更新WebUI

README.md

@@ -1,14 +1,6 @@
 # AI 作文批改助手 ✨
 
-(＾▽＾)ﾉﾞ 欢迎使用 AI 作文批改助手！这是一款专为教育工作者和学生设计的智能桌面工具，能够像经验丰富的英语老师一样，自动批改手写英文作文图片，并生成专业详细的批改报告。
+(＾▽＾)ﾉﾞ 欢迎使用 AI 作文批改助手！这是一款专为教育工作者和学生设计的本地 Web 应用，能够像经验丰富的英语老师一样，自动批改手写英文作文图片，并生成专业详细的批改报告。
-
-## 🎯 应用界面预览
-![应用界面](photo/1.png)
-![设置界面](photo/2.png)
-![批改过程](photo/3.png)
-![批改报告](photo/4.png)
-
----
 
 ## ✨ 核心特色功能
 
@@ -44,19 +36,26 @@
 
 ### 快速开始
 1. **下载程序**: 前往 [Releases页面](https://github.com/Eric-Terminal/Pro_llm_correct/releases) 下载最新版本
-2. **首次配置**: 
+2. **启动 Web UI**:
-   - 运行程序，自动弹出设置窗口
+   - 在终端运行 `python3 main.py`
-   - 配置VLM和LLM服务的URL、API密钥和模型名称
+   - 程序会从 4567 端口起寻找可用端口，并自动打开浏览器访问 Web 界面
-   - 点击确定保存，密钥自动加密存储
+3. **配置服务**:
-3. **开始批改**:
+   - 通过顶部导航切换到“服务设置”页，填写 VLM/LLM 的 URL、API Key、模型名称等参数
-   - 在主界面输入作文题目
+   - 可自定义 Prompt 模板、并发数量、重试策略与输出目录
-   - 点击"选择图片"，多选需要批改的作文图片
+   - 密钥字段不会回显；若提示“已保存”，留空即可沿用原值，输入新值即可覆盖
-   - 点击"开始批改"，程序自动进行并发处理
+   - 点击“保存设置”即可持久化到本地 `config.json`（密钥自动加密）
-4. **查看报告**: 处理完成后，Markdown和HTML格式报告自动保存在原图片目录
+4. **上传批改**:
+   - Web 首页默认停留在“批改作文”页，在表单中输入作文题目或场景说明
+   - 上传需要批改的作文照片（支持多选）
+   - 点击“开始批改”，浏览器会实时显示每个文件的处理状态与日志
+5. **查看报告**:
+   - 所有输出默认保存在 `output_reports/<时间戳>/` 目录
+   - 结果卡片中提供 Markdown/HTML 链接，可直接在浏览器查看或下载
 
 ### 输出文件说明
-- `原文件名_report.md`: Markdown格式详细批改报告
+- 默认保存在 `output_reports/<时间戳>/` 目录
-- `原文件名_report.html`: HTML可视化批改报告
+- `原文件名_report.md`: Markdown 格式详细批改报告
+- `原文件名_report.html`: HTML 可视化批改报告
 - 包含: 作文内容、综合评价、亮点优点、问题建议、分数评估
 
 ---
@@ -76,9 +75,10 @@ source venv/bin/activate  # Linux/Mac
 
 # 3. 安装依赖
 pip install -r requirements.txt
+# 需要确保系统已安装 curl（macOS/Linux 默认自带，Windows 可安装 Git Bash 或使用 WSL）
 
 # 4. 运行程序
-python main.py
+python3 main.py
 ```
 
 ### 项目打包
@@ -90,11 +90,11 @@ pyinstaller --noconsole --onefile main.py
 ```
 
 ### 技术架构
-- **前端**: Tkinter GUI界面
+- **前端**: Flask Web 服务 + 原生 HTML/CSS（玻璃拟态苹果风界面）
 - **核心**: 双AI引擎架构 (VLM + LLM)
-- **安全**: cryptography加密库
+- **安全**: cryptography 加密存储配置
-- **并发**: threading + concurrent.futures
+- **并发**: threading + concurrent.futures.ThreadPoolExecutor
-- **输出**: Markdown + HTML渲染
+- **输出**: Markdown/HTML 报告（内置样式渲染器）
 
