124_input_nhwc",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 151,
-      "producer_op_type": "PRELU",
-      "assume_channel_last_layout": true,
-      "status": "rewritten",
-      "is_rewritten": true,
-      "rewrite_events": [
-        {
-          "event_index": 52,
-          "kind": "rename_tensor",
-          "from": "676",
-          "to": "Conv_124_input_nhwc"
-        }
-      ],
-      "rewrite_name_chain": [
-        "676",
-        "Conv_124_input_nhwc"
-      ]
-    },
-    {
-      "identifier": 125,
-      "onnx_op_type": "Conv",
-      "onnx_op_name": "Conv_124",
-      "onnx_output_index": 0,
-      "onnx_output_name": "840",
-      "resolved_tflite_tensor_name": "Conv_124_output_nhwc",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 152,
-      "producer_op_type": "CONV_2D",
-      "assume_channel_last_layout": true,
-      "status": "rewritten",
-      "is_rewritten": true,
-      "rewrite_events": [
-        {
-          "event_index": 289,
-          "kind": "replace_input",
-          "from": "840",
-          "to": "840__to_nhwc"
-        },
-        {
-          "event_index": 298,
-          "kind": "replace_input",
-          "from": "840__to_nhwc",
-          "to": "Conv_124_output_nhwc"
-        }
-      ],
-      "rewrite_name_chain": [
-        "840",
-        "840__to_nhwc",
-        "Conv_124_output_nhwc"
-      ]
-    },
-    {
-      "identifier": 126,
-      "onnx_op_type": "Add",
-      "onnx_op_name": "Add_125",
-      "onnx_output_index": 0,
-      "onnx_output_name": "679",
-      "resolved_tflite_tensor_name": "679",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 153,
-      "producer_op_type": "ADD",
-      "assume_channel_last_layout": false,
-      "status": "direct",
-      "is_rewritten": false,
-      "rewrite_events": [],
-      "rewrite_name_chain": [
-        "679"
-      ]
-    },
-    {
-      "identifier": 127,
-      "onnx_op_type": "BatchNormalization",
-      "onnx_op_name": "BatchNormalization_126",
-      "onnx_output_index": 0,
-      "onnx_output_name": "680",
-      "resolved_tflite_tensor_name": "680",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 156,
-      "producer_op_type": "TRANSPOSE",
-      "assume_channel_last_layout": false,
-      "status": "direct",
-      "is_rewritten": false,
-      "rewrite_events": [
-        {
-          "event_index": 293,
-          "kind": "rename_tensor",
-          "from": "680",
-          "to": "680__to_nhwc"
-        },
-        {
-          "event_index": 295,
-          "kind": "replace_input",
-          "from": "680__to_nhwc",
-          "to": "680"
-        }
-      ],
-      "rewrite_name_chain": [
-        "680",
-        "680__to_nhwc",
-        "680"
-      ]
-    },
-    {
-      "identifier": 128,
-      "onnx_op_type": "Flatten",
-      "onnx_op_name": "Flatten_127",
-      "onnx_output_index": 0,
-      "onnx_output_name": "681",
-      "resolved_tflite_tensor_name": "681",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 157,
-      "producer_op_type": "RESHAPE",
-      "assume_channel_last_layout": false,
-      "status": "direct",
-      "is_rewritten": false,
-      "rewrite_events": [],
-      "rewrite_name_chain": [
-        "681"
-      ]
-    },
-    {
-      "identifier": 129,
-      "onnx_op_type": "Gemm",
-      "onnx_op_name": "Gemm_128",
-      "onnx_output_index": 0,
-      "onnx_output_name": "682",
-      "resolved_tflite_tensor_name": "682",
-      "exists_in_final_model": true,
-      "is_graph_output": false,
-      "consumer_count": 1,
-      "producer_operator_index": 158,
-      "producer_op_type": "FULLY_CONNECTED",
-      "assume_channel_last_layout": false,
-      "status": "direct",
-      "is_rewritten": false,
-      "rewrite_events": [],
-      "rewrite_name_chain": [
-        "682"
-      ]
-    },
-    {
-      "identifier": 130,
-      "onnx_op_type": "BatchNormalization",
-      "onnx_op_name": "BatchNormalization_129",
-      "onnx_output_index": 0,
-      "onnx_output_name": "683",
-      "resolved_tflite_tensor_name": "683",
-      "exists_in_final_model": true,
-      "is_graph_output": true,
-      "consumer_count": 0,
-      "producer_operator_index": 160,
-      "producer_op_type": "ADD",
-      "assume_channel_last_layout": false,
-      "status": "direct",
-      "is_rewritten": false,
-      "rewrite_events": [],
-      "rewrite_name_chain": [
-        "683"
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/app/src/main/java/com/example/studentfaceregistry/MainActivity.kt b/app/src/main/java/com/example/studentfaceregistry/MainActivity.kt
index 1abb770..db7d082 100644
--- a/app/src/main/java/com/example/studentfaceregistry/MainActivity.kt
+++ b/app/src/main/java/com/example/studentfaceregistry/MainActivity.kt
@@ -4,6 +4,9 @@ import android.Manifest
 import android.content.pm.PackageManager
 import android.graphics.Typeface
 import android.os.Bundle
+import android.os.SystemClock
+import android.util.Size
+import android.util.Log
 import android.view.Gravity
 import android.view.View
 import android.widget.Button
@@ -21,12 +24,12 @@ import androidx.camera.lifecycle.ProcessCameraProvider
 import androidx.camera.view.PreviewView
 import androidx.core.content.ContextCompat
 import androidx.lifecycle.lifecycleScope
-import com.example.studentfaceregistry.data.StudentRepository
 import com.example.studentfaceregistry.data.Student
-import com.example.studentfaceregistry.face.FaceEmbedder
+import com.example.studentfaceregistry.data.StudentRepository
 import com.example.studentfaceregistry.face.FaceMatcher
 import com.example.studentfaceregistry.face.FaceProcessor
 import com.example.studentfaceregistry.face.MatchType
+import com.google.mlkit.vision.common.InputImage
 import com.example.studentfaceregistry.ui.DetectionUi
 import com.example.studentfaceregistry.ui.OverlayView
 import com.example.studentfaceregistry.upload.UploadServer
@@ -37,38 +40,47 @@ import kotlinx.coroutines.withContext
 import java.util.concurrent.Executors
 
