366 lines
18 KiB
Python
366 lines
18 KiB
Python
import base64
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import json
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import logging
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import mimetypes
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import os
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import re
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import time
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import urllib.request
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from typing import Any, Dict, Optional, Tuple
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from openai import OpenAI, OpenAIError
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from packaging import version
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from config_manager import ConfigManager
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from markdown_renderer import create_markdown_renderer
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# 定义默认的LLM Prompt模板。使用`.format()`方法进行后续的动态填充。
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DEFAULT_LLM_PROMPT_TEMPLATE = """# ESSAY TOPIC
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{topic}
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# INSTRUCTIONS FOR AI (Process in English)
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## 1. ROLE & GOAL
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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.
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## 2. INPUT DATA
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You will receive three pieces of data:
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- `<topic>`: For "Application Writing", this is the essay prompt. For "Read and Continue Writing", this is the initial story provided to the student.
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- `<wscore>`: A quantitative handwriting quality score from 0.0 to 1.0.
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- `<text>`: The full text of the student's handwritten essay.
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## 3. STEP 1: IDENTIFY ESSAY TYPE
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First, you MUST determine which of the two following Gaokao essay types this is. This decision will change the total score.
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* **TYPE A: Application Writing (应用文)**
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* **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...").
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* **Total Score:** 15 points.
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* **TYPE B: Read and Continue Writing (读后续写)**
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* **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.
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* **Total Score:** 25 points.
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## 4. STEP 2: APPLY SCORING LOGIC
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Based on the identified essay type, apply the corresponding grading logic. The Handwriting score calculation is the same for both.
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* **Handwriting & Presentation Score (通用卷面分计算):**
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* This sub-score is always out of **3 points**.
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* **Calculation:** Get a raw score (`Raw Score = wscore * 3`). Then, round the `Raw Score` **up** to the nearest half-point (0.5).
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* **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.
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* **GRADING FOR TYPE A: Application Writing (Total 15)**
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* **Content & Language (12 points):** Evaluate grammar, vocabulary, sentence structure, and relevance to the topic.
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* **Handwriting & Presentation (3 points):** Use the calculation described above.
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* **Final Score:** (Content & Language Score) + (Handwriting Score) out of 15.
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* **GRADING FOR TYPE B: Read and Continue Writing (Total 25)**
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* **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.
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* **Handwriting & Presentation (3 points):** Use the calculation described above.
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* **Final Score:** (Content & Language Score) + (Handwriting Score) out of 25.
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## 5. FINAL TASK
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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).
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#--- End of English Instructions ---
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# OUTPUT SPECIFICATION (MUST BE IN SIMPLIFIED CHINESE)
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#你应该综合考量书写和内容的评分，内容是主要的，字体是次要的，例如对于写的内容和作文毫无关系但是字很好看的，不应该给3分而是直接给0分
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# 请使用以下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。对于分数的总分则必须由你选择是15分还是25分(不一定是下面的15分)。
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###【作文内容】
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* **作文文本:** [在此处粘贴完整的作文文本。]
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### 【综合评价】
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(在此处用一两句鼓励性的话，对本次作文进行总体概述。如果写的太烂了也可以骂人)
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### 【亮点与优点】
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* **(优点1):** [具体描述作文内容或语言上的一个亮点。]
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* **(优点2):** [具体描述另一个优点。]
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* **(优点3):（以此类推，不限制数量，但建议控制在3个以内。）
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### 【问题与修改建议】
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* **[问题1 - 语法/拼写错误]:**
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* **原文句子:** "[引用出现错误的原文句子]"
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* **问题分析:** [简要说明错误类型。]
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* **修改建议:** "[写出修改后的正确句子]"
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* **[问题2 - 表达/逻辑]:**
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* **原文句子:** "[引用表达欠佳的原文句子]"
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* **问题分析:** [说明问题所在。]
