添加API密钥管理功能，包括获取、更新和验证API密钥的接口，增强了对Mathpix和其他API的支持；优化了设置面板的用户体验，改进了API密钥的状态显示和编辑功能，确保用户能够方便地管理和验证API密钥。

models/alibaba.py

@@ -5,7 +5,9 @@ from .base import BaseModel
 
 class AlibabaModel(BaseModel):
     def __init__(self, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None, model_name: str = None):
-        self.model_name = model_name or "QVQ-Max-2025-03-25"  # 默认使用QVQ-Max模型
+        # 如果没有提供模型名称，才使用默认值
+        self.model_name = model_name if model_name else "QVQ-Max-2025-03-25"
+        print(f"初始化阿里巴巴模型: {self.model_name}")
         # 在super().__init__之前设置model_name，这样get_default_system_prompt能使用它
         super().__init__(api_key, temperature, system_prompt, language)
     
@@ -36,22 +38,35 @@ class AlibabaModel(BaseModel):
             "qwen-vl-max-latest": "qwen-vl-max",  # 修正为正确的API标识符
         }
         
+        print(f"模型名称: {self.model_name}")
+        
         # 从模型映射表中获取模型标识符，如果不存在则使用默认值
         model_id = model_mapping.get(self.model_name)
         if model_id:
+            print(f"从映射表中获取到模型标识符: {model_id}")
             return model_id
             
         # 如果没有精确匹配，检查是否包含特定前缀
-        if self.model_name and "qwen-vl" in self.model_name:
-            if "max" in self.model_name:
+        if self.model_name and "qwen-vl" in self.model_name.lower():
+            if "max" in self.model_name.lower():
+                print(f"识别为qwen-vl-max模型")
                 return "qwen-vl-max"
-            elif "plus" in self.model_name:
+            elif "plus" in self.model_name.lower():
+                print(f"识别为qwen-vl-plus模型")
                 return "qwen-vl-plus"
-            elif "lite" in self.model_name:
+            elif "lite" in self.model_name.lower():
+                print(f"识别为qwen-vl-lite模型")
                 return "qwen-vl-lite"
+            print(f"默认使用qwen-vl-max模型")
             return "qwen-vl-max"  # 默认使用最强版本
+        
+        # 如果包含QVQ或alibaba关键词，默认使用qvq-max
+        if self.model_name and ("qvq" in self.model_name.lower() or "alibaba" in self.model_name.lower()):
+            print(f"识别为QVQ模型，使用qvq-max")
+            return "qvq-max"
             
         # 最后的默认值
+        print(f"警告：无法识别的模型名称 {self.model_name}，默认使用qvq-max")
         return "qvq-max"
 
     def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
@@ -107,7 +122,8 @@ class AlibabaModel(BaseModel):
                 is_answering = False
                 
                 # 检查是否为通义千问VL模型（不支持reasoning_content）
-                is_qwen_vl = "qwen-vl" in self.get_model_identifier()
+                is_qwen_vl = "qwen-vl" in self.get_model_identifier().lower()
+                print(f"分析文本使用模型标识符: {self.get_model_identifier()}, 是否为千问VL模型: {is_qwen_vl}")
                 
                 for chunk in response:
                     if not chunk.choices:
@@ -237,7 +253,8 @@ class AlibabaModel(BaseModel):
                 is_answering = False
                 
                 # 检查是否为通义千问VL模型（不支持reasoning_content）
-                is_qwen_vl = "qwen-vl" in self.get_model_identifier()
+                is_qwen_vl = "qwen-vl" in self.get_model_identifier().lower()
+                print(f"分析图像使用模型标识符: {self.get_model_identifier()}, 是否为千问VL模型: {is_qwen_vl}")
                 
                 for chunk in response:
                     if not chunk.choices:

models/anthropic.py

@@ -56,7 +56,7 @@ class AnthropicModel(BaseModel):
             # 处理推理配置
             if hasattr(self, 'reasoning_config') and self.reasoning_config:
                 # 如果设置了extended reasoning
-                if self.reasoning_config.get('reasoning_depth') == 'extended':
+                if self.reasoning_config.get('reasoning_depth') == 'extended':  
                     think_budget = self.reasoning_config.get('think_budget', max_tokens // 2)
                     payload['thinking'] = {
                         'type': 'enabled',
@@ -64,7 +64,6 @@ class AnthropicModel(BaseModel):
                     }
                 # 如果设置了instant模式
                 elif self.reasoning_config.get('speed_mode') == 'instant':
-                    payload['speed_mode'] = 'instant'
                     # 确保当使用speed_mode时不包含thinking参数
                     if 'thinking' in payload:
                         del payload['thinking']