 class MainActivity : AppCompatActivity() {
+    private lateinit var countText: TextView
     private lateinit var previewView: PreviewView
     private lateinit var overlayView: OverlayView
-    private lateinit var statusText: TextView
-    private lateinit var countText: TextView
+    private lateinit var recognitionStatusText: TextView
+    private lateinit var uploadStatusText: TextView
+    private lateinit var uploadUrlText: TextView
+    private lateinit var pauseRecognitionButton: Button
+    private lateinit var homePanel: View
+    private lateinit var recognitionPanel: View
+    private lateinit var uploadPanel: View
+
+    private var currentMode = AppMode.HOME
     private var processor: FaceProcessor? = null
     private var uploadServer: UploadServer? = null
+    private var analysisUseCase: ImageAnalysis? = null
+    private var cameraProvider: ProcessCameraProvider? = null
 
     private val repository by lazy { StudentRepository(this) }
-    // ArcFace 推荐阈值
     private val matcher = FaceMatcher(euclideanThreshold = 1.0f, cosineThreshold = 0.6f)
     private val cameraExecutor = Executors.newSingleThreadExecutor()
     private var students: List<Student> = emptyList()
     private var recognitionEnabled = true
     private var analyzing = false
+    private var lastAnalysisAt = 0L
 
     private val permissionLauncher = registerForActivityResult(
         ActivityResultContracts.RequestMultiplePermissions()
     ) { grants ->
-        if (grants[Manifest.permission.CAMERA] == true) startCamera()
-        else toast("需要相机权限才能识别学生身份。")
+        if (grants[Manifest.permission.CAMERA] == true) {
+            if (currentMode == AppMode.RECOGNITION) {
+                startRecognitionSession()
+            }
+        } else if (currentMode == AppMode.RECOGNITION) {
+            recognitionStatusText.text = "需要相机权限才能识别学生身份。"
+            toast("需要相机权限才能识别学生身份。")
+        }
     }
 
     override fun onCreate(savedInstanceState: Bundle?) {
         super.onCreate(savedInstanceState)
         setContentView(createContentView())
-        processor = runCatching { FaceProcessor(this) }
-            .onFailure {
-                statusText.text = "缺少模型文件：app/src/main/assets/facenet.tflite"
-                toast("请先放入 facenet.tflite 模型。")
-            }
-            .getOrNull()
-        uploadServer = UploadServer(this, repository) { processor }.also { it.start() }
 
         lifecycleScope.launch {
             repository.students.collectLatest { list ->
@@ -77,14 +89,13 @@ class MainActivity : AppCompatActivity() {
             }
         }
 
-        statusText.text = "电脑浏览器打开上传地址来批量导入头像"
-        if (hasCameraPermission()) startCamera() else permissionLauncher.launch(arrayOf(Manifest.permission.CAMERA))
+        switchMode(AppMode.HOME)
     }
 
     override fun onDestroy() {
         super.onDestroy()
-        processor?.close()
-        uploadServer?.close()
+        stopRecognitionSession()
+        stopUploadSession()
         cameraExecutor.shutdown()
     }
 
@@ -108,80 +119,299 @@ class MainActivity : AppCompatActivity() {
             textSize = 15f
             setTextColor(0xFF475569.toInt())
         }
-        statusText = TextView(this).apply {
-            text = "点击上传地址，用电脑浏览器批量上传头像"
-            textSize = 22f
-            typeface = Typeface.DEFAULT_BOLD
-            setTextColor(0xFF0F172A.toInt())
-            setPadding(0, 12, 0, 0)
-        }
         header.addView(countText)
-        header.addView(statusText)
 
-        val cameraFrame = FrameLayout(this).apply {
-            layoutParams = LinearLayout.LayoutParams(LinearLayout.LayoutParams.MATCH_PARENT, 0, 1f)
+        val content = FrameLayout(this).apply {
+            layoutParams = LinearLayout.LayoutParams(
+                LinearLayout.LayoutParams.MATCH_PARENT,
+                0,
+                1f
+            )
         }
-        previewView = PreviewView(this).apply {
-            scaleType = PreviewView.ScaleType.FILL_CENTER
-        }
-        overlayView = OverlayView(this)
-        cameraFrame.addView(previewView, FrameLayout.LayoutParams(-1, -1))
-        cameraFrame.addView(overlayView, FrameLayout.LayoutParams(-1, -1))
 
