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NovelConfig.py

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+"""
+小说生成器配置文件
+包含各种高级配置选项和预设模板
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Any, Optional
+
+@dataclass
+class AdvancedNovelConfig:
+    """高级小说生成配置"""
+    # 基础配置
+    genre: str
+    target_total_chars: int
+    model: str = "qwen-turbo"
+    temperature: float = 0.8
+    
+    # 质量控制
+    enable_logic_check: bool = True
+    max_logic_iterations: int = 3
+    enable_emotion_enhancement: bool = True
+    enable_dialogue_optimization: bool = True
+    
+    # 写作风格
+    writing_style: str = "balanced"  # balanced, literary, commercial, dramatic
+    narrative_perspective: str = "third"  # first, third, mixed
+    tone: str = "neutral"  # light, neutral, serious, humorous
+    
+    # 内容控制
+    max_characters: int = 8  # 最大角色数量
+    min_dialogue_ratio: float = 0.3  # 对话占比最小值
+    max_description_ratio: float = 0.4  # 描写占比最大值
+    
+    # 技术参数
+    segment_size: int = 2000  # 分段处理大小
+    parallel_workers: int = 3  # 并行处理线程数
+    retry_attempts: int = 3  # 重试次数
+
+
+# 预设配置模板
+PRESET_CONFIGS = {
+    "精品长篇": AdvancedNovelConfig(
+        genre="都市逆袭",
+        target_total_chars=25000,
+        model="qwen-max",
+        temperature=0.9,
+        enable_logic_check=True,
+        max_logic_iterations=5,
+        writing_style="literary",
+        enable_emotion_enhancement=True,
+        enable_dialogue_optimization=True,
+        max_characters=12,
+        parallel_workers=4
+    )
+}
+
+# 写作风格配置
+WRITING_STYLES = {
+    "balanced": {
+        "description": "平衡风格，适合大众阅读",
+        "dialogue_ratio": 0.35,
+        "description_ratio": 0.30,
+        "action_ratio": 0.35,
+        "complexity": "medium"
+    },
+    
+    "literary": {
+        "description": "文学风格，注重深度和内涵",
+        "dialogue_ratio": 0.25,
+        "description_ratio": 0.45,
+        "action_ratio": 0.30,
+        "complexity": "high"
+    },
+    
+    "commercial": {
+        "description": "商业风格，节奏快，易读",
+        "dialogue_ratio": 0.45,
+        "description_ratio": 0.25,
+        "action_ratio": 0.30,
+        "complexity": "low"
+    },
+    
+    "dramatic": {
+        "description": "戏剧风格，冲突强烈",
+        "dialogue_ratio": 0.50,
+        "description_ratio": 0.20,
+        "action_ratio": 0.30,
+        "complexity": "medium"
+    }
+}
+
+# 语调配置
+TONE_CONFIGS = {
+    "light": {
+        "description": "轻松愉快的语调",
+        "keywords": ["温馨", "轻松", "幽默", "治愈"],
+        "avoid_keywords": ["沉重", "压抑", "悲伤"]
+    },
+    
+    "neutral": {
+        "description": "中性平衡的语调",
+        "keywords": ["自然", "真实", "平衡"],
+        "avoid_keywords": ["过于夸张", "过于沉重"]
