docs: 📝 更新鹰眼AI文档

- 优化中文提示信息格式
- 添加限制级内容检测提示
- 规范化日志输出格式
- 完善示例输出说明

test/test_yingyan.py

@@ -8,80 +8,80 @@
 
 class TestYingYanAI(unittest.TestCase):
     def setUp(self):
-        """Set up test fixtures"""
+        """初始化测试环境"""
         self.yingyan = YingYanAI()
         self.logger = setup_logger(__name__)
 
     @patch("tensorflow.keras.Model.fit")
     @patch("tensorflow.keras.Model.save")
     def test_train_normal_dataset(self, mock_save, mock_fit):
-        """Test training with normal sized dataset"""
-        # Mock train generator
+        """测试正常大小数据集的训练过程"""
+        # 模拟训练数据生成器
         self.yingyan.train_generator.filenames = ["img1.jpg", "img2.jpg"] * 10
 
-        # Mock fit return value
+        # 模拟训练历史记录
         mock_history = Mock()
         mock_fit.return_value = mock_history
 
-        # Execute training
+        # 执行训练
         result = self.yingyan.train(epochs=5)
 
-        # Verify fit was called with correct parameters
+        # 验证训练参数是否正确
         mock_fit.assert_called_once_with(
             self.yingyan.train_generator,
             epochs=5,
             validation_data=self.yingyan.validation_generator,
             verbose=1,
         )
 
-        # Verify model was saved
+        # 验证模型是否保存
         mock_save.assert_called_once_with("models/yingyan_model.h5")
 
-        # Verify history was returned
+        # 验证返回训练历史
         self.assertEqual(result, mock_history)
 
     @patch("tensorflow.keras.Model.fit")
     @patch("tensorflow.keras.Model.save")
     def test_train_small_dataset(self, mock_save, mock_fit):
-        """Test training with small dataset (< 10 samples)"""
-        # Mock small dataset
+        """测试小规模数据集的训练过程（少于10个样本）"""
+        # 模拟小规模数据集
         self.yingyan.train_generator.filenames = ["img1.jpg", "img2.jpg"]
 
         mock_history = Mock()
         mock_fit.return_value = mock_history
 
         result = self.yingyan.train(epochs=5)
 
-        # Verify warning was logged for small dataset
+        # 验证小数据集警告
         self.assertTrue(len(self.yingyan.train_generator.filenames) < 10)
 
-        # Verify training still proceeded
+        # 验证训练是否继续进行
         mock_fit.assert_called_once()
         mock_save.assert_called_once()
         self.assertEqual(result, mock_history)
 
     @patch("tensorflow.keras.Model.fit")
     def test_train_model_save_error(self, mock_fit):
-        """Test handling of model save errors"""
-        # Mock successful training
+        """测试模型保存错误的处理"""
+        # 模拟成功训练
         mock_history = Mock()
         mock_fit.return_value = mock_history
 
-        # Mock save error
+        # 模拟保存错误
         with patch("tensorflow.keras.Model.save") as mock_save:
-            mock_save.side_effect = Exception("Save failed")
+            mock_save.side_effect = Exception("保存失败")
 
-            # Verify exception is raised
+            # 验证异常抛出
             with self.assertRaises(Exception):
                 self.yingyan.train(epochs=5)
 
     def test_models_directory_creation(self):
-        """Test models directory is created if not exists"""
-        # Remove models directory if exists
+        """测试模型目录的创建"""
+        # 如果存在则删除模型目录
         if os.path.exists("models"):
             os.rmdir("models")
 
-        # Train should create models directory
+        # 训练时应创建模型目录
         with patch("tensorflow.keras.Model.fit"), patch("tensorflow.keras.Model.save"):
             self.yingyan.train()