-        val controls = LinearLayout(this).apply {
-            gravity = Gravity.CENTER
-            orientation = LinearLayout.HORIZONTAL
-            setPadding(20, 20, 20, 28)
-        }
-        controls.addView(Button(this).apply {
-            text = "上传地址"
-            setOnClickListener {
-                statusText.text = uploadServer?.accessUrl() ?: "上传服务未启动"
-            }
-        })
-        controls.addView(Button(this).apply {
-            text = "暂停识别"
-            setOnClickListener {
-                recognitionEnabled = !recognitionEnabled
-                text = if (recognitionEnabled) "暂停识别" else "开始识别"
-                statusText.text = if (recognitionEnabled) "正在识别" else "识别已暂停"
-            }
-        })
+        homePanel = createHomePanel()
+        recognitionPanel = createRecognitionPanel()
+        uploadPanel = createUploadPanel()
+        homePanel.visibility = View.VISIBLE
+        recognitionPanel.visibility = View.GONE
+        uploadPanel.visibility = View.GONE
+
+        content.addView(homePanel, FrameLayout.LayoutParams(-1, -1))
+        content.addView(recognitionPanel, FrameLayout.LayoutParams(-1, -1))
+        content.addView(uploadPanel, FrameLayout.LayoutParams(-1, -1))
+
         root.addView(header)
-        root.addView(cameraFrame)
-        root.addView(controls)
+        root.addView(content)
         return root
     }
 
-    private fun startCamera() {
+    private fun createHomePanel(): View {
+        return LinearLayout(this).apply {
+            orientation = LinearLayout.VERTICAL
+            gravity = Gravity.CENTER
+            setPadding(40, 40, 40, 40)
+
+            addView(TextView(this@MainActivity).apply {
+                text = "请选择要进入的功能"
+                textSize = 26f
+                typeface = Typeface.DEFAULT_BOLD
+                setTextColor(0xFF0F172A.toInt())
+                gravity = Gravity.CENTER
+            })
+
+            addView(TextView(this@MainActivity).apply {
+                text = "识别和注册上传分开运行，减少同时占用相机、模型和本地服务带来的卡顿。"
+                textSize = 16f
+                setTextColor(0xFF475569.toInt())
+                gravity = Gravity.CENTER
+                setPadding(0, 20, 0, 36)
+            })
+
+            addView(Button(this@MainActivity).apply {
+                text = "进入识别"
+                textSize = 20f
+                setPadding(36, 24, 36, 24)
+                setOnClickListener { switchMode(AppMode.RECOGNITION) }
+            })
+
+            addView(Button(this@MainActivity).apply {
+                text = "进入注册上传"
+                textSize = 20f
+                setPadding(36, 24, 36, 24)
+                setOnClickListener { switchMode(AppMode.UPLOAD) }
+            })
+        }
+    }
+
+    private fun createRecognitionPanel(): View {
+        return LinearLayout(this).apply {
+            orientation = LinearLayout.VERTICAL
+            setPadding(24, 16, 24, 24)
+
+            recognitionStatusText = TextView(this@MainActivity).apply {
+                text = "准备进入识别模式"
+                textSize = 22f
+                typeface = Typeface.DEFAULT_BOLD
+                setTextColor(0xFF0F172A.toInt())
+                setPadding(8, 8, 8, 20)
+            }
+            addView(recognitionStatusText)
+
+            val cameraFrame = FrameLayout(this@MainActivity).apply {
+                layoutParams = LinearLayout.LayoutParams(
+                    LinearLayout.LayoutParams.MATCH_PARENT,
+                    0,
+                    1f
+                )
+            }
+            previewView = PreviewView(this@MainActivity).apply {
+                scaleType = PreviewView.ScaleType.FILL_CENTER
+            }
+            overlayView = OverlayView(this@MainActivity)
+            cameraFrame.addView(previewView, FrameLayout.LayoutParams(-1, -1))
+            cameraFrame.addView(overlayView, FrameLayout.LayoutParams(-1, -1))
+            addView(cameraFrame)
+
+            addView(LinearLayout(this@MainActivity).apply {
+                gravity = Gravity.CENTER
+                orientation = LinearLayout.HORIZONTAL
+                setPadding(12, 20, 12, 8)
+
+                addView(Button(this@MainActivity).apply {
+                    text = "返回首页"
+                    setOnClickListener { switchMode(AppMode.HOME) }
+                })
+
+                pauseRecognitionButton = Button(this@MainActivity).apply {
+                    text = "暂停识别"
+                    setOnClickListener {
+                        recognitionEnabled = !recognitionEnabled
+                        text = if (recognitionEnabled) "暂停识别" else "继续识别"
+                        recognitionStatusText.text = if (recognitionEnabled) "正在识别" else "识别已暂停"
+                        if (!recognitionEnabled) {
+                            overlayView.update(emptyList(), 1, 1)
+                        }
+                        updateAnalyzerState()
+                    }
+                }
+                addView(pauseRecognitionButton)
+            })
+        }
+    }
+
+    private fun createUploadPanel(): View {
+        return LinearLayout(this).apply {
+            orientation = LinearLayout.VERTICAL
+            gravity = Gravity.CENTER_HORIZONTAL
+            setPadding(40, 48, 40, 48)
+
+            uploadStatusText = TextView(this@MainActivity).apply {
+                text = "准备进入注册上传模式"
+                textSize = 24f
+                typeface = Typeface.DEFAULT_BOLD
+                setTextColor(0xFF0F172A.toInt())
+                gravity = Gravity.CENTER
+            }
+            addView(uploadStatusText)
+
+            addView(TextView(this@MainActivity).apply {
+                text = "在同一 Wi-Fi 下，用电脑浏览器打开下面的地址，就可以上传学生图片或视频。"
+                textSize = 16f
+                setTextColor(0xFF475569.toInt())
+                gravity = Gravity.CENTER
+                setPadding(0, 16, 0, 20)
+            })
+
+            uploadUrlText = TextView(this@MainActivity).apply {
+                text = "-"
+                textSize = 20f
+                typeface = Typeface.MONOSPACE
+                setTextColor(0xFF0F172A.toInt())
+                gravity = Gravity.CENTER
+                setPadding(24, 20, 24, 20)
+                setTextIsSelectable(true)
+                setBackgroundColor(0xFFE2E8F0.toInt())
+            }
+            addView(uploadUrlText)
+
+            addView(LinearLayout(this@MainActivity).apply {
+                gravity = Gravity.CENTER
+                orientation = LinearLayout.HORIZONTAL
+                setPadding(12, 28, 12, 8)
+
+                addView(Button(this@MainActivity).apply {
+                    text = "刷新地址"
+                    setOnClickListener { startUploadSession() }
+                })
+
+                addView(Button(this@MainActivity).apply {
+                    text = "返回首页"
+                    setOnClickListener { switchMode(AppMode.HOME) }
+                })
+            })
+        }
+    }
+
+    private fun switchMode(mode: AppMode) {
+        if (currentMode == mode) return
+
+        val previousMode = currentMode
+        currentMode = mode
+        homePanel.visibility = if (mode == AppMode.HOME) View.VISIBLE else View.GONE
+        recognitionPanel.visibility = if (mode == AppMode.RECOGNITION) View.VISIBLE else View.GONE
+        uploadPanel.visibility = if (mode == AppMode.UPLOAD) View.VISIBLE else View.GONE
+
+        when (previousMode) {
+            AppMode.RECOGNITION -> stopRecognitionSession()
+            AppMode.UPLOAD -> stopUploadSession()
+            AppMode.HOME -> Unit
+        }
+
+        when (mode) {
+            AppMode.HOME -> Unit
+            AppMode.RECOGNITION -> enterRecognitionMode()
+            AppMode.UPLOAD -> enterUploadMode()
+        }
+    }
+
+    private fun enterRecognitionMode() {
+        recognitionEnabled = true
+        analyzing = false
+        lastAnalysisAt = 0L
+        pauseRecognitionButton.text = "暂停识别"
+        recognitionStatusText.text = "正在准备识别服务..."
+        overlayView.update(emptyList(), 1, 1)
+
+        if (hasCameraPermission()) {
+            startRecognitionSession()
+        } else {
+            permissionLauncher.launch(arrayOf(Manifest.permission.CAMERA))
+        }
+    }
+
+    private fun startRecognitionSession() {
+        if (currentMode != AppMode.RECOGNITION) return
+
+        val faceProcessor = ensureProcessor() ?: return
         val providerFuture = ProcessCameraProvider.getInstance(this)
         providerFuture.addListener({
-            val cameraProvider = providerFuture.get()
+            val provider = providerFuture.get()
+            cameraProvider = provider
+            if (currentMode != AppMode.RECOGNITION) {
+                provider.unbindAll()
+                return@addListener
+            }
+
             val preview = Preview.Builder().build().also {
                 it.setSurfaceProvider(previewView.surfaceProvider)
             }
             val analysis = ImageAnalysis.Builder()
+                .setTargetResolution(ANALYSIS_SIZE)
                 .setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST)
                 .build()
-                .also { it.setAnalyzer(cameraExecutor) { image -> analyze(image) } }
-            cameraProvider.unbindAll()
-            cameraProvider.bindToLifecycle(this, CameraSelector.DEFAULT_BACK_CAMERA, preview, analysis)
+
+            provider.unbindAll()
+            provider.bindToLifecycle(this, CameraSelector.DEFAULT_BACK_CAMERA, preview, analysis)
+            analysisUseCase = analysis
+            recognitionStatusText.text = if (faceProcessor === processor) "正在识别" else "识别服务已启动"
+            updateAnalyzerState()
         }, ContextCompat.getMainExecutor(this))
     }
 
+    private fun stopRecognitionSession() {
+        recognitionEnabled = false
+        analyzing = false
+        analysisUseCase?.clearAnalyzer()
+        analysisUseCase = null
+        cameraProvider?.unbindAll()
+        cameraProvider = null
+        overlayView.update(emptyList(), 1, 1)
+        processor?.close()
+        processor = null
+    }
+
+    private fun updateAnalyzerState() {
+        val analysis = analysisUseCase ?: return
+        if (currentMode == AppMode.RECOGNITION && recognitionEnabled && processor != null) {
+            analysis.setAnalyzer(cameraExecutor) { image -> analyze(image) }
+        } else {
+            analysis.clearAnalyzer()
+        }
+    }
+
     private fun analyze(image: ImageProxy) {
         val faceProcessor = processor
-        if (!recognitionEnabled || analyzing || faceProcessor == null) {
+        val now = SystemClock.elapsedRealtime()
+        if (
+            currentMode != AppMode.RECOGNITION ||
+            !recognitionEnabled ||
+            analyzing ||
+            faceProcessor == null ||
+            now - lastAnalysisAt < ANALYSIS_INTERVAL_MS
+        ) {
             image.close()
             return
         }
-        analyzing = true
-        lifecycleScope.launch {
-            try {
-                val bitmap = withContext(Dispatchers.Default) { ImageUtils.imageProxyToBitmap(image) }
-                val faces = faceProcessor.detectFaces(bitmap)
 