+    },
+    
+    "serious": {
+        "description": "严肃深刻的语调",
+        "keywords": ["深刻", "思考", "内省", "成长"],
+        "avoid_keywords": ["轻浮", "肤浅"]
+    },
+    
+    "humorous": {
+        "description": "幽默风趣的语调",
+        "keywords": ["幽默", "风趣", "机智", "轻松"],
+        "avoid_keywords": ["严肃", "沉重", "悲伤"]
+    }
+}
+
+# 题材扩展配置
+EXTENDED_GENRES = {
+    "都市逆袭": {
+        "core": "底层主角通过智慧和机遇逐步崛起，每一步都有合理动机和代价",
+        "typical_characters": ["奋斗主角", "贵人导师", "竞争对手", "支持朋友", "家人"],
+        "common_conflicts": ["阶层差距", "能力质疑", "机遇选择", "道德考验"],
+        "emotional_themes": ["自我证明", "成长蜕变", "责任担当", "情感成熟"]
+    },
+    
+    "甜宠先婚后爱": {
+        "core": "两个有血有肉的人从陌生到相知相爱的心理变化过程",
+        "typical_characters": ["独立女主", "温柔男主", "闺蜜", "家人", "工作伙伴"],
+        "common_conflicts": ["误解矛盾", "价值观差异", "外界阻力", "内心恐惧"],
+        "emotional_themes": ["信任建立", "情感觉醒", "相互理解", "共同成长"]
+    },
+    
+    "悬疑推理": {
+        "core": "通过逻辑推理和线索收集，逐步揭开真相的智力较量过程",
+        "typical_characters": ["推理主角", "神秘反派", "关键证人", "警方助手", "受害者"],
+        "common_conflicts": ["线索迷雾", "逻辑陷阱", "时间压力", "真相冲击"],
+        "emotional_themes": ["真相追求", "正义坚持", "智慧较量", "人性探索"]
+    },
+    
+    "末世生存": {
+        "core": "在绝望环境中求生，展现人性光辉与黑暗的生存史诗",
+        "typical_characters": ["生存主角", "幸存团队", "资源争夺者", "希望守护者", "堕落者"],
+        "common_conflicts": ["资源争夺", "环境威胁", "人性考验", "道德选择"],
+        "emotional_themes": ["生存意志", "人性光辉", "团队协作", "希望重燃"]
+    },
+    
+    "玄幻升级": {
+        "core": "在奇幻世界中通过修炼和历练不断突破自我的成长之路",
+        "typical_characters": ["修炼主角", "师父导师", "同门师兄", "强敌对手", "红颜知己"],
+        "common_conflicts": ["境界瓶颈", "强敌挑战", "门派争斗", "天道考验"],
+        "emotional_themes": ["修炼感悟", "师徒情深", "友情考验", "爱情升华"]
+    },
+    
+    "校园青春": {
+        "core": "青春期的成长、友情、初恋和自我发现的故事",
+        "typical_characters": ["青涩主角", "初恋对象", "好友团体", "老师", "家长"],
+        "common_conflicts": ["学业压力", "情感困惑", "友情考验", "家庭期待"],
+        "emotional_themes": ["青春迷茫", "初恋美好", "友情珍贵", "成长痛苦"]
+    },
+    
+    "职场励志": {
+        "core": "职场新人或转型者在工作中的成长和突破",
+        "typical_characters": ["职场新人", "严厉上司", "竞争同事", "导师前辈", "客户"],
+        "common_conflicts": ["工作挑战", "人际关系", "职业选择", "价值冲突"],
+        "emotional_themes": ["专业成长", "自我价值", "团队合作", "领导力"]
+    },
+    
+    "家庭温情": {
+        "core": "家庭成员之间的情感纠葛和最终和解",
+        "typical_characters": ["家庭成员", "长辈", "晚辈", "亲戚", "邻居"],
+        "common_conflicts": ["代沟问题", "价值观差异", "生活压力", "情感隔阂"],
+        "emotional_themes": ["亲情珍贵", "理解包容", "传承责任", "家庭和谐"]
+    }
+}
+
+def get_preset_config(preset_name: str) -> Optional[AdvancedNovelConfig]:
+    """获取预设配置"""
+    return PRESET_CONFIGS.get(preset_name)
+
+def get_writing_style_info(style: str) -> Dict[str, Any]:
+    """获取写作风格信息"""
+    return WRITING_STYLES.get(style, WRITING_STYLES["balanced"])
+
+def get_tone_info(tone: str) -> Dict[str, Any]:
+    """获取语调信息"""
+    return TONE_CONFIGS.get(tone, TONE_CONFIGS["neutral"])
+
+def get_genre_info(genre: str) -> Dict[str, Any]:
+    """获取题材信息"""
+    return EXTENDED_GENRES.get(genre, EXTENDED_GENRES["都市逆袭"])
+
+def list_available_presets() -> List[str]:
+    """列出可用的预设配置"""
+    return list(PRESET_CONFIGS.keys())
+
+def list_available_genres() -> List[str]:
+    """列出可用的题材"""
+    return list(EXTENDED_GENRES.keys())
+
+def list_available_styles() -> List[str]:
+    """列出可用的写作风格"""
+    return list(WRITING_STYLES.keys())
+
+def list_available_tones() -> List[str]:
+    """列出可用的语调"""
+    return list(TONE_CONFIGS.keys())

QualityAssessment.py

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+"""
+小说质量评估模块
+提供多维度的质量评估和改进建议
+"""
+
+import re
+import json
+import logging
+from typing import Dict, List, Tuple, Any
+from dataclasses import dataclass
+from collections import Counter
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class QualityMetrics:
+    """质量指标"""
+    # 基础指标
+    total_chars: int
+    effective_chars: int
+    paragraph_count: int
+    sentence_count: int
+    dialogue_count: int
+    
+    # 比例指标
+    dialogue_ratio: float
+    description_ratio: float
+    action_ratio: float
+    
+    # 复杂度指标
+    avg_sentence_length: float
+    vocabulary_richness: float
+    repetition_rate: float
+    
+    # 结构指标
+    scene_transitions: int
+    character_mentions: Dict[str, int]
+    emotion_words: int
+    
+    # 质量评分
+    overall_score: float
+    dimension_scores: Dict[str, float]
+
+
+class NovelQualityAssessor:
+    """小说质量评估器"""
+    
+    def __init__(self):
+        # 情感词汇库
+        self.emotion_words = {
+            "positive": ["开心", "快乐", "兴奋", "满足", "温暖", "感动", "幸福", "甜蜜", "欣慰", "骄傲"],
+            "negative": ["难过", "痛苦", "愤怒", "失望", "恐惧", "焦虑", "绝望", "孤独", "委屈", "后悔"],
+            "complex": ["复杂", "矛盾", "纠结", "挣扎", "无奈", "感慨", "思考", "反思", "领悟", "成长"]
+        }
+        
+        # 动作词汇
+        self.action_words = ["走", "跑", "坐", "站", "看", "听", "说", "想", "做", "拿", "放", "开", "关", "推", "拉"]
+        
+        # 描写词汇
+        self.description_words = ["美丽", "漂亮", "高大", "宽敞", "明亮", "温暖", "寒冷", "炎热", "安静", "嘈杂"]
+        
+        # 对话标记
+        self.dialogue_patterns = [
+            r'[【「"]([^【「"]+)[】」"]',  # 【】「」""标记的对话
+            r'"([^"]+)"',  # 英文引号
+            r"'([^']+)'",  # 中文引号
+        ]
+    
+    def assess_novel_quality(self, text: str, title: str = "") -> QualityMetrics:
+        """评估小说质量"""
+        logger.info("开始质量评估...")
