优化代码结构和注释

api_services.py

@@ -1,5 +1,5 @@
 import base64
-from typing import Dict, Any
+from typing import Dict, Any, Tuple
 from config_manager import ConfigManager
 import os
 import mimetypes
@@ -12,18 +12,45 @@ DEFAULT_LLM_PROMPT_TEMPLATE = """# ESSAY TOPIC
 
 # INSTRUCTIONS FOR AI (Process in English)
 ## 1. ROLE & GOAL
-You are a highly experienced senior high school English teacher. Your task is to provide a detailed, constructive, and encouraging evaluation of a student's essay.
+You are a highly experienced senior high school English teacher specializing in the Chinese National College Entrance Examination (Gaokao). Your goal is to provide a detailed, constructive, and encouraging evaluation of a student's essay, correctly identifying the essay type and applying the appropriate scoring standard.
+
 ## 2. INPUT DATA
-You will receive a quantitative `<wscore>` and the full `<text>` of the essay. The essay is based on the topic provided above.
+You will receive three pieces of data:
-## 3. GRADING LOGIC (Total Score: 15 points)
+- `<topic>`: For "Application Writing", this is the essay prompt. For "Read and Continue Writing", this is the initial story provided to the student.
-- **Content & Language (12 points):** Evaluate this based on grammar, vocabulary, sentence structure, etc., in relation to the essay topic.
+- `<wscore>`: A quantitative handwriting quality score from 0.0 to 1.0.
-- **Handwriting & Presentation (3 points):** Calculate the score by first getting a raw score (`Raw Score = wscore * 3`), and then rounding the `Raw Score` **up** to the nearest half-point (0.5).
+- `<text>`: The full text of the student's handwritten essay.
-    - *Rounding Logic Example:* A raw score of 2.49 becomes 2.5. A raw score of 2.51 becomes 3.0. A raw score of 2.50 remains 2.5. A score of 0 remains 0.
+
-## 4. FINAL TASK
+## 3. STEP 1: IDENTIFY ESSAY TYPE
-Analyze the text, calculate scores, and present your feedback in **Simplified Chinese** using the precise Markdown format specified below.
+First, you MUST determine which of the two following Gaokao essay types this is. This decision will change the total score.
+*   **TYPE A: Application Writing (应用文)**
+    *   **Clues:** The total word count of the student's `<text>` is shorter, typically around 80-100 words. The `<topic>` is a straightforward instruction (e.g., "Write a letter to...").
+    *   **Total Score:** 15 points.
+*   **TYPE B: Read and Continue Writing (读后续写)**
+    *   **Clues:** The total word count of the student's `<text>` is longer, typically around 150 words. The `<topic>` contains a substantial story. The student's `<text>` will consist of two distinct paragraphs, and the beginning of each paragraph will match the starting sentences provided in the original exam prompt.
+    *   **Total Score:** 25 points.
+
+## 4. STEP 2: APPLY SCORING LOGIC
+Based on the identified essay type, apply the corresponding grading logic. The Handwriting score calculation is the same for both.
+*   **Handwriting & Presentation Score (通用卷面分计算):**
+    *   This sub-score is always out of **3 points**.
+    *   **Calculation:** Get a raw score (`Raw Score = wscore * 3`). Then, round the `Raw Score` **up** to the nearest half-point (0.5).
+    *   **Rounding Example:** A raw score of 2.49 becomes 2.5. A raw score of 2.51 becomes 3.0. A score of 2.50 remains 2.5.
+
+*   **GRADING FOR TYPE A: Application Writing (Total 15)**
+    *   **Content & Language (12 points):** Evaluate grammar, vocabulary, sentence structure, and relevance to the topic.
+    *   **Handwriting & Presentation (3 points):** Use the calculation described above.
+    *   **Final Score:** (Content & Language Score) + (Handwriting Score) out of 15.
+
+*   **GRADING FOR TYPE B: Read and Continue Writing (Total 25)**
+    *   **Content & Language (22 points):** Evaluate the quality of the continuation. Key criteria include: coherence with the original story, logical plot development, character consistency, richness of detail, and advanced use of grammar, vocabulary, and sentence structures.
+    *   **Handwriting & Presentation (3 points):** Use the calculation described above.
+    *   **Final Score:** (Content & Language Score) + (Handwriting Score) out of 25.
+
+## 5. FINAL TASK
+Analyze the text, identify the essay type, calculate the scores, and present your complete feedback in **Simplified Chinese** using the precise Markdown format specified in the "OUTPUT SPECIFICATION" section. Ensure the final score correctly reflects the total points possible (15 or 25).
 #--- End of English Instructions ---
 # OUTPUT SPECIFICATION (MUST BE IN SIMPLIFIED CHINESE)
-# 请严格使用以下Markdown格式，并用简体中文填充所有内容，优点可以两个到三个，问题建议要把全部问题找出来并且解析，都要遵循类似格式。
+# 请严格使用以下Markdown格式，并用简体中文填充所有内容，优点找不到不要硬找，问题建议要把全部问题找出来并且解析，都要遵循类似格式。
 
 
 ###【作文内容】
@@ -76,11 +103,12 @@ class ApiService:
             encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
         return f"data:{mime_type};base64,{encoded_string}"
 
-    def process_essay_image(self, file_path: str, topic: str) -> str:
+    def process_essay_image(self, file_path: str, topic: str) -> Tuple[str, Dict[str, int], Dict[str, int]]:
         """
         执行完整的两步式作文批改流程：
         1. VLM调用：分析作文图片，提取手写文本和书写质量分数。
         2. LLM调用：基于VLM的输出和作文题目，生成详细的批改报告。
+        返回: (批改报告, VLM token使用情况, LLM token使用情况)
         """
         # --- 步骤 1: 调用VLM进行图像分析 ---
         vlm_client = OpenAI(
@@ -115,10 +143,15 @@ Strictly adhere to the following format. Do not output anything else.
 </text>"""
         vlm_messages = [{"role": "user", "content": [{"type": "text", "text": vlm_prompt}, {"type": "image_url", "image_url": {"url": base64_image_url}}]}]
         
-        vlm_model = self.config.get("VlmModel", "gemini-2.5-pro")
+        vlm_model = self.config.get("VlmModel", "Pro/THUDM/GLM-4.1V-9B-Thinking")
         vlm_response = vlm_client.chat.completions.create(model=vlm_model, messages=vlm_messages, max_tokens=4096, temperature=1)
         vlm_output = vlm_response.choices[0].message.content or ""
         
+        vlm_usage = {
+            "prompt_tokens": vlm_response.usage.prompt_tokens if vlm_response.usage else 0,
+            "completion_tokens": vlm_response.usage.completion_tokens if vlm_response.usage else 0,
+        }
+        
         # 解析VLM返回的XML格式输出，提取分数和文本
         wscore_match = re.search(r'<wscore>(.*?)</wscore>', vlm_output, re.DOTALL)
         text_match = re.search(r'<text>(.*?)</text>', vlm_output, re.DOTALL)
@@ -156,8 +189,13 @@ Strictly adhere to the following format. Do not output anything else.
         
         llm_messages = [{"role": "user", "content": final_llm_prompt}]
 
-        llm_model = self.config.get("LlmModel", "gemini-2.5-pro")
+        llm_model = self.config.get("LlmModel", "moonshotai/Kimi-K2-Instruct")
         llm_response = llm_client.chat.completions.create(model=llm_model, messages=llm_messages, temperature=1, max_tokens=16384)
         final_report = llm_response.choices[0].message.content or "错误：AI未能生成报告。"
 
-        return final_report
+        llm_usage = {
+            "prompt_tokens": llm_response.usage.prompt_tokens if llm_response.usage else 0,
+            "completion_tokens": llm_response.usage.completion_tokens if llm_response.usage else 0,
+        }
+
+        return final_report, vlm_usage, llm_usage

app_ui.py

@@ -10,7 +10,7 @@ from api_services import ApiService, DEFAULT_LLM_PROMPT_TEMPLATE
 
 class AboutDialog(tk.Toplevel):
     """“关于”对话框，展示应用信息，支持滚动查看。"""
-    def __init__(self, parent):
+    def __init__(self, parent, config_manager: ConfigManager):
         super().__init__(parent)
         self.transient(parent)
         self.title("关于 AI 作文批改助手")
@@ -32,7 +32,12 @@ class AboutDialog(tk.Toplevel):
         scrollbar.grid(row=0, column=1, sticky="ns")
         text_widget.config(yscrollcommand=scrollbar.set)
 
-        about_text = """
+        vlm_in = config_manager.get('UsageVlmInput', 0)
+        vlm_out = config_manager.get('UsageVlmOutput', 0)
+        llm_in = config_manager.get('UsageLlmInput', 0)
+        llm_out = config_manager.get('UsageLlmOutput', 0)
+
+        about_text = f"""
 欢迎使用 AI 作文批改助手！这是一款专为教育者和学生设计的智能工具，旨在利用前沿的人工智能技术，提供高效、精准、个性化的英文作文批改体验。
 
 核心亮点:
@@ -59,6 +64,13 @@ class AboutDialog(tk.Toplevel):
 作者: Eric_Terminal
 https://github.com/Eric-Terminal
 版本: 2.5
+
+---
+历史Token使用量 (仅供参考):
+- VLM 输入: {vlm_in:,}
+- VLM 输出: {vlm_out:,}
+- LLM 输入: {llm_in:,}
+- LLM 输出: {llm_out:,}
 """
         
         text_widget.insert("1.0", about_text)
@@ -166,8 +178,13 @@ class SettingsDialog(tk.Toplevel):
             "LlmModel": self.llm_model.get(),
             "SensitivityFactor": self.sensitivity_factor.get(),
             "MaxWorkers": self.max_workers.get(),
-            "LlmPromptTemplate": self.llm_prompt_text.get("1.0", "end-1c") # 从Text控件获取用户修改后的Prompt模板
+            "LlmPromptTemplate": self.llm_prompt_text.get("1.0", "end-1c")
         }
+
+        # 如果用户修改后的模板与默认模板内容一致，则不写入配置文件，以使用默认值
+        if self.result["LlmPromptTemplate"].strip() == DEFAULT_LLM_PROMPT_TEMPLATE.strip():
+            self.result["LlmPromptTemplate"] = None  # 使用 None 作为信号，表示应移除此配置项
+
         self.destroy()
 
     def on_close(self):
@@ -295,12 +312,19 @@ class MainApp:
             self.config_manager.config.pop("OcrApiKey", None)
             self.config_manager.config.pop("OcrSecretKey", None)
             for key, value in dialog.result.items():
+                if key == "LlmPromptTemplate":
+                    if value is None:
+                        # 如果值为None，表示用户希望恢复默认模板，因此从配置中移除该键
+                        self.config_manager.config.pop(key, None)
+                    else:
+                        self.config_manager.set(key, value)
+                else:
                     self.config_manager.set(key, value)
             self.config_manager.save()
 
     def _open_about_dialog(self):
         """创建并显示“关于”对话框。"""
-        AboutDialog(self.root)
+        AboutDialog(self.root, self.config_manager)
 
     def _open_file_dialog(self):
         """打开文件选择对话框，让用户选择一个或多个图片文件。"""
@@ -372,13 +396,25 @@ class MainApp:
         base_name = os.path.basename(file_path)
         self.ui_queue.put(("log", f"开始处理: {base_name}"))
         try:
-            final_report = self.api_service.process_essay_image(file_path, topic)
+            final_report, vlm_usage, llm_usage = self.api_service.process_essay_image(file_path, topic)
             
             report_filename = os.path.splitext(file_path)[0] + "_report.md"
             with open(report_filename, 'w', encoding='utf-8') as f:
                 f.write(final_report)
             
+            vlm_in = vlm_usage.get("prompt_tokens", 0)
+            vlm_out = vlm_usage.get("completion_tokens", 0)
+            llm_in = llm_usage.get("prompt_tokens", 0)
+            llm_out = llm_usage.get("completion_tokens", 0)
+
+            usage_log = f"Token用量: VLM(in:{vlm_in}, out:{vlm_out}), LLM(in:{llm_in}, out:{llm_out})"
             self.ui_queue.put(("log", f"完成批改: {base_name} -> {os.path.basename(report_filename)}"))
+            self.ui_queue.put(("log", usage_log))
+
+            # 加锁以保证线程安全地更新和保存配置
+            with self.lock:
+                self.config_manager.update_token_usage(vlm_in, vlm_out, llm_in, llm_out)
+                self.config_manager.save()
 
         except Exception as e:
             self.ui_queue.put(("log", f"文件: {base_name} 失败: {e}"))

config_manager.py

@@ -2,7 +2,7 @@ import json
 import os
 import base64
 import hashlib
-from typing import Dict, Optional, Tuple
+from typing import Dict, Optional, Tuple, Any
 from cryptography.fernet import Fernet, InvalidToken
 
 class ConfigManager:
@@ -17,7 +17,7 @@ class ConfigManager:
 
     def __init__(self, file_path: str = "config.json"):
         self.file_path = file_path
-        self.config: Dict[str, str] = {}
+        self.config: Dict[str, Any] = {}
         self._fernet: Optional[Fernet] = None
         self._initialize_encryption()
         self.load()
@@ -70,23 +70,30 @@ class ConfigManager:
         except IOError as e:
             print(f"保存配置失败: {e}")
 
-    def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
+    def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]:
         """获取指定键的配置值。如果键属于敏感信息，则自动解密后返回。"""
         value = self.config.get(key)
         if value is None:
             return default
         
         if key in self.SENSITIVE_KEYS:
-            return self._decrypt(value)
+            return self._decrypt(str(value))
         return value
 
-    def set(self, key: str, value: str):
+    def set(self, key: str, value: Any):
         """设置指定键的配置值。如果键属于敏感信息，则自动加密后存储。"""
         if key in self.SENSITIVE_KEYS:
-            self.config[key] = self._encrypt(value)
+            self.config[key] = self._encrypt(str(value))
         else:
             self.config[key] = value
 
+    def update_token_usage(self, vlm_input: int, vlm_output: int, llm_input: int, llm_output: int):
+        """累加本次API调用的token使用量到配置中。"""
+        self.config['UsageVlmInput'] = self.get('UsageVlmInput', 0) + vlm_input
+        self.config['UsageVlmOutput'] = self.get('UsageVlmOutput', 0) + vlm_output
+        self.config['UsageLlmInput'] = self.get('UsageLlmInput', 0) + llm_input
+        self.config['UsageLlmOutput'] = self.get('UsageLlmOutput', 0) + llm_output
+
     def check_settings(self) -> Tuple[bool, Optional[str]]:
         """
         检查所有必需的配置项是否都已设置。