-                // 同时识别所有人脸
+        analyzing = true
+        lastAnalysisAt = now
+        lifecycleScope.launch(Dispatchers.Default) {
+            try {
+                val mediaImage = image.image ?: return@launch
+                val inputImage = InputImage.fromMediaImage(mediaImage, image.imageInfo.rotationDegrees)
+                val faces = faceProcessor.detectFaces(inputImage)
+
+                if (faces.isEmpty()) {
+                    withContext(Dispatchers.Main) {
+                        if (currentMode == AppMode.RECOGNITION) {
+                            overlayView.update(emptyList(), 1, 1)
+                            updateRecognitionStatus(emptyList())
+                        }
+                    }
+                    return@launch
+                }
+
+                val bitmap = ImageUtils.imageProxyToBitmap(image)
                 val detections = faces.map { face ->
                     val embedding = faceProcessor.embedFace(bitmap, face)
                     val result = matcher.findNearest(embedding, students)
@@ -192,12 +422,19 @@ class MainActivity : AppCompatActivity() {
                     )
                 }
 
-                overlayView.update(detections, bitmap.width, bitmap.height)
-
-                // 显示所有识别结果
-                updateStatusText(detections)
-            } catch (_: Exception) {
-                statusText.text = "识别暂不可用，请确认模型文件和相机画面。"
+                withContext(Dispatchers.Main) {
+                    if (currentMode == AppMode.RECOGNITION) {
+                        overlayView.update(detections, bitmap.width, bitmap.height)
+                        updateRecognitionStatus(detections)
+                    }
+                }
+            } catch (e: Exception) {
+                Log.e("MainActivity", "Recognition failed", e)
+                withContext(Dispatchers.Main) {
+                    if (currentMode == AppMode.RECOGNITION) {
+                        recognitionStatusText.text = "识别失败：${e.message ?: e.javaClass.simpleName}"
+                    }
+                }
             } finally {
                 analyzing = false
                 image.close()
@@ -205,40 +442,80 @@ class MainActivity : AppCompatActivity() {
         }
     }
 
-    private fun updateStatusText(detections: List<DetectionUi>) {
+    private fun updateRecognitionStatus(detections: List<DetectionUi>) {
         if (detections.isEmpty()) {
-            statusText.text = "未检测到人脸"
+            recognitionStatusText.text = "未检测到人脸"
             return
         }
 
-        // 统计识别结果
         val matched = detections.filter { it.result.isMatched }
         val unsure = detections.filter { it.result.matchType == MatchType.UNSURE }
-        val unmatched = detections.filter { !it.result.isMatched && it.result.matchType != MatchType.UNSURE }
-
         val total = detections.size
-        val matchedCount = matched.size
 
-        if (matchedCount > 0) {
-            if (matchedCount == 1 && total == 1) {
+        if (matched.isNotEmpty()) {
+            if (matched.size == 1 && total == 1) {
                 val student = matched[0].result.student!!
                 val confidence = (matched[0].confidence!! * 100).toInt()
-                statusText.text = "识别到：${student.name}（${student.studentNo}）置信度${confidence}%"
+                recognitionStatusText.text = "识别到：${student.name}（${student.studentNo}）置信度${confidence}%"
             } else {
                 val names = matched.joinToString(", ") {
-                    val s = it.result.student!!
-                    val c = (it.confidence!! * 100).toInt()
-                    "${s.name}(${c}%)"
+                    val student = it.result.student!!
+                    val confidence = (it.confidence!! * 100).toInt()
+                    "${student.name}(${confidence}%)"
                 }
-                statusText.text = "识别到 $matchedCount/$total 人：$names"
+                recognitionStatusText.text = "识别到 ${matched.size}/$total 人：$names"
             }
         } else if (unsure.isNotEmpty()) {
-            statusText.text = "疑似检测到 ${unsure.size} 人（不确定）"
+            recognitionStatusText.text = "疑似检测到 ${unsure.size} 人（不确定）"
         } else {
-            statusText.text = "检测到 $total 张未入库人脸"
+            recognitionStatusText.text = "检测到 $total 张未入库人脸"
         }
     }
 