+        
+        # 基础统计
+        basic_stats = self._calculate_basic_stats(text)
+        
+        # 内容分析
+        content_analysis = self._analyze_content(text)
+        
+        # 结构分析
+        structure_analysis = self._analyze_structure(text)
+        
+        # 语言质量分析
+        language_analysis = self._analyze_language_quality(text)
+        
+        # 计算综合评分
+        dimension_scores = {
+            "内容丰富度": content_analysis["richness_score"],
+            "结构合理性": structure_analysis["structure_score"],
+            "语言质量": language_analysis["language_score"],
+            "情感表达": content_analysis["emotion_score"],
+            "对话质量": content_analysis["dialogue_score"]
+        }
+        
+        overall_score = sum(dimension_scores.values()) / len(dimension_scores)
+        
+        # 构建质量指标对象
+        metrics = QualityMetrics(
+            total_chars=basic_stats["total_chars"],
+            effective_chars=basic_stats["effective_chars"],
+            paragraph_count=basic_stats["paragraph_count"],
+            sentence_count=basic_stats["sentence_count"],
+            dialogue_count=basic_stats["dialogue_count"],
+            
+            dialogue_ratio=content_analysis["dialogue_ratio"],
+            description_ratio=content_analysis["description_ratio"],
+            action_ratio=content_analysis["action_ratio"],
+            
+            avg_sentence_length=language_analysis["avg_sentence_length"],
+            vocabulary_richness=language_analysis["vocabulary_richness"],
+            repetition_rate=language_analysis["repetition_rate"],
+            
+            scene_transitions=structure_analysis["scene_transitions"],
+            character_mentions=structure_analysis["character_mentions"],
+            emotion_words=content_analysis["emotion_words"],
+            
+            overall_score=overall_score,
+            dimension_scores=dimension_scores
+        )
+        
+        logger.info(f"质量评估完成，综合评分: {overall_score:.1f}")
+        return metrics
+    
+    def _calculate_basic_stats(self, text: str) -> Dict[str, Any]:
+        """计算基础统计信息"""
+        # 总字符数
+        total_chars = len(text)
+        
+        # 有效字符数（中文字符+标点+数字）
+        lines = [line.strip() for line in text.split('\n') if line.strip()]
+        combined = ''.join(lines)
+        chinese_chars = re.findall(r'[\u4e00-\u9fa5]', combined)
+        punctuation = re.findall(r'[，。！？；：、""''（）【】《》]', combined)
+        numbers = re.findall(r'\d', combined)
+        effective_chars = len(chinese_chars) + len(punctuation) + len(numbers)
+        
+        # 段落数
+        paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
+        paragraph_count = len(paragraphs)
+        
+        # 句子数
+        sentences = re.split(r'[。！？]', text)
+        sentence_count = len([s for s in sentences if s.strip()])
+        
+        # 对话数
+        dialogue_count = 0
+        for pattern in self.dialogue_patterns:
+            dialogue_count += len(re.findall(pattern, text))
+        
+        return {
+            "total_chars": total_chars,
+            "effective_chars": effective_chars,
+            "paragraph_count": paragraph_count,
+            "sentence_count": sentence_count,
+            "dialogue_count": dialogue_count
+        }
+    
+    def _analyze_content(self, text: str) -> Dict[str, Any]:
+        """分析内容质量"""
+        # 对话比例
+        dialogue_chars = 0
+        for pattern in self.dialogue_patterns:
+            matches = re.findall(pattern, text)
+            dialogue_chars += sum(len(match) for match in matches)
+        
+        dialogue_ratio = dialogue_chars / len(text) if text else 0
+        
+        # 描写比例（通过描写词汇估算）
+        description_count = 0
+        for word in self.description_words:
+            description_count += text.count(word)
+        description_ratio = min(description_count / 100, 0.5)  # 归一化
+        
+        # 动作比例
+        action_count = 0
+        for word in self.action_words:
+            action_count += text.count(word)
+        action_ratio = min(action_count / 100, 0.5)  # 归一化
+        
+        # 情感词汇统计
+        emotion_words = 0
+        for category, words in self.emotion_words.items():
+            for word in words:
+                emotion_words += text.count(word)
+        
+        # 评分计算
+        richness_score = min(100, (dialogue_ratio * 30 + description_ratio * 40 + action_ratio * 30) * 100)
+        emotion_score = min(100, emotion_words * 2)  # 每个情感词2分
+        dialogue_score = min(100, dialogue_ratio * 200)  # 对话比例评分
+        
+        return {
+            "dialogue_ratio": dialogue_ratio,
+            "description_ratio": description_ratio,
+            "action_ratio": action_ratio,
+            "emotion_words": emotion_words,
+            "richness_score": richness_score,
+            "emotion_score": emotion_score,
+            "dialogue_score": dialogue_score
+        }
+    
+    def _analyze_structure(self, text: str) -> Dict[str, Any]:
+        """分析结构质量"""
+        # 场景转换（通过时间、地点词汇估算）
+        scene_markers = ["突然", "接着", "然后", "后来", "此时", "这时", "同时", "与此同时", "第二天", "几天后"]
+        scene_transitions = 0
+        for marker in scene_markers:
+            scene_transitions += text.count(marker)
+        
+        # 角色提及统计（简单的人名识别）
+        # 这里使用简单的启发式方法，实际应用中可以使用NER
+        potential_names = re.findall(r'[\u4e00-\u9fa5]{2,3}(?=[，。！？：；]|说|想|看|听)', text)
+        character_mentions = Counter(potential_names)
+        
+        # 只保留出现频率较高的（可能是角色名）
+        character_mentions = {name: count for name, count in character_mentions.items() if count >= 3}
+        
+        # 结构评分
+        structure_score = min(100, scene_transitions * 5 + len(character_mentions) * 10)
+        
+        return {
+            "scene_transitions": scene_transitions,
+            "character_mentions": character_mentions,
+            "structure_score": structure_score
+        }
+    
+    def _analyze_language_quality(self, text: str) -> Dict[str, Any]:
+        """分析语言质量"""
+        # 句子平均长度
+        sentences = re.split(r'[。！？]', text)
+        valid_sentences = [s.strip() for s in sentences if s.strip()]
+        avg_sentence_length = sum(len(s) for s in valid_sentences) / len(valid_sentences) if valid_sentences else 0
+        
+        # 词汇丰富度（简单估算）
+        words = re.findall(r'[\u4e00-\u9fa5]+', text)
+        unique_words = set(words)
+        vocabulary_richness = len(unique_words) / len(words) if words else 0
+        
+        # 重复率（检查重复的短语）
+        phrases = []
+        for i in range(len(text) - 10):
+            phrase = text[i:i+10]
+            if re.match(r'^[\u4e00-\u9fa5，。！？；：、""''（）【】《》]+$', phrase):
+                phrases.append(phrase)
+        
+        phrase_counts = Counter(phrases)
+        repeated_phrases = sum(1 for count in phrase_counts.values() if count > 1)
+        repetition_rate = repeated_phrases / len(phrases) if phrases else 0
+        
+        # 语言质量评分
+        length_score = min(100, max(0, 100 - abs(avg_sentence_length - 20) * 2))  # 理想句长20字
+        richness_score = vocabulary_richness * 100
+        repetition_score = max(0, 100 - repetition_rate * 200)
+        
+        language_score = (length_score + richness_score + repetition_score) / 3
+        
+        return {
+            "avg_sentence_length": avg_sentence_length,
+            "vocabulary_richness": vocabulary_richness,
+            "repetition_rate": repetition_rate,
+            "language_score": language_score
+        }
+    
+    def generate_improvement_suggestions(self, metrics: QualityMetrics) -> List[str]:
+        """生成改进建议"""
+        suggestions = []
+        
+        # 基于各维度评分给出建议
+        if metrics.dimension_scores["内容丰富度"] < 70:
+            suggestions.append("建议增加更多的场景描写和人物内心活动，提升内容丰富度")
+        
+        if metrics.dimension_scores["结构合理性"] < 70:
+            suggestions.append("建议优化故事结构，增加场景转换和情节推进")
+        
+        if metrics.dimension_scores["语言质量"] < 70:
+            suggestions.append("建议提升语言表达质量，避免重复用词，丰富词汇")
+        
+        if metrics.dimension_scores["情感表达"] < 70:
+            suggestions.append("建议增强情感表达，多使用情感词汇和内心独白")
+        
+        if metrics.dimension_scores["对话质量"] < 70:
+            suggestions.append("建议增加对话内容，让角色通过对话展现性格")
+        
+        # 基于具体指标给出建议
+        if metrics.dialogue_ratio < 0.2:
+            suggestions.append("对话比例偏低，建议增加角色对话来推进情节")
+        
+        if metrics.avg_sentence_length > 30:
+            suggestions.append("句子平均长度偏长，建议适当使用短句增加节奏感")
+        
+        if metrics.repetition_rate > 0.1:
+            suggestions.append("存在较多重复表达，建议使用同义词替换增加表达多样性")
+        
+        if len(metrics.character_mentions) < 3:
+            suggestions.append("角色数量偏少，建议增加配角丰富故事内容")
+        
+        return suggestions
+    
+    def export_quality_report(self, metrics: QualityMetrics, title: str = "", output_file: str = None) -> str:
+        """导出质量报告"""
+        report = f"""
+# 小说质量评估报告
+
+## 基本信息
+- 标题: {title or "未命名"}
+- 总字符数: {metrics.total_chars:,}
+- 有效字符数: {metrics.effective_chars:,}
+- 段落数: {metrics.paragraph_count}
+- 句子数: {metrics.sentence_count}
+- 对话数: {metrics.dialogue_count}
+
+## 综合评分: {metrics.overall_score:.1f}/100
+
+## 各维度评分
+- 内容丰富度: {metrics.dimension_scores['内容丰富度']:.1f}/100
+- 结构合理性: {metrics.dimension_scores['结构合理性']:.1f}/100
+- 语言质量: {metrics.dimension_scores['语言质量']:.1f}/100
+- 情感表达: {metrics.dimension_scores['情感表达']:.1f}/100
+- 对话质量: {metrics.dimension_scores['对话质量']:.1f}/100
+
+## 详细指标
+- 对话比例: {metrics.dialogue_ratio:.1%}
+- 描写比例: {metrics.description_ratio:.1%}
+- 动作比例: {metrics.action_ratio:.1%}
+- 平均句长: {metrics.avg_sentence_length:.1f}字
+- 词汇丰富度: {metrics.vocabulary_richness:.1%}
+- 重复率: {metrics.repetition_rate:.1%}
+- 场景转换: {metrics.scene_transitions}次
+- 情感词汇: {metrics.emotion_words}个
+
+## 角色提及统计
+"""
+        
+        for character, count in metrics.character_mentions.items():
+            report += f"- {character}: {count}次\n"
+        
+        # 添加改进建议
+        suggestions = self.generate_improvement_suggestions(metrics)
+        if suggestions:
+            report += "\n## 改进建议\n"
+            for i, suggestion in enumerate(suggestions, 1):
+                report += f"{i}. {suggestion}\n"
+        
+        # 保存到文件
+        if output_file:
+            try:
+                with open(output_file, 'w', encoding='utf-8') as f:
+                    f.write(report)
+                logger.info(f"质量报告已保存到: {output_file}")
+            except Exception as e:
+                logger.error(f"保存质量报告失败: {str(e)}")
+        
+        return report
+
+
+def assess_novel_quality(text: str, title: str = "") -> QualityMetrics:
+    """便捷函数：评估小说质量"""
+    assessor = NovelQualityAssessor()
+    return assessor.assess_novel_quality(text, title)
+
+
+def generate_quality_report(text: str, title: str = "", output_file: str = None) -> str:
+    """便捷函数：生成质量报告"""
+    assessor = NovelQualityAssessor()
+    metrics = assessor.assess_novel_quality(text, title)
+    return assessor.export_quality_report(metrics, title, output_file)
+
+
+if __name__ == "__main__":
+    # 测试代码
+    sample_text = """
+    《测试小说》
+    
+    李明走进办公室，心情复杂。今天是他入职的第一天，既兴奋又紧张。
+    
+    【你好，我是新来的李明。】他对前台小姐说道。
+    
+    【欢迎！我是小王，有什么需要帮助的尽管说。】小王热情地回答。
+    
+    李明感到一阵温暖，看来这里的同事都很友善。他想起了母亲昨天的话：【要好好工作，不要让我们失望。】
+    
+    突然，一个严肃的声音响起：【你就是新来的？跟我来。】
+    """
+    
+    metrics = assess_novel_quality(sample_text, "测试小说")
+    print(f"综合评分: {metrics.overall_score:.1f}")
+    print(f"对话比例: {metrics.dialogue_ratio:.1%}")
+    print(f"平均句长: {metrics.avg_sentence_length:.1f}")

resume_generation.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""
+恢复中断的小说生成任务
+"""
+
+import os
+import sys
+import json
+from pathlib import Path
+
+def find_unfinished_projects():
+    """查找未完成的项目"""
+    base_dir = Path(__file__).parent
+    unfinished = []
+    
+    for item in base_dir.iterdir():
+        if item.is_dir() and item.name.startswith('n') and item.name[1:].isdigit():
+            progress_file = item / ".generation_progress.json"
+            if progress_file.exists():
+                try:
+                    with open(progress_file, 'r', encoding='utf-8') as f:
+                        progress = json.load(f)
+                    
+                    if progress.get('stage') != 'complete':
+                        unfinished.append({
+                            'dir': item.name,
+                            'stage': progress.get('stage', 'unknown'),
+                            'progress': progress.get('progress_percent', 0),
+                            'last_update': progress.get('last_update', 'unknown')
+                        })
+                except:
+                    continue
+    
+    return sorted(unfinished, key=lambda x: x['last_update'], reverse=True)
+
+def main():
+    print("🔍 查找未完成的小说生成任务...")
+    
+    unfinished = find_unfinished_projects()
+    
+    if not unfinished:
+        print("✅ 没有找到未完成的任务")
+        print("💡 运行 python ViralNovel.py 开始新的生成")
+        return
+    
+    print(f"\n📋 找到 {len(unfinished)} 个未完成的任务:")
+    print("-" * 60)
+    
+    for i, proj in enumerate(unfinished, 1):
+        print(f"{i}. 项目: {proj['dir']}")
+        print(f"   阶段: {proj['stage']}")
+        print(f"   进度: {proj['progress']:.1f}%")
+        print(f"   更新: {proj['last_update']}")
+        print()
+    
+    if len(unfinished) == 1:
+        choice = input("是否继续这个任务？(y/n): ").strip().lower()
+        if choice == 'y':
+            selected = unfinished[0]
+        else:
+            print("❌ 已取消")
+            return
+    else:
+        try:
+            choice = input(f"选择要继续的任务 (1-{len(unfinished)}): ").strip()
+            idx = int(choice) - 1
+            if 0 <= idx < len(unfinished):
+                selected = unfinished[idx]
+            else:
+                print("❌ 无效选择")
+                return
+        except ValueError:
+            print("❌ 请输入数字")
+            return
+    
+    print(f"\n🚀 继续生成任务: {selected['dir']}")
+    print(f"📊 当前进度: {selected['stage']} ({selected['progress']:.1f}%)")
+    print("⏳ 正在恢复...")
+    
+    try:
+        # 导入并运行恢复函数
+        sys.path.append(str(Path(__file__).parent))
+        from ViralNovel import resume_generation_from_progress
+        
+        result = resume_generation_from_progress(selected['dir'])
+        
+        print(f"\n✅ 生成完成！")
+        print(f"📖 标题: {result['title']}")
+        print(f"📁 保存位置: {result['project_dir']}")
+        
+    except ImportError:
+        print("❌ 无法导入 ViralNovel 模块")
+        print("💡 请确保 ViralNovel.py 文件存在")
+        
+    except Exception as e:
+        error_str = str(e)
+        print(f"\n❌ 恢复失败: {error_str}")
+        
+        if "网络连接" in error_str or "超时" in error_str:
+            print("\n🔧 网络问题解决建议:")
+            print("1. 检查网络连接")
+            print("2. 等待几分钟后重试")
+            print("3. 运行: python resume_generation.py")
+            
+        elif "rate limit" in error_str.lower():
+            print("\n⏰ API调用频率限制:")
+            print("1. 等待几分钟后重试")
+            print("2. 运行: python resume_generation.py")
+
+if __name__ == "__main__":
+    main()