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* **修改建议:** "[提供一个更好的表达方式。]
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* **[问题3 - （以此类推，不限制数量，但建议控制在3个以内。）]:**
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* **原文句子:** "[说明问题所在。]"
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* **问题分析:** [说明问题所在。]
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### 【分数评估】
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* **内容与语言分 (Content & Language):** [分数] / 12
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* **卷面与书写分 (Handwriting & Presentation):** [分数] / 3
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* ---
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* **最终得分 (Final Score):** **[总分] / 15**
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# INPUT DATA FOR THIS TASK
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<wscore>{wscore}</wscore>
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<text>
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{essay_text}
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</text>
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"""
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class ApiService:
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"""封装了与外部API（VLM和LLM）交互的所有逻辑。"""
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def __init__(self, config_manager: ConfigManager, ui_queue: Optional[Any] = None):
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self.config = config_manager
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self.ui_queue = ui_queue
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self.markdown_renderer = create_markdown_renderer(config_manager)
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self.logger = logging.getLogger("essay_corrector.api")
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def _log(self, message: str):
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"""将日志消息放入UI队列。"""
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self.logger.info(message)
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if self.ui_queue:
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self.ui_queue.put(("log", message))
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def _encode_image_to_base64_url(self, image_path: str) -> str:
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"""将本地图片文件编码为Base64数据URL。"""
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"Image file not found at: {image_path}")
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mime_type, _ = mimetypes.guess_type(image_path)
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if not mime_type or not mime_type.startswith('image'):
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raise ValueError(f"File is not a recognizable image type: {mime_type}")
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with open(image_path, "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
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return f"data:{mime_type};base64,{encoded_string}"
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def _chat_endpoint(self, base_url: Optional[str]) -> str:
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if not base_url:
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raise ValueError("服务地址未配置，请先在设置中填写 API Base URL")
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return base_url.rstrip("/")
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def _usage_from_response(self, response_json: Dict[str, Any]) -> Dict[str, int]:
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usage = response_json.get("usage") or {}
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return {
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"prompt_tokens": int(usage.get("prompt_tokens", 0) or 0),
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"completion_tokens": int(usage.get("completion_tokens", 0) or 0),
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}
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def _create_openai_client(self, base_url: str, api_key: Optional[str], timeout: float) -> OpenAI:
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client_kwargs: Dict[str, Any] = {
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"base_url": base_url.rstrip("/"),
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"timeout": max(timeout, 1.0),
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"max_retries": 0,
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}
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if api_key:
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client_kwargs["api_key"] = api_key
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return OpenAI(**client_kwargs)
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def _invoke_chat_completion(
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self,
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label: str,
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base_url: Optional[str],
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api_key: Optional[str],
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payload: Dict[str, Any],
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max_retries: int,
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retry_delay: int,
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timeout: float,
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) -> Dict[str, Any]:
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normalized_base_url = self._chat_endpoint(base_url)
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endpoint = f"{normalized_base_url}/chat/completions"
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client = self._create_openai_client(normalized_base_url, api_key, timeout)
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last_error: Optional[Exception] = None
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for attempt in range(max_retries):
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try:
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model = payload.get("model")
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self._log(f"{label} 请求: endpoint={endpoint}, model={model}")
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response = client.chat.completions.create(**payload)
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response_json = response.model_dump()
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trimmed = json.dumps(response_json, ensure_ascii=False)
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if len(trimmed) > 800:
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trimmed = trimmed[:797] + "..."