+    private fun enterUploadMode() {
+        uploadStatusText.text = "正在准备注册上传服务..."
+        startUploadSession()
+    }
+
+    private fun startUploadSession() {
+        if (currentMode != AppMode.UPLOAD) return
+
+        val server = uploadServer ?: UploadServer(this, repository).also {
+            uploadServer = it
+        }
+        runCatching {
+            server.start()
+            uploadStatusText.text = "请在电脑浏览器打开以下地址"
+            uploadUrlText.text = server.accessUrl()
+        }.onFailure {
+            val message = it.message ?: "unknown error"
+            uploadStatusText.text = "上传服务启动失败：$message"
+            uploadUrlText.text = "-"
+        }
+    }
+
+    private fun stopUploadSession() {
+        uploadServer?.close()
+        uploadServer = null
+        uploadStatusText.text = "注册上传服务已停止"
+        uploadUrlText.text = "-"
+    }
+
+    private fun ensureProcessor(): FaceProcessor? {
+        val current = processor
+        if (current != null) return current
+
+        val created = runCatching { FaceProcessor(this) }
+            .onFailure {
+                val message = it.message ?: "unknown error"
+                recognitionStatusText.text = "模型初始化失败：$message"
+                toast("模型初始化失败：$message")
+            }
+            .getOrNull()
+        processor = created
+        return created
+    }
+
     private fun hasCameraPermission(): Boolean {
         return ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) == PackageManager.PERMISSION_GRANTED
     }
@@ -246,4 +523,15 @@ class MainActivity : AppCompatActivity() {
     private fun toast(message: String) {
         Toast.makeText(this, message, Toast.LENGTH_SHORT).show()
     }
+
+    companion object {
+        private const val ANALYSIS_INTERVAL_MS = 250L
+        private val ANALYSIS_SIZE = Size(640, 480)
+    }
+}
+
+private enum class AppMode {
+    HOME,
+    RECOGNITION,
+    UPLOAD
 }
diff --git a/app/src/main/java/com/example/studentfaceregistry/face/FaceEmbedder.kt b/app/src/main/java/com/example/studentfaceregistry/face/FaceEmbedder.kt
index 6150858..643ede9 100644
--- a/app/src/main/java/com/example/studentfaceregistry/face/FaceEmbedder.kt
+++ b/app/src/main/java/com/example/studentfaceregistry/face/FaceEmbedder.kt
@@ -2,9 +2,9 @@ package com.example.studentfaceregistry.face
 
 import android.content.Context
 import android.graphics.Bitmap
-import org.tensorflow.lite.Interpreter
-import java.nio.ByteBuffer
-import java.nio.ByteOrder
+import com.google.ai.edge.litert.Accelerator
+import com.google.ai.edge.litert.CompiledModel
+import com.google.ai.edge.litert.TensorBuffer
 import kotlin.math.sqrt
 
 /**
@@ -21,7 +21,9 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
         ARCFACE    // 归一化：pixel / 255.0，然后 (x - mean) / std
     }
 
-    private val interpreter: Interpreter
+    private val model: CompiledModel
+    private val inputBuffer: TensorBuffer
+    private val outputBuffer: TensorBuffer
     private val inputSize: Int
     private val outputSize: Int
 
@@ -30,31 +32,16 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
     private val std = floatArrayOf(127.5f, 127.5f, 127.5f)
 
     init {
-        val modelBytes = context.assets.open("facenet.tflite").use { input ->
-            input.readBytes()
-        }
-
-        val options = Interpreter.Options().apply {
-            setNumThreads(4)
-            // 如果设备支持，可以启用 GPU delegate
-            // val gpuDelegate = GpuDelegate()
-            // addDelegate(gpuDelegate)
-        }
-
-        interpreter = Interpreter(
-            ByteBuffer.allocateDirect(modelBytes.size).apply {
-                order(ByteOrder.nativeOrder())
-                put(modelBytes)
-                rewind()
-            },
-            options
+        model = CompiledModel.create(
+            context.assets,
+            MODEL_ASSET_NAME,
+            CompiledModel.Options(setOf(Accelerator.CPU))
         )
-
-        val inputShape = interpreter.getInputTensor(0).shape()
-        inputSize = inputShape[1] // 通常是 112 或 160
-
-        val outputShape = interpreter.getOutputTensor(0).shape()
-        outputSize = outputShape.last()
+        inputBuffer = model.createInputBuffers().first()
+        outputBuffer = model.createOutputBuffers().first()
+        val inputElements = inputBuffer.readFloat().size
+        inputSize = inferInputSize(inputElements)
+        outputSize = outputBuffer.readFloat().size
     }
 
     /**
@@ -67,13 +54,11 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
         val resized = Bitmap.createScaledBitmap(face, inputSize, inputSize, true)
 
         // 准备输入数据
-        val input = ByteBuffer
-            .allocateDirect(inputSize * inputSize * 3 * Float.SIZE_BYTES)
-            .order(ByteOrder.nativeOrder())
-
+        val input = FloatArray(inputSize * inputSize * 3)
         val pixels = IntArray(inputSize * inputSize)
         resized.getPixels(pixels, 0, inputSize, 0, 0, inputSize, inputSize)
 
+        var index = 0
         for (color in pixels) {
             val r = ((color shr 16) and 0xFF).toFloat()
             val g = ((color shr 8) and 0xFF).toFloat()
@@ -82,32 +67,30 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
             when (modelType) {
                 ModelType.FACENET -> {
                     // FaceNet 归一化：(pixel - 127.5) / 127.5
-                    input.putFloat((r - 127.5f) / 127.5f)
-                    input.putFloat((g - 127.5f) / 127.5f)
-                    input.putFloat((b - 127.5f) / 127.5f)
+                    input[index++] = (r - 127.5f) / 127.5f
+                    input[index++] = (g - 127.5f) / 127.5f
+                    input[index++] = (b - 127.5f) / 127.5f
                 }
                 ModelType.ARCFACE -> {
                     // ArcFace 归一化：(pixel - mean) / std
                     // 等价于 (pixel - 127.5) / 127.5，但语义更清晰
-                    input.putFloat((r - mean[0]) / std[0])
-                    input.putFloat((g - mean[1]) / std[1])
-                    input.putFloat((b - mean[2]) / std[2])
+                    input[index++] = (r - mean[0]) / std[0]
+                    input[index++] = (g - mean[1]) / std[1]
+                    input[index++] = (b - mean[2]) / std[2]
                 }
             }
         }
 
-        input.rewind()
-
         // 执行推理
-        val output = Array(1) { FloatArray(outputSize) }
-        interpreter.run(input, output)
+        inputBuffer.writeFloat(input)
+        model.run(listOf(inputBuffer), listOf(outputBuffer))
 
         // L2 归一化输出特征
-        return l2Normalize(output[0])
+        return l2Normalize(outputBuffer.readFloat())
     }
 
     override fun close() {
-        interpreter.close()
+        model.close()
     }
 
     /**
@@ -130,4 +113,17 @@ class FaceEmbedder(context: Context, private val modelType: ModelType = ModelTyp
      * 获取特征向量维度
      */
     fun getOutputSize(): Int = outputSize
+
+    companion object {
+        const val MODEL_ASSET_NAME = "facenet.tflite"
+    }
+
+    private fun inferInputSize(floatCount: Int): Int {
+        val pixelCount = floatCount / 3
+        val size = kotlin.math.sqrt(pixelCount.toDouble()).toInt()
+        require(size > 0 && size * size * 3 == floatCount) {
+            "Unexpected face model input size: $floatCount floats"
+        }
+        return size
+    }
 }
diff --git a/app/src/main/java/com/example/studentfaceregistry/face/FaceMatcher.kt b/app/src/main/java/com/example/studentfaceregistry/face/FaceMatcher.kt
index d2324c7..3af9c9c 100644
--- a/app/src/main/java/com/example/studentfaceregistry/face/FaceMatcher.kt
+++ b/app/src/main/java/com/example/studentfaceregistry/face/FaceMatcher.kt
@@ -28,8 +28,8 @@ class FaceMatcher(
         var secondNearestEuclidean = Float.MAX_VALUE
 
         for (student in students) {
-            val euclideanDist = euclideanDistance(embedding, student.embedding)
             val cosineSim = cosineSimilarity(embedding, student.embedding)
+            val euclideanDist = euclideanDistanceFromCosine(cosineSim)
 
             if (euclideanDist < nearestEuclidean) {
                 secondNearestEuclidean = nearestEuclidean
@@ -60,10 +60,11 @@ class FaceMatcher(
         topK: Int = 3
     ): List<MatchCandidate> {
         val candidates = students.map { student ->
+            val cosineSim = cosineSimilarity(embedding, student.embedding)
             MatchCandidate(
                 student = student,
-                euclideanDistance = euclideanDistance(embedding, student.embedding),
-                cosineSimilarity = cosineSimilarity(embedding, student.embedding)
+                euclideanDistance = euclideanDistanceFromCosine(cosineSim),
+                cosineSimilarity = cosineSim
             )
         }
 
@@ -83,6 +84,13 @@ class FaceMatcher(
         return sqrt(sum)
     }
 
+    /**
+     * 对已 L2 归一化向量，通过余弦相似度推导欧氏距离，避免重复遍历
+     */
+    private fun euclideanDistanceFromCosine(cosineSimilarity: Float): Float {
+        return sqrt((2f - 2f * cosineSimilarity).coerceAtLeast(0f))
+    }
+
     /**
      * 计算余弦相似度
      * 对于已 L2 归一化的向量，余弦相似度等于点积
diff --git a/app/src/main/java/com/example/studentfaceregistry/face/FaceProcessor.kt b/app/src/main/java/com/example/studentfaceregistry/face/FaceProcessor.kt
index 1a66cb7..d7f3cd6 100644
--- a/app/src/main/java/com/example/studentfaceregistry/face/FaceProcessor.kt
+++ b/app/src/main/java/com/example/studentfaceregistry/face/FaceProcessor.kt
@@ -102,8 +102,15 @@ class FaceProcessor(context: Context) : AutoCloseable {
     /**
      * 实时检测人脸（用于摄像头预览）
      */
+    suspend fun detectFaces(image: InputImage): List<Face> {
+        return realtimeDetector.process(image).await()
+    }
+
+    /**
+     * 实时检测人脸（bitmap 版本）
+     */
     suspend fun detectFaces(bitmap: Bitmap): List<Face> {
-        return realtimeDetector.process(InputImage.fromBitmap(bitmap, 0)).await()
+        return detectFaces(InputImage.fromBitmap(bitmap, 0))
     }
 
     /**
diff --git a/app/src/main/java/com/example/studentfaceregistry/face/SmartEnrollment.kt b/app/src/main/java/com/example/studentfaceregistry/face/SmartEnrollment.kt
index 151a231..3136af6 100644
--- a/app/src/main/java/com/example/studentfaceregistry/face/SmartEnrollment.kt
+++ b/app/src/main/java/com/example/studentfaceregistry/face/SmartEnrollment.kt
@@ -469,7 +469,31 @@ sealed class EnrollmentResult {
         val frameCount: Int = 1,
         val yawRange: Float = 0f,
         val pitchRange: Float = 0f
-    ) : EnrollmentResult()
+    ) : EnrollmentResult() {
+        override fun equals(other: Any?): Boolean {
+            if (this === other) return true
+            if (javaClass != other?.javaClass) return false
+
+            other as Success
+
+            if (frameCount != other.frameCount) return false
+            if (yawRange != other.yawRange) return false
+            if (pitchRange != other.pitchRange) return false
+            if (!embedding.contentEquals(other.embedding)) return false
+            if (sourceType != other.sourceType) return false
+
+            return true
+        }
+
+        override fun hashCode(): Int {
+            var result = frameCount
+            result = 31 * result + yawRange.hashCode()
+            result = 31 * result + pitchRange.hashCode()
+            result = 31 * result + embedding.contentHashCode()
+            result = 31 * result + sourceType.hashCode()
+            return result
+        }
+    }
 