 ---
 
@@ -113,6 +113,7 @@ pyinstaller --noconsole --onefile main.py
 - `MaxWorkers`: 最大并发数（默认4）
 - `MaxRetries`: 最大重试次数（默认3）
 - `RetryDelay`: 重试延迟秒数（默认5）
+- `RequestTimeout`: 单次 API 请求超时时长（秒，默认120）
 - `SaveMarkdown`: 是否保存Markdown文件（默认True）
 - `RenderMarkdown`: 是否渲染HTML报告（默认True）
 

api_services.py

@@ -8,9 +8,9 @@ from markdown_renderer import create_markdown_renderer
 import os
 import mimetypes
 import re
-from openai import OpenAI
 import time
 import logging
+import subprocess
 
 # 定义默认的LLM Prompt模板。使用`.format()`方法进行后续的动态填充。
 DEFAULT_LLM_PROMPT_TEMPLATE = """# ESSAY TOPIC
@@ -105,6 +105,8 @@ class ApiService:
         """将日志消息放入UI队列。"""
         if self.ui_queue:
             self.ui_queue.put(("log", message))
+        else:
+            logging.info(message)
 
     def _encode_image_to_base64_url(self, image_path: str) -> str:
         """将本地图片文件编码为Base64数据URL。"""
@@ -117,6 +119,89 @@ class ApiService:
             encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
         return f"data:{mime_type};base64,{encoded_string}"
 
+    def _chat_endpoint(self, base_url: Optional[str]) -> str:
+        if not base_url:
+            raise ValueError("服务地址未配置，请先在设置中填写 API Base URL")
+        return base_url.rstrip('/') + "/chat/completions"
+
+    def _usage_from_response(self, response_json: Dict[str, Any]) -> Dict[str, int]:
+        usage = response_json.get("usage") or {}
+        return {
+            "prompt_tokens": int(usage.get("prompt_tokens", 0) or 0),
+            "completion_tokens": int(usage.get("completion_tokens", 0) or 0),
+        }
+
+    def _post_json_with_curl(self, endpoint: str, api_key: Optional[str], payload: Dict[str, Any], timeout: float) -> Dict[str, Any]:
+        data_str = json.dumps(payload, ensure_ascii=False)
+        command = [
+            "curl",
+            "-sS",
+            "-X",
+            "POST",
+            endpoint,
+            "-H",
+            "Content-Type: application/json",
+            "--data-binary",
+            "@-",
+            "-w",
+            "\nHTTP_STATUS:%{http_code}",
+            "--max-time",
+            str(max(timeout, 1.0)),
+        ]
+        if api_key:
+            command.extend(["-H", f"Authorization: Bearer {api_key}"])
+
+        completed = subprocess.run(command, capture_output=True, text=True, input=data_str)
+        stdout = completed.stdout or ""
+        stderr = completed.stderr.strip()
+
+        status_code = None
+        if "HTTP_STATUS:" in stdout:
+            stdout, status_part = stdout.rsplit("HTTP_STATUS:", 1)
+            try:
+                status_code = int(status_part.strip())
+            except ValueError:
+                status_code = None
+
+        response_text = stdout.strip()
+
+        if completed.returncode != 0 or (status_code and status_code >= 400):
+            error_message = response_text or stderr or f"curl exited with code {completed.returncode}"
+            raise RuntimeError(f"调用失败 (HTTP {status_code}): {error_message}")
+
+        if not response_text:
+            return {}
+
+        try:
+            return json.loads(response_text)
+        except json.JSONDecodeError as exc:
+            raise ValueError(f"无法解析 API 返回的 JSON: {response_text[:500]}") from exc
+
+    def _invoke_chat_completion(
+        self,
+        label: str,
+        base_url: Optional[str],
+        api_key: Optional[str],
+        payload: Dict[str, Any],
+        max_retries: int,
+        retry_delay: int,
+        timeout: float,
+    ) -> Dict[str, Any]:
+        endpoint = self._chat_endpoint(base_url)
+        last_error: Optional[Exception] = None
+        for attempt in range(max_retries):
+            try:
+                return self._post_json_with_curl(endpoint, api_key, payload, timeout)
+            except Exception as exc:  # pylint: disable=broad-except
+                last_error = exc
+                if attempt == max_retries - 1:
+                    raise
+                self._log(f"{label} 调用失败，{retry_delay}秒后重试... (尝试 {attempt + 1}/{max_retries})，错误: {exc}")
+                time.sleep(retry_delay)
+        if last_error:
+            raise last_error
+        raise RuntimeError(f"{label} 调用失败：未知错误")
+
     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：
@@ -131,19 +216,12 @@ class ApiService:
         except (ValueError, TypeError):
             max_retries = 3
             retry_delay = 5
-        
+
-        for attempt in range(max_retries):
+        try:
-            try:
+            request_timeout = float(self.config.get("RequestTimeout", 120))
-                vlm_client = OpenAI(
+        except (ValueError, TypeError):
-                    api_key=self.config.get("VlmApiKey"),
+            request_timeout = 120.0
-                    base_url=self.config.get("VlmUrl")
+
-                )
-                break
-            except Exception as e:
-                if attempt == max_retries - 1:
-                    raise
-                self._log(f"VLM客户端创建失败，{retry_delay}秒后重试... (尝试 {attempt + 1}/{max_retries})")
-                time.sleep(retry_delay)
         base64_image_url = self._encode_image_to_base64_url(file_path)
 
         vlm_prompt = """# ROLE
@@ -173,21 +251,28 @@ Strictly adhere to the following format. Do not output anything else.
         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", "Pro/THUDM/GLM-4.1V-9B-Thinking")
-        for attempt in range(max_retries):
+        vlm_payload = {
-            try:
+            "model": vlm_model,
-                vlm_response = vlm_client.chat.completions.create(model=vlm_model, messages=vlm_messages, max_tokens=4096, temperature=1)
+            "messages": vlm_messages,
-                vlm_output = vlm_response.choices[0].message.content or ""
+            "max_tokens": 4096,
-                break
+            "temperature": 1,
-            except Exception as e:
-                if attempt == max_retries - 1:
-                    raise
-                self._log(f"VLM调用失败，{retry_delay}秒后重试... (尝试 {attempt + 1}/{max_retries})")
-                time.sleep(retry_delay)
-        
-        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,
         }
+        vlm_response_json = self._invoke_chat_completion(
+            "VLM",
+            self.config.get("VlmUrl"),
+            self.config.get("VlmApiKey"),
+            vlm_payload,
+            max_retries,
+            retry_delay,
+            request_timeout,
+        )
+
+        choices = vlm_response_json.get("choices") or []
+        if not choices:
+            raise ValueError(f"VLM 未返回 choices，响应：{vlm_response_json}")
+        vlm_output = choices[0].get("message", {}).get("content") or ""
+
+        vlm_usage = self._usage_from_response(vlm_response_json)
         
         # 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)
@@ -207,19 +292,6 @@ Strictly adhere to the following format. Do not output anything else.
             raise ValueError(f"VLM未能按预期格式返回，无法解析文本。模型返回：\n{vlm_output}")
 
         # --- 步骤 2: 调用LLM生成批改报告 ---
-        for attempt in range(max_retries):
-            try:
-                llm_client = OpenAI(
-                    api_key=self.config.get("LlmApiKey"),
-                    base_url=self.config.get("LlmUrl")
-                )
-                break
-            except Exception as e:
-                if attempt == max_retries - 1:
-                    raise
-                self._log(f"LLM客户端创建失败，{retry_delay}秒后重试... (尝试 {attempt + 1}/{max_retries})")
-                time.sleep(retry_delay)
-        
         # 从配置加载Prompt模板，若用户未定义则使用默认模板
         prompt_template = self.config.get("LlmPromptTemplate")
         if not prompt_template:
@@ -235,23 +307,35 @@ 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", "moonshotai/Kimi-K2-Instruct")
-        for attempt in range(max_retries):
+        llm_payload = {
-            try:
+            "model": llm_model,
-                llm_response = llm_client.chat.completions.create(model=llm_model, messages=llm_messages, temperature=1, max_tokens=16384)
+            "messages": llm_messages,
-                final_report = llm_response.choices[0].message.content or "错误：AI未能生成报告。"
+            "temperature": 1,
-                break
+            "max_tokens": 16384,
-            except Exception as e:
-                if attempt == max_retries - 1:
-                    final_report = f"错误：AI生成报告失败（达到最大重试次数 {max_retries} 次）"
-                else:
-                    self._log(f"LLM调用失败，{retry_delay}秒后重试... (尝试 {attempt + 1}/{max_retries})")
-                    time.sleep(retry_delay)
-
-        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,
         }
 
+        final_report: str
+        try:
+            llm_response_json = self._invoke_chat_completion(
+                "LLM",
+                self.config.get("LlmUrl"),
+                self.config.get("LlmApiKey"),
+                llm_payload,
+                max_retries,
+                retry_delay,
+                request_timeout,
+            )
+            llm_choices = llm_response_json.get("choices") or []
+            if not llm_choices:
+                raise ValueError(f"LLM 未返回 choices，响应：{llm_response_json}")
+            final_report = llm_choices[0].get("message", {}).get("content") or "错误：AI未能生成报告。"
+        except Exception as exc:
+            self._log(f"LLM 调用失败：{exc}")
+            final_report = f"错误：AI生成报告失败（{exc}）"
+            llm_response_json = {}
+
+        llm_usage = self._usage_from_response(llm_response_json)
+
         # 渲染Markdown为HTML（如果配置开启）
         html_path = None
         if self.markdown_renderer:
@@ -286,4 +370,4 @@ def check_for_updates(current_version_str: str) -> Optional[str]:
                     return latest_version_name
     except Exception as e:
         logging.error(f"Failed to check for updates: {e}")
-    return None
+    return None

app_ui.py

@@ -9,12 +9,11 @@ import logging
 import webbrowser
 from config_manager import ConfigManager
 from api_services import ApiService, DEFAULT_LLM_PROMPT_TEMPLATE, check_for_updates
+from version import CURRENT_VERSION
 
 # 配置日志记录器
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-CURRENT_VERSION = "3.2.0"
-
 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, config_manager: ConfigManager):

config.json

@@ -1 +1,23 @@
-{}
+{
+    "__device_salt__": "c2FsdF9mb3JfbGxtX2FwcF9jb25maWdDMTdGUkFDUlE2TDQ=",
+    "__device_fingerprint__": "d9bddd980989cdac1e26fdb26d21fa098ea9d5f2dc2da062b069d268f8c9a1ff",
+    "__device_fingerprint_source__": "hardware",
+    "SaveMarkdown": true,
+    "RenderMarkdown": true,
+    "VlmUrl": "https://api.ericterminal.com/v1",
+    "VlmApiKey": "gAAAAABo-F2EZa6kKSdQNolAPQcvN5RMdyCcgJpQ2VFg0szZWFWfa7MQQsMWT7R6jKpSBgVxSCYuzEC2A-xcZqIdkY_rqdFSYzT0Uou84UKP0aqkTCNTJwd3wwetbYQTpcWtJpRuw6KrVnFtypcON4K7gXdV0biDEw==",
+    "VlmModel": "gemini-2.5-pro",
+    "LlmUrl": "https://api.ericterminal.com/v1",
+    "LlmApiKey": "gAAAAABo-F2E-b6xQWGj5DhJfTNlZkekVeidkYI9ZH7-bhPayqcbAkNVsc9NpgSFo5b1dfifT9cVirJeGjoxobSS6ewWJvDN0JgbZa2e1pZeo5Gsqvei3O2s4xOEk9kAsWej6WjynjFYHkBEz10sK53cCPHZI1RqAA==",
+    "LlmModel": "gemini-2.5-pro",
+    "OutputDirectory": "output_reports",
+    "MaxWorkers": 4,
+    "MaxRetries": 3,
+    "RetryDelay": 5,
+    "SensitivityFactor": 1.0,
+    "AutoUpdateCheck": true,
+    "UsageVlmInput": 645,
+    "UsageVlmOutput": 19,
+    "UsageLlmInput": 1385,
+    "UsageLlmOutput": 296
+}

main.py

@@ -1,9 +1,12 @@
-import tkinter as tk
-from app_ui import MainApp
-from config_manager import ConfigManager
-from api_services import ApiService
-import sys
 import os
+import socket
+import sys
+import threading
+import time
+import webbrowser
+
+from config_manager import ConfigManager
+from web_app import create_app
 
 
 def get_config_path():
@@ -21,17 +24,40 @@ def get_config_path():
         # 开发环境，使用当前目录
         return "config.json"
 
+def find_available_port(start: int = 4567, limit: int = 4667) -> int:
+    """Return the first free TCP port within the inclusive range."""
+    for port in range(start, limit + 1):
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+            try:
+                sock.bind(("127.0.0.1", port))
+                return port
+            except OSError:
+                continue
+    raise RuntimeError(f"无法在 {start}-{limit} 范围内找到可用端口")
+
+
+def open_browser_later(url: str, delay: float = 1.0) -> None:
+    """Open the default browser after a small delay."""
+
+    def _opener():
+        time.sleep(delay)
+        try:
+            webbrowser.open_new(url)
+        except Exception:
+            pass
+
+    threading.Thread(target=_opener, daemon=True).start()
+
+
 if __name__ == "__main__":
-    # 1. 初始化核心服务
-    # 使用合适的配置文件路径
     config_path = get_config_path()
     config_manager = ConfigManager(config_path)
+    app = create_app(config_manager)
 
-    # 2. 创建Tkinter主窗口
+    port = find_available_port()
-    root = tk.Tk()
+    url = f"http://127.0.0.1:{port}/"
-    
+    print(f"🚀 Web UI 已启动，访问: {url}")
-    # 3. 实例化主应用，服务将在MainApp内部创建
+    open_browser_later(url)
-    app = MainApp(root, config_manager)
 
-    # 4. 启动Tkinter事件循环
+    app.run(host="127.0.0.1", port=port, debug=False, use_reloader=False)
-    root.mainloop()

output_reports/20251022-124454/4K_report.md

@@ -0,0 +1,16 @@
+###【作文内容】
+*   **作文文本:** 
+### 【综合评价】
+同学，你提交的是一张白卷，这在考试中是绝对不允许的。一分不得的情况下，你将与其他考生拉开巨大的差距。希望这只是一个系统测试，在真正的考试中，请务必认真对待，写出你的想法。
+### 【亮点与优点】
+*   本次作文为空白卷，无任何优点。
+### 【问题与修改建议】
+*   **[问题1 - 未作答]:**
+    *   **原文句子:** "全文为空。"
+    *   **问题分析:** 考生没有书写任何内容，无法进行评分。在任何考试中，交白卷都意味着该题得分为0，这将对总成绩产生灾难性的影响。
+    *   **修改建议:** 务必审题并按照要求完成写作任务。即使语言表达不够完美，也要尽力尝试，争取拿到基本的分数。请记住，写了就有可能得分，不写一定是0分。
+### 【分数评估】
+*   **内容与语言分 (Content & Language):** 0 / 12
+*   **卷面与书写分 (Handwriting & Presentation):** 0 / 3
+*   ---
+*   **最终得分 (Final Score):** **0 / 15**

requirements.txt

@@ -1,4 +1,4 @@
 cryptography
-openai
+flask
 markdown
-packaging
+packaging

version.py

@@ -0,0 +1 @@
+CURRENT_VERSION = "4"