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self._log(f"{label} 响应: {trimmed}")
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return response_json
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except OpenAIError as exc:
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last_error = exc
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error_message = str(exc)
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except Exception as exc: # pylint: disable=broad-except
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last_error = exc
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error_message = str(exc)
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if attempt == max_retries - 1:
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raise last_error
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self._log(f"{label} 调用失败，{retry_delay}秒后重试... (尝试 {attempt + 1}/{max_retries})，错误: {error_message}")
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time.sleep(retry_delay)
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if last_error:
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raise last_error
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raise RuntimeError(f"{label} 调用失败：未知错误")
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def process_essay_image(self, file_path: str, topic: str) -> Tuple[str, Dict[str, int], Dict[str, int]]:
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"""
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执行完整的两步式作文批改流程：
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1. VLM调用：分析作文图片，提取手写文本和书写质量分数。
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2. LLM调用：基于VLM的输出和作文题目，生成详细的批改报告。
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返回: (批改报告, VLM token使用情况, LLM token使用情况)
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"""
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# --- 步骤 1: 调用VLM进行图像分析 ---
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try:
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max_retries = int(self.config.get("MaxRetries", 3))
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retry_delay = int(self.config.get("RetryDelay", 5))
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except (ValueError, TypeError):
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max_retries = 3
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retry_delay = 5
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try:
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request_timeout = float(self.config.get("RequestTimeout", 120))
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except (ValueError, TypeError):
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request_timeout = 120.0
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try:
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vlm_temperature = float(self.config.get("VlmTemperature", 0.0))
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except (ValueError, TypeError):
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vlm_temperature = 0.0
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vlm_temperature = min(max(vlm_temperature, 0.0), 2.0)
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base64_image_url = self._encode_image_to_base64_url(file_path)
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vlm_prompt = """# ROLE
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You are a high-precision OCR (Optical Character Recognition) and handwriting analysis engine. Your only job is to analyze the provided image and output structured data. Do not add any conversational text or explanations.
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# TASK
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Analyze the handwriting quality and extract all text from the image.
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## 1. Handwriting Quality Analysis:
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- Critically evaluate the handwriting on a continuous scale from 0.0 to 1.0.
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- The scoring must be stringent. A score of 1.0 is reserved for flawless, machine-printed-like perfection, which is virtually unattainable.
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- **Score Tiers:**
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- **0.90-0.99:** Near-perfect, professional calligrapher level. Extremely rare.
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- **0.80-0.89:** Excellent, clear, consistent, and aesthetically pleasing. The best a top student can achieve.
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- **0.70-0.79:** Good and very legible, but with minor inconsistencies in size or spacing.
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- **0.60-0.69:** Clear and legible, but with noticeable inconsistencies.
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- **Below 0.60:** Legibility is impacted.
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- Output this score enclosed in a single <wscore> XML tag.
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## 2. Full Text Extraction:
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- Perform a high-accuracy OCR on the entire image.
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- Preserve the original line breaks and paragraph structure as best as possible.
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- Output the full extracted text enclosed in a single <text> XML tag.
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# OUTPUT FORMAT
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Strictly adhere to the following format. Do not output anything else.
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<wscore>[Your calculated score, e.g., 0.85]</wscore>
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<text>
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[The full extracted text from the image goes here.]
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</text>"""
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vlm_messages = [{"role": "user", "content": [{"type": "text", "text": vlm_prompt}, {"type": "image_url", "image_url": {"url": base64_image_url}}]}]
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vlm_model = self.config.get("VlmModel", "Qwen/Qwen3-VL-235B-A22B-Instruct")
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vlm_payload = {
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"model": vlm_model,
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"messages": vlm_messages,
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"max_tokens": 4096,
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"temperature": vlm_temperature,
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}
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vlm_response_json = self._invoke_chat_completion(
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"VLM",
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self.config.get("VlmUrl"),
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self.config.get("VlmApiKey"),
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vlm_payload,
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max_retries,
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retry_delay,
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request_timeout,
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)
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choices = vlm_response_json.get("choices") or []
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if not choices:
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raise ValueError(f"VLM 未返回 choices，响应：{vlm_response_json}")
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vlm_output = choices[0].get("message", {}).get("content") or ""
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vlm_usage = self._usage_from_response(vlm_response_json)
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# 解析VLM返回的XML格式输出，提取分数和文本
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wscore_match = re.search(r'<wscore>(.*?)</wscore>', vlm_output, re.DOTALL)
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text_match = re.search(r'<text>(.*?)</text>', vlm_output, re.DOTALL)
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original_wscore = float(wscore_match.group(1).strip()) if wscore_match else 0.0
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essay_text = text_match.group(1).strip() if text_match else "错误：无法从图片中提取文本。"
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try:
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sensitivity_factor = float(self.config.get("SensitivityFactor", "1.0"))
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except (ValueError, TypeError):
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# 如果配置的敏感度因子无效，则使用默认值1.0
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sensitivity_factor = 1.0
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wscore = original_wscore ** sensitivity_factor
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if not text_match:
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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}")
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# --- 步骤 2: 调用LLM生成批改报告 ---
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# 从配置加载Prompt模板，若用户未定义则使用默认模板
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prompt_template = self.config.get("LlmPromptTemplate")
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if not prompt_template:
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prompt_template = DEFAULT_LLM_PROMPT_TEMPLATE
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# 使用作文题目、书写分数和识别出的文本填充Prompt模板
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final_llm_prompt = prompt_template.format(
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topic=topic,
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wscore=wscore,
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essay_text=essay_text
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)
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llm_messages = [{"role": "user", "content": final_llm_prompt}]
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try:
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llm_temperature = float(self.config.get("LlmTemperature", 0.0))
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except (ValueError, TypeError):
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llm_temperature = 0.0
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llm_temperature = min(max(llm_temperature, 0.0), 2.0)
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llm_model = self.config.get("LlmModel", "Qwen/Qwen3-VL-235B-A22B-Instruct")
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llm_payload = {
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"model": llm_model,
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"messages": llm_messages,
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"temperature": llm_temperature,
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"max_tokens": 4096,
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}
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final_report: str
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try:
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llm_response_json = self._invoke_chat_completion(
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"LLM",
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self.config.get("LlmUrl"),
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self.config.get("LlmApiKey"),
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llm_payload,
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max_retries,
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retry_delay,
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request_timeout,
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)
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llm_choices = llm_response_json.get("choices") or []
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if not llm_choices:
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raise ValueError(f"LLM 未返回 choices，响应：{llm_response_json}")
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final_report = llm_choices[0].get("message", {}).get("content") or "错误：AI未能生成报告。"
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except Exception as exc:
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self._log(f"LLM 调用失败：{exc}")
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final_report = f"错误：AI生成报告失败（{exc}）"
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llm_response_json = {}
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llm_usage = self._usage_from_response(llm_response_json)
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# 渲染Markdown为HTML（如果配置开启）
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html_path = None
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if self.markdown_renderer:
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# 定义HTML报告的文件名
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report_base_name = os.path.splitext(file_path)[0]
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html_output_path = f"{report_base_name}_report.html"
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html_path = self.markdown_renderer.render_markdown_to_html_file(final_report, html_output_path)
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if html_path:
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self._log(f"已生成HTML报告: {os.path.basename(html_path)}")
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return final_report, vlm_usage, llm_usage, html_path
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def check_for_updates(current_version_str: str) -> Optional[str]:
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"""
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Checks for new releases on GitHub.
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Returns the new version tag if an update is available, otherwise None.
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"""
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try:
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url = "https://api.github.com/repos/Eric-Terminal/Pro_llm_correct/releases/latest"
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# Add a user-agent to avoid being blocked
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req = urllib.request.Request(url, headers={'User-Agent': 'Pro_llm_correct-Update-Checker'})
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with urllib.request.urlopen(req) as response:
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if response.status == 200:
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data = json.loads(response.read().decode())
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# 根据您的调试反馈，我们现在读取 'name' 字段来获取版本号
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latest_version_name = data.get("name", "v0.0.0")
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# Use packaging.version for robust comparison
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if version.parse(latest_version_name) > version.parse(current_version_str):
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return latest_version_name
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except Exception as e:
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logging.error(f"Failed to check for updates: {e}")
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return None
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