     data class Failure(val reason: String) : EnrollmentResult()
 }
diff --git a/app/src/main/java/com/example/studentfaceregistry/upload/UploadServer.kt b/app/src/main/java/com/example/studentfaceregistry/upload/UploadServer.kt
index cc00500..5a7e0bc 100644
--- a/app/src/main/java/com/example/studentfaceregistry/upload/UploadServer.kt
+++ b/app/src/main/java/com/example/studentfaceregistry/upload/UploadServer.kt
@@ -4,7 +4,6 @@ import android.content.Context
 import android.util.Base64
 import com.example.studentfaceregistry.data.Student
 import com.example.studentfaceregistry.data.StudentRepository
-import com.example.studentfaceregistry.face.FaceProcessor
 import com.example.studentfaceregistry.face.SmartEnrollment
 import kotlinx.coroutines.runBlocking
 import org.json.JSONObject
@@ -20,18 +19,17 @@ import kotlin.concurrent.thread
 
 class UploadServer(
     private val context: Context,
-    private val repository: StudentRepository,
-    private val processorProvider: () -> FaceProcessor?
+    private val repository: StudentRepository
 ) : AutoCloseable {
     private var serverSocket: ServerSocket? = null
     @Volatile private var running = false
-    private lateinit var smartEnrollment: SmartEnrollment
+    private var smartEnrollment: SmartEnrollment? = null
 
     fun start() {
         if (running) return
+        val socket = ServerSocket(8080)
+        serverSocket = socket
         running = true
-        smartEnrollment = SmartEnrollment(context)
-        serverSocket = ServerSocket(8080)
         thread(name = "upload-server", isDaemon = true) {
             while (running) {
                 val socket = try {
@@ -52,7 +50,9 @@ class UploadServer(
     override fun close() {
         running = false
         runCatching { serverSocket?.close() }
-        runCatching { smartEnrollment.close() }
+        serverSocket = null
+        runCatching { smartEnrollment?.close() }
+        smartEnrollment = null
     }
 
     private fun handle(socket: Socket) {
@@ -71,8 +71,9 @@ class UploadServer(
     }
 
     private fun handleUpload(request: HttpRequest, output: OutputStream) {
-        if (processorProvider() == null) {
-            respondText(output, 503, "Missing facenet.tflite model", "text/plain; charset=utf-8")
+        val enrollment = getOrCreateEnrollment()
+        if (enrollment == null) {
+            respondText(output, 503, "Face model unavailable", "text/plain; charset=utf-8")
             return
         }
         val bodyText = String(request.body, StandardCharsets.UTF_8)
@@ -85,7 +86,7 @@ class UploadServer(
             // 单文件或文件列表上传（支持图片和视频）
             val items = parseUploadItems(payload)
             items.forEachIndexed { index, item ->
-                processUploadItem(item.fileName.ifBlank { "student_$index" }, item.base64, item.mimeType) { ok, name, error ->
+                processUploadItem(item.fileName.ifBlank { "student_$index" }, item.base64, item.mimeType, enrollment) { ok, name, error ->
                     if (ok) {
                         accepted += 1
                         notices += "OK $name"
@@ -190,19 +191,31 @@ class UploadServer(
         }
     }
 
-    private fun processUploadItem(fileName: String, base64: String, mimeType: String, report: (ok: Boolean, name: String, error: String?) -> Unit) {
+    private fun processUploadItem(
+        fileName: String,
+        base64: String,
+        mimeType: String,
+        enrollment: SmartEnrollment,
+        report: (ok: Boolean, name: String, error: String?) -> Unit
+    ) {
         runCatching {
             val data = Base64.decode(base64, Base64.DEFAULT)
-            processBytes(fileName, data, mimeType, report)
+            processBytes(fileName, data, mimeType, enrollment, report)
         }.onFailure {
             report(false, fileName, it.message)
         }
     }
 
-    private fun processBytes(fileName: String, data: ByteArray, mimeType: String, report: (ok: Boolean, name: String, error: String?) -> Unit) {
+    private fun processBytes(
+        fileName: String,
+        data: ByteArray,
+        mimeType: String,
+        enrollment: SmartEnrollment,
+        report: (ok: Boolean, name: String, error: String?) -> Unit
+    ) {
         runCatching {
             val result = runBlocking {
-                smartEnrollment.enroll(data, mimeType, fileName)
+                enrollment.enroll(data, mimeType, fileName)
             }
 
             when (result) {
@@ -230,6 +243,16 @@ class UploadServer(
         }
     }
 
+    @Synchronized
+    private fun getOrCreateEnrollment(): SmartEnrollment? {
+        val current = smartEnrollment
+        if (current != null) return current
+
+        val created = runCatching { SmartEnrollment(context) }.getOrNull()
+        smartEnrollment = created
+        return created
+    }
+
     private fun parseStudent(filename: String): Pair<String, String> {
         val base = filename.substringBeforeLast(".")
         val parts = base.split(Regex("[_\\-\\s]+"), limit = 2).map { it.trim() }