import base64 from typing import Dict, Any, Tuple, Optional from config_manager import ConfigManager from markdown_renderer import create_markdown_renderer import os import mimetypes import re from openai import OpenAI import time import logging # 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 {topic} # INSTRUCTIONS FOR AI (Process in English) ## 1. ROLE & GOAL 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 three pieces of data: - ``: For "Application Writing", this is the essay prompt. For "Read and Continue Writing", this is the initial story provided to the student. - ``: A quantitative handwriting quality score from 0.0 to 1.0. - ``: The full text of the student's handwritten essay. ## 3. STEP 1: IDENTIFY ESSAY TYPE 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 `` is shorter, typically around 80-100 words. The `` 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 `` is longer, typically around 150 words. The `` contains a substantial story. The student's `` 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) #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 # 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)。 ###【作文内容】 * **作文文本:** [在此处粘贴完整的作文文本。] ### 【综合评价】 (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) ### 【亮点与优点】 * **(优点1):** [具体描述作文内容或语言上的一个亮点。] * **(优点2):** [具体描述另一个优点。] * **(优点3):（以此类推，不限制数量，但建议控制在3个以内。） ### 【问题与修改建议】 * **[问题1 - 语法/拼写错误]:** * **原文句子:** "[引用出现错误的原文句子]" * **问题分析:** [简要说明错误类型。] * **修改建议:** "[写出修改后的正确句子]" * **[问题2 - 表达/逻辑]:** * **原文句子:** "[引用表达欠佳的原文句子]" * **问题分析:** [说明问题所在。] * **修改建议:** "[提供一个更好的表达方式。] * **[问题3 - （以此类推，不限制数量，但建议控制在3个以内。）]:** * **原文句子:** "[说明问题所在。]" * **问题分析:** [说明问题所在。] ### 【分数评估】 * **内容与语言分 (Content & Language):** [分数] / 12 * **卷面与书写分 (Handwriting & Presentation):** [分数] / 3 * --- * **最终得分 (Final Score):** **[总分] / 15** # INPUT DATA FOR THIS TASK {wscore} {essay_text} """ class ApiService: """封装了与外部API（VLM和LLM）交互的所有逻辑。""" def __init__(self, config_manager: ConfigManager, ui_queue: Optional[Any] = None): self.config = config_manager self.ui_queue = ui_queue self.markdown_renderer = create_markdown_renderer(config_manager) def _log(self, message: str): """将日志消息放入UI队列。""" if self.ui_queue: self.ui_queue.put(("log", 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。""" if not os.path.exists(image_path): raise FileNotFoundError(f"Image file not found at: {image_path}") mime_type, _ = mimetypes.guess_type(image_path) if not mime_type or not mime_type.startswith('image'): raise ValueError(f"File is not a recognizable image type: {mime_type}") with open(image_path, "rb") as image_file: 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) -> 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 --- try: max_retries = int(self.config.get("MaxRetries", 3)) retry_delay = int(self.config.get("RetryDelay", 5)) except (ValueError, TypeError): max_retries = 3 retry_delay = 5 for attempt in range(max_retries): try: vlm_client = OpenAI( api_key=self.config.get("VlmApiKey"), 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 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. # TASK Analyze the handwriting quality and extract all text from the image. ## 1. Handwriting Quality Analysis: - Critically evaluate the handwriting on a continuous scale from 0.0 to 1.0. - The scoring must be stringent. A score of 1.0 is reserved for flawless, machine-printed-like perfection, which is virtually unattainable. - **Score Tiers:** - **0.90-0.99:** Near-perfect, professional calligrapher level. Extremely rare. - **0.80-0.89:** Excellent, clear, consistent, and aesthetically pleasing. The best a top student can achieve. - **0.70-0.79:** Good and very legible, but with minor inconsistencies in size or spacing. - **0.60-0.69:** Clear and legible, but with noticeable inconsistencies. - **Below 0.60:** Legibility is impacted. - Output this score enclosed in a single XML tag. ## 2. Full Text Extraction: - Perform a high-accuracy OCR on the entire image. - Preserve the original line breaks and paragraph structure as best as possible. - Output the full extracted text enclosed in a single XML tag. # OUTPUT FORMAT Strictly adhere to the following format. Do not output anything else. [Your calculated score, e.g., 0.85] [The full extracted text from the image goes here.] """ 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): try: 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 "" break 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, } # 解析VLM返回的XML格式输出，提取分数和文本 wscore_match = re.search(r'(.*?)', vlm_output, re.DOTALL) text_match = re.search(r'(.*?)', vlm_output, re.DOTALL) original_wscore = float(wscore_match.group(1).strip()) if wscore_match else 0.0 essay_text = text_match.group(1).strip() if text_match else "错误：无法从图片中提取文本。" try: sensitivity_factor = float(self.config.get("SensitivityFactor", "1.0")) except (ValueError, TypeError): # 如果配置的敏感度因子无效，则使用默认值1.0 sensitivity_factor = 1.0 wscore = original_wscore ** sensitivity_factor if not text_match: 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: prompt_template = DEFAULT_LLM_PROMPT_TEMPLATE # 使用作文题目、书写分数和识别出的文本填充Prompt模板 final_llm_prompt = prompt_template.format( topic=topic, wscore=wscore, essay_text=essay_text ) llm_messages = [{"role": "user", "content": final_llm_prompt}] llm_model = self.config.get("LlmModel", "moonshotai/Kimi-K2-Instruct") for attempt in range(max_retries): try: 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。" break 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, } # 渲染Markdown为HTML（如果配置开启） html_path = None if self.markdown_renderer: # 定义HTML报告的文件名 report_base_name = os.path.splitext(file_path)[0] html_output_path = f"{report_base_name}_report.html" html_path = self.markdown_renderer.render_markdown_to_html_file(final_report, html_output_path) if html_path: self._log(f"已生成HTML报告: {os.path.basename(html_path)}") return final_report, vlm_usage, llm_usage, html_path