models/deepseek.py

@@ -23,8 +23,22 @@ class DeepSeekModel(BaseModel):
         # 通过模型名称来确定实际的API调用标识符
         if self.model_name == "deepseek-chat":
             return "deepseek-chat"
-        # deepseek-reasoner是默认的推理模型名称
-        return "deepseek-reasoner"
+        # 如果是deepseek-reasoner或包含reasoner的模型名称，返回推理模型标识符
+        if "reasoner" in self.model_name.lower():
+            return "deepseek-reasoner"
+        # 对于deepseek-chat也返回对应的模型名称
+        if "chat" in self.model_name.lower() or self.model_name == "deepseek-chat":
+            return "deepseek-chat"
+        
+        # 根据配置中的模型ID来确定实际的模型类型
+        if self.model_name == "deepseek-reasoner":
+            return "deepseek-reasoner"
+        elif self.model_name == "deepseek-chat":
+            return "deepseek-chat"
+            
+        # 默认使用deepseek-chat作为API标识符
+        print(f"未知的DeepSeek模型名称: {self.model_name}，使用deepseek-chat作为默认值")
+        return "deepseek-chat"
 
     def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
         """Stream DeepSeek's response for text analysis"""
@@ -75,10 +89,10 @@ class DeepSeekModel(BaseModel):
                 }
                 
                 # 只有非推理模型才设置temperature参数
-                if not self.model_name.endswith('reasoner') and self.temperature is not None:
+                if not self.get_model_identifier().endswith('reasoner') and self.temperature is not None:
                     params["temperature"] = self.temperature
                     
-                print(f"调用DeepSeek API: {self.get_model_identifier()}, 是否设置温度: {not self.model_name.endswith('reasoner')}")
+                print(f"调用DeepSeek API: {self.get_model_identifier()}, 是否设置温度: {not self.get_model_identifier().endswith('reasoner')}, 温度值: {self.temperature if not self.get_model_identifier().endswith('reasoner') else 'N/A'}")
 
                 response = client.chat.completions.create(**params)
                 
@@ -253,7 +267,7 @@ class DeepSeekModel(BaseModel):
                 }
                 
                 # 只有非推理模型才设置temperature参数
-                if not self.model_name.endswith('reasoner') and self.temperature is not None:
+                if not self.get_model_identifier().endswith('reasoner') and self.temperature is not None:
                     params["temperature"] = self.temperature
 
                 response = client.chat.completions.create(**params)

models/factory.py

@@ -98,36 +98,26 @@ class ModelFactory:
     @classmethod
     def create_model(cls, model_name: str, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None) -> BaseModel:
         """
-        Create and return an instance of the specified model.
+        Create a model instance based on the model name.
         
         Args:
-            model_name: The identifier of the model to create
-            api_key: The API key for the model
-            temperature: Optional temperature parameter for response generation
-            system_prompt: Optional custom system prompt
-            language: Optional language preference for responses
+            model_name: The identifier for the model
+            api_key: The API key for the model service
+            temperature: The temperature to use for generation
+            system_prompt: The system prompt to use
+            language: The preferred language for responses
             
         Returns:
-            An instance of the specified model
-            
-        Raises:
-            ValueError: If the model_name is not recognized
+            A model instance
         """
-        model_info = cls._models.get(model_name)
-        if not model_info:
+        if model_name not in cls._models:
             raise ValueError(f"Unknown model: {model_name}")
-        
+            
+        model_info = cls._models[model_name]
         model_class = model_info['class']
         
-        # 对于Mathpix模型，不传递language参数
-        if model_name == 'mathpix':
-            return model_class(
-                api_key=api_key,
-                temperature=temperature,
-                system_prompt=system_prompt
-            )
-        else:
-            # 对于所有其他模型，传递model_name参数
+        # 对于DeepSeek模型，需要传递正确的模型名称
+        if 'deepseek' in model_name.lower():
             return model_class(
                 api_key=api_key,
                 temperature=temperature,
@@ -135,6 +125,30 @@ class ModelFactory:
                 language=language,
                 model_name=model_name
             )
+        # 对于阿里巴巴模型，也需要传递正确的模型名称
+        elif 'qwen' in model_name.lower() or 'qvq' in model_name.lower() or 'alibaba' in model_name.lower():
+            return model_class(
+                api_key=api_key,
+                temperature=temperature,
+                system_prompt=system_prompt,
+                language=language,
+                model_name=model_name
+            )
+        # 对于Mathpix模型，不传递language参数
+        elif model_name == 'mathpix':
+            return model_class(
+                api_key=api_key,
+                temperature=temperature,
+                system_prompt=system_prompt
+            )
+        else:
+            # 其他模型仅传递标准参数
+            return model_class(
+                api_key=api_key,
+                temperature=temperature,
+                system_prompt=system_prompt,
+                language=language
+            )
 
     @classmethod
     def get_available_models(cls) -> list[Dict[str, Any]]: