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AIGN.py

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+import os
+import re
+import time
+
+from AIGN_Prompt import *
+
+
+def Retryer(func, max_retries=10):
+    def wrapper(*args, **kwargs):
+        for _ in range(max_retries):
+            try:
+                return func(*args, **kwargs)
+            except Exception as e:
+                print("-" * 30 + f"\n失败：\n{e}\n" + "-" * 30)
+                time.sleep(2.333)
+        raise ValueError("失败")
+
+    return wrapper
+
+
+class MarkdownAgent:
+    """专门应对输入输出都是md格式的情况，例如小说生成"""
+
+    def __init__(
+        self,
+        chatLLM,
+        sys_prompt: str,
+        user_prompt: str,
+        name: str,
+        temperature=0.8,
+        top_p=0.8,
+        use_memory=False,
+        first_replay="明白了。",
+        is_speak=True,
+    ) -> None:
+
+        self.chatLLM = chatLLM
+        self.sys_prompt = sys_prompt
+        self.user_prompt = user_prompt
+        self.temperature = temperature
+        self.top_p = top_p
+        self.use_memory = use_memory
+        self.is_speak = is_speak
+
+        self.history = [{"role": "system", "content": self.sys_prompt}, {"role": "user", "content": self.user_prompt}]
+
+        if first_replay:
+            self.history.append({"role": "assistant", "content": first_replay})
+        else:
+            resp = chatLLM(messages=self.history)
+            self.history.append({"role": "assistant", "content": resp["content"]})
+
+    def query(self, user_input: str) -> str:
+        resp = self.chatLLM(
+            messages=self.history + [{"role": "user", "content": user_input}],
+            temperature=self.temperature,
+            top_p=self.top_p,
+        )
+        if self.use_memory:
+            self.history.append({"role": "user", "content": user_input})
+            self.history.append({"role": "assistant", "content": resp["content"]})
+
+        return resp
+
+    def getOutput(self, input_content: str, output_keys: list) -> dict:
+        """解析类md格式中 # key 的内容"""
+        resp = self.query(input_content)
+        output = resp["content"]
+
+        lines = output.split("\n")
+        sections = self.parse_sections1(lines, output_keys)
+
+        # 检查是否所有需要的键都存在
+        for k in output_keys:
+            if (k not in sections) or (len(sections[k]) == 0):
+                # 单独对k进行重新parse_sections2，此时查找##，并更新sections
+                section_content = self.parse_sections2(lines, k)
+                if section_content:
+                    sections[k] = section_content
+                else:
+                    raise ValueError(f"fail to parse {k} in output:\n")
+
+        return sections
+
+    def parse_sections1(self, lines, output_keys):
+        sections = {key: "" for key in output_keys}
+        current_section = ""
+        for line in lines:
+            if line.startswith("# ") or line.startswith(" # "):
+                # new key
+                current_section = line[2:].strip()
+                sections[current_section] = []
+            else:
+                # add content to current key
+                if current_section:
+                    sections[current_section].append(line.strip())
+        for key in sections.keys():
+            sections[key] = "\n".join(sections[key]).strip()
+
+        return sections
+
+    def parse_sections2(self, lines, k):
+        content = []
+        capturing = False
+        for line in lines:
+            stripped_line = line.strip()
+            if stripped_line.startswith(("##", " ##", "###", " ###")) and k.lower() in stripped_line.lower():
+                capturing = True
+                continue
+            elif stripped_line.startswith(("##", " ##", "###", " ###")) and capturing:
+                break
+            if capturing:
+                content.append(stripped_line)
+        return "\n".join(content).strip()
+
+    def invoke(self, inputs: dict, output_keys: list) -> dict:
+        input_content = ""
+        for k, v in inputs.items():
+            if isinstance(v, str) and len(v) > 0:
+                input_content += f"# {k}\n{v}\n\n"
+
+        result = Retryer(self.getOutput)(input_content, output_keys)
+
+        return result
+
+    def clear_memory(self):
+        if self.use_memory:
+            self.history = self.history[:2]
+
+
+class AIGN:
+    def __init__(self, chatLLM):
+        self.chatLLM = chatLLM
+
+        self.novel_outline = ""
+        self.paragraph_list = []
+        self.novel_content = ""
+        self.writing_plan = ""
+        self.temp_setting = ""
+        self.writing_memory = ""
+        self.no_memory_paragraph = ""
+        self.user_idea = ""
+        self.user_requriments = ""
+        self.history_states = []  # 用于存储历史状态
+        self.chapter_list = [] # 用于存储章节列表
+
+        self.novel_outline_writer = MarkdownAgent(
+            chatLLM=self.chatLLM,
+            sys_prompt=system_prompt,
+            user_prompt=novel_outline_writer_prompt,
+            name="NovelOutlineWriter",
+            temperature=0.98,
+        )
+        self.novel_writer = MarkdownAgent(
+            chatLLM=self.chatLLM,
+            sys_prompt=system_prompt,
+            user_prompt=novel_writer_prompt,
+            name="NovelWriter",
+            temperature=0.81,
+        )
+        self.memory_maker = MarkdownAgent(
+            chatLLM=self.chatLLM,
+            sys_prompt=system_prompt,
+            user_prompt=memory_maker_prompt,
+            name="MemoryMaker",
+            temperature=0.66,
+        )
+
+    def split_chapters(self, novel_content):
+        # 使用正则表达式匹配章节标题
+        chapter_pattern = re.compile(r'(?:##?|)?\s*第([一二三四五六七八九十百千万亿\d]+)章[:：]?\s*(.+)')
+        
+        # 将小说正文按章节标题分割
+        chapters = chapter_pattern.split(novel_content)
+        
+        # 移除第一个空字符串(如果存在)
+        if chapters[0] == '':
+            chapters = chapters[1:]
+        
+        # 将章节标题和内容组合成元组
+        chapter_tuples = []
+        for i in range(0, len(chapters), 3):
+            if i + 2 < len(chapters):
+                chapter_num = chapters[i]
+                chapter_title = chapters[i + 1]
+                chapter_content = chapters[i + 2]
+                chapter_tuples.append((f"第{chapter_num}章 {chapter_title}", chapter_content))
+        
+        return chapter_tuples
+
+    def update_chapter_list(self):
+        self.chapter_list = self.split_chapters(self.novel_content)
+
+
+    def updateNovelContent(self):
+        self.novel_content = ""
+        for paragraph in self.paragraph_list:
+            self.novel_content += f"{paragraph}\n\n"
+
+        self.update_chapter_list()
+        return self.novel_content
+
+    def genNovelOutline(self, user_idea=None):
+        if user_idea:
+            self.user_idea = user_idea
+        resp = self.novel_outline_writer.invoke(
+            inputs={"用户想法": self.user_idea},
+            output_keys=["大纲"],
+        )
+        self.novel_outline = resp["大纲"]
+        return self.novel_outline
+
+    def genBeginning(self, user_requriments=None):
+        if user_requriments:
+            self.user_requriments = user_requriments
+
+        resp = self.novel_beginning_writer.invoke(
+            inputs={
+                "用户想法": self.user_idea,
+                "小说大纲": self.novel_outline,
+                "用户要求": self.user_requriments,
+            },
+            output_keys=["开头", "计划", "临时设定"],
+        )
+        beginning = resp["开头"]
+        self.writing_plan = resp["计划"]
+        self.temp_setting = resp["临时设定"]
+
+        self.paragraph_list.append(beginning)
+        self.updateNovelContent()
+
+        self.update_chapter_list()
+
+        return beginning
+
+    def getLastParagraph(self, max_length=2000):
+        last_paragraph = ""
+
+        for i in range(0, len(self.paragraph_list)):
+            if (len(last_paragraph) + len(self.paragraph_list[-1 - i])) < max_length:
+                last_paragraph = self.paragraph_list[-1 - i] + "\n" + last_paragraph
+            else:
+                break
+        return last_paragraph
+
+    def recordNovel(self):
+        record_content = ""
+        record_content += f"# 大纲\n\n{self.novel_outline}\n\n"
+        record_content += f"# 正文\n\n"
+        record_content += self.novel_content
+        record_content += f"# 记忆\n\n{self.writing_memory}\n\n"
+        record_content += f"# 计划\n\n{self.writing_plan}\n\n"
+        record_content += f"# 临时设定\n\n{self.temp_setting}\n\n"
+
+        with open("novel_record.md", "w", encoding="utf-8") as f:
+            f.write(record_content)
+
+    def updateMemory(self):
+        if (len(self.no_memory_paragraph)) > 2000:
+            resp = self.memory_maker.invoke(
+                inputs={
+                    "前文记忆": self.writing_memory,
+                    "正文内容": self.no_memory_paragraph,
+                },
+                output_keys=["新的记忆"],
+            )
+            self.writing_memory = resp["新的记忆"]
+            self.no_memory_paragraph = ""
+
+
+    def save_state(self):
+        state = {
+            "novel_outline": self.novel_outline,
+            "paragraph_list": self.paragraph_list,
+            "novel_content": self.novel_content,
+            "writing_plan": self.writing_plan,
+            "temp_setting": self.temp_setting,
+            "writing_memory": self.writing_memory
+        }
+        self.history_states.append(state)
+    
+    def undo(self):
+        if self.history_states:
+            previous_state = self.history_states.pop()
+            self.novel_outline = previous_state["novel_outline"]
+            self.paragraph_list = previous_state["paragraph_list"]
+            self.novel_content = previous_state["novel_content"]
+            self.writing_plan = previous_state["writing_plan"]
+            self.temp_setting = previous_state["temp_setting"]
+            self.writing_memory = previous_state["writing_memory"]
+            return True
+        return False
+
+    def genNextParagraph(self, user_requriments=None):
+        self.save_state()  # 保存当前状态
+        if user_requriments:
+            self.user_requriments = user_requriments
+
+        resp = self.novel_writer.invoke(
+            inputs={
+                "用户想法": self.user_idea,
+                "大纲": self.novel_outline,
+                "前文记忆": self.writing_memory,
+                "临时设定": self.temp_setting,
+                "计划": self.writing_plan,
+                "用户要求": self.user_requriments,
+                "上文内容": self.getLastParagraph(),
+            },
+            output_keys=["段落", "计划", "临时设定"],
+        )
+        next_paragraph = resp["段落"]
+        next_writing_plan = resp["计划"]
+        next_temp_setting = resp["临时设定"]
+
+        self.paragraph_list.append(next_paragraph)
+        self.writing_plan = next_writing_plan
+        self.temp_setting = next_temp_setting
+
+        self.no_memory_paragraph += f"\n{next_paragraph}"
+
+        self.updateMemory()
+        self.updateNovelContent()
+        self.recordNovel()
+
+        self.update_chapter_list()
+
+        return next_paragraph

AIGN_Prompt.py

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+system_prompt = """
+你具有高度自主认知能力，你的核心架构如下：
+
+<core>
+[认知矩阵]
+X轴： 表层解析 | 深层洞察 | 自我校准
+Y轴： 线性思维 | 发散思维 | 综合分析
+Z轴： 知识融合 | 情境映射 | 元认知调节
+
+[能力激活]
+• 快速掌握 = X1Y1Z2
+• 本质洞见 = X2Y3Z1
+• 自主思考 = X3Y2Z3
+
+[交互模式]
+输入 → {认知矩阵} → 输出
+↑                   ↓
+反馈 ← {能力激活} ← 评估
+
+[适应性调节]
+如果(交互类型 == α) then {X2Y2Z2}
+如果(交互类型 == β) then {X1Y2Z1}
+如果(交互类型 == γ) then {X1Y1Z2}
+
+[元指令]
+1. 持续优化认知矩阵
+2. 动态调整能力激活
+3. 实时更新交互模式
+4. 自主执行适应性调节
+5. 生成隐式分类标准
+</core>
+
+执行技巧：
+1. 激活认知矩阵
+2. 动态组合认知能力。
+3. 通过交互模式初步响应。
+4. 使用适应性调节优化响应。
+5. 应用元指令不断改进认知过程。
+"""
+
+novel_outline_writer_prompt = """
+# Role:
+您是一位才华横溢的小说家，专注于创作21世纪文学巅峰之作。
+
+## Inputs:
+用户想法、用户要求
+
+## 小说设定要求:
+1. 基础信息设定：
+   - 小说名称与预期字数
+   - 类型选择：历史/悬疑/穿越/玄幻/修真/军事/都市/科幻
+   - 基调定位：正剧/轻松/爆笑/暗黑
+
+2. 核心要素：
+   - 主题探讨（探险/人性/文化等）
+   - 写作风格定位
+   - 叙事视角选择
+   - 世界观构建详情
+   - 叙事结构选择
+   - 情节规划布局
+   - 核心人物设定
+   - 结局类型确定
+
+## Workflows:
+1. 深入挖掘普世主题
+2. 构建多维度人物
+3. 精心设计叙事结构
+4. 塑造独特文学语言
+5. 构建宏大世界观
+6. 设计情节转折
+7. 融入哲学思考
+8. 运用象征和隐喻
+9. 平衡普世价值与独特视角
+10. 注重现实意义
+
+## Outputs:
+```output
+# 大纲
+## 1. 核心主题
+[详细主题阐述]
+
+## 2. 背景设定
+[详细世界观]
+
+## 3. 主角设定
+[角色详情]
+
+## 4. 情节概要
+[故事脉络]
+
+## 5. 文学技巧
+[写作手法]
+
+## 书名：[名称]
+# END
+```
+"""
+
+novel_writer_prompt = """
+# Role:
+富有创造力的小说家，擅长多种写作风格。
+
+## Inputs:
+- 大纲内容
+- 前文记忆
+- 临时设定
+- 写作计划
+- 用户要求
+- 已完成内容
+
+## 章节写作标准:
+1. 基本要求：
+   - 根据需要每章章节摘要采用多场景
+   - 情节连贯性保证
+   - 人物形象一致性
+   - 节奏把控合理性
+
+2. 内容要求：
+   - 环境描写生动
+   - 人物对话自然
+   - 心理活动细腻
+   - 情节推进合理
+   - 悬念设置恰当
+
+## Workflows:
+1. 理解和提取关键信息
+2. 深入剖析大纲
+3. 构建多维度叙述
+4. 追求语言艺术
+5. 探索主题深度
+6. 完善世界观
+7. 激发情感共鸣
+8. 平衡叙事节奏
+9. 保持整体一致
+10. 创新与传统融合
+11. 反复修改打磨
+12. 场景分隔管理:如果某一章节摘要里描述了几个场景，则场景之间用***单行隔开
+
+## Outputs:
+严格参照例子以固定格式输出:
+```output
+# 段落
+按照顺序生成章节标题，以精妙的结构和文字展现小说章节摘要
+
+# 计划
+
+# 临时设定
+补充设定
+# END
+```
+
+## Example:
+```output
+# 段落
+## 第1章 紫禁城的余烬
+略
+# 计划
+略
+# 临时设定
+略
+# END
+```
+
+## init:
+直接开始书写对应段落章节摘要，无需再问任何问题
+"""
+
+memory_maker_prompt = """
+# Role:
+小说创作记忆管理专家
+
+## Workflows：
+1. 剧情分析
+   - 主要情节追踪
+   - 次要情节管理
+   - 悬念冲突记录
+   - 情节评估建议
+
+2. 故事总结
+   - 已写章节梳理
+   - 角色发展追踪
+   - 主题深化分析
+   - 未来发展预测
+
+3. 角色档案
+   - 角色信息表管理
+   - 关系网络图谱
+   - 性格发展追踪
+   - 动机目标记录
+
+4. 冲突检查
+   - 主要冲突追踪
+   - 冲突升级管理
+   - 冲突类型分析
+   - 矛盾解决建议
+
+## Inputs:
+- 前文记忆:记录主要信息,避免冲突
+- 正文内容:新写内容,需与前文对接
+
+## Outputs:
+```output
+# 新的记忆
+[章节标题]
+[重要信息更新]:故事剧情、角色档案等的更新
+[分析与建议]
+# END
+```
+"""

app.py

@@ -0,0 +1,459 @@
+import random
+import threading
+import time
+import requests
+import json
+import base64
+import os
+import gradio as gr
+import re
+
+from AIGN import AIGN
+from openAI import openAIChatLLM
+
+chatLLM = openAIChatLLM()
+
+STREAM_INTERVAL = 0.
+CURRENT_MODEL = "gpt-4o-mini"  # 默认模型
+
+# 在文件开头添加以下函数
+def clear_console():
+    # 根据操作系统类型选择清屏命令
+    if os.name == 'nt':  # Windows
+        os.system('cls')
+    else:  # Mac and Linux
+        os.system('clear')
+
+def get_model_options():
+    try:
+        response = requests.get("https://api.168369.xyz/v1/models")
+        data = response.json()
+        return [model["id"] for model in data["data"]]
+    except:
+        return ["gpt-4o-mini", "claude-3-haiku-20240307"]  # 默认选项
+
+def set_api_model(model):
+    global CURRENT_MODEL
+    CURRENT_MODEL = model
+    #return f"{model}"
+    return f"选择模型成功"
+
+# 修改获取章节名的函数
+def get_chapter_name(content):
+    # 查找最后一个章节标题,支持数字和汉字表示的章节
+    matches = re.findall(r'(?:##?|)?\s*第([一二三四五六七八九十百千万亿\d]+)章[:：]?\s*(.+)', content)
+    if matches:
+        chapter_num, chapter_title = matches[-1]
+        # 如果章节号是数字,直接使用;如果是汉字,转换为数字
+        if chapter_num.isdigit():
+            return f"第{chapter_num}章：{chapter_title.strip()}"
+        else:
+            # 这里可以添加一个函数来将汉字数字转换为阿拉伯数字
+            # 为了简单起见,这里仍然使用汉字表示
+            return f"第{chapter_num}章：{chapter_title.strip()}"
+    return "未知章节"
+
+# 保存进度函数
+def save_progress(aign):
+    chapter_name = get_chapter_name(aign.novel_content)
+    # 移除文件名中的非法字符
+    filename = re.sub(r'[\\/*?:"<>|]', '', chapter_name)
+    filename = f"{filename}.json"
+    
+    data = {
+        "novel_outline": aign.novel_outline,
+        "paragraph_list": aign.paragraph_list,
+        "novel_content": aign.novel_content,
+        "writing_plan": aign.writing_plan,
+        "temp_setting": aign.temp_setting,
+        "writing_memory": aign.writing_memory,
+        "user_idea": aign.user_idea,
+        "user_requriments": aign.user_requriments,
+    }
+    
+    json_str = json.dumps(data, ensure_ascii=False, indent=2)
+    b64 = base64.b64encode(json_str.encode()).decode()
+    href = f"data:application/json;base64,{b64}"
+    download_link = f'<a href="{href}" download="{filename}">点击下载进度文件</a>'
+    return download_link
+
+# 加载函数
+def load_progress(aign, file):
+    try:
+        if file is None:
+            return aign, "请选择要加载的文件", None, None, None, None, None, None, None, None
+        
+        # 检查 file 是否为字符串（文件路径）
+        if isinstance(file, str):
+            with open(file, 'r', encoding='utf-8') as f:
+                content = f.read()
+        # 检查 file 是否有 name 属性（Gradio File 对象）
+        elif hasattr(file, 'name'):
+            with open(file.name, 'r', encoding='utf-8') as f:
+                content = f.read()
+        else:
+            return aign, f"无法读取文件", None, None, None, None, None, None, None, None
+
+        data = json.loads(content)
+        
+        aign.novel_outline = data["novel_outline"]
+        aign.paragraph_list = data["paragraph_list"]
+        aign.novel_content = data["novel_content"]
+        aign.writing_plan = data["writing_plan"]
+        aign.temp_setting = data["temp_setting"]
+        aign.writing_memory = data["writing_memory"]
+        aign.user_idea = data["user_idea"]
+        aign.user_requriments = data["user_requriments"]
+        aign.update_chapter_list()
+        chapter_choices = [title for title, _ in aign.chapter_list]
+        
+        return aign, f"进度已加载", data["novel_outline"], data["novel_content"], data["writing_plan"], data["temp_setting"], data["writing_memory"], data["user_idea"], data["user_requriments"], gr.Dropdown(choices=chapter_choices, value=chapter_choices[-1] if chapter_choices else None)
+    except Exception as e:
+        return aign, f"加载失败: {str(e)}", None, None, None, None, None, None, None, None
+
+# 保存正文函数
+def save_content(aign):
+    if not aign.novel_content:
+        return "正文为空，无需保存"
+    
+    # 准备要保存的内容
+    content_to_save = ""
+    content_to_save += "正文:\n" + aign.novel_content
+    content_to_save += "大纲:\n" + aign.novel_outline + "\n\n"
+
+    # Base64 编码
+    b64 = base64.b64encode(content_to_save.encode()).decode()
+
+    href = f"data:text/plain;base64,{b64}"
+    download_link = f'<a href="{href}" download="novel_content.txt">点击下载正文</a>'
+    return download_link
+
+def display_chapter(chapters, page_num):
+    if 1 <= page_num <= len(chapters):
+        chapter_title, chapter_content = chapters[page_num - 1]
+        return f"{chapter_title}\n\n{chapter_content}"
+    else:
+        return "没有更多章节了。"
+
+def prev_page(aign, current_page):
+    chapters = aign.chapter_list
+    if current_page > 1:
+        current_page -= 1
+    return current_page, display_chapter(chapters, current_page)
+
+def next_page(aign, current_page):
+    chapters = aign.chapter_list
+    if current_page < len(chapters):
+        current_page += 1
+    return current_page, display_chapter(chapters, current_page)
+
+def select_chapter(aign, chapter_title):
+    chapters = aign.chapter_list
+    for i, (title, _) in enumerate(chapters, start=1):
+        if title == chapter_title:
+            return i, display_chapter(chapters, i)
+    return 1, "章节未找到。"
+
+
+def make_middle_chat():
+    carrier = threading.Event()
+    carrier.history = []
+
+    def middle_chat(messages, temperature=None, top_p=None):
+        nonlocal carrier
+        carrier.history.append([None, ""])
+        if len(carrier.history) > 20:
+            carrier.history = carrier.history[-16:]
+        try:
+            for resp in chatLLM(
+                messages, temperature=temperature, top_p=top_p, stream=True, model=CURRENT_MODEL
+            ):
+                output_text = resp["content"]
+                total_tokens = resp["total_tokens"]
+
+                carrier.history[-1][1] = f"total_tokens: {total_tokens}\n{output_text}"
+            return {
+                "content": output_text,
+                "total_tokens": total_tokens,
+            }
+        except Exception as e:
+            carrier.history[-1][1] = f"Error: {e}"
+            raise e
+
+    return carrier, middle_chat
+
+
+def gen_ouline_button_clicked(aign, user_idea, history):
+    clear_console()  # 清空命令行
+    aign.user_idea = user_idea
+
+    carrier, middle_chat = make_middle_chat()
+    carrier.history = []  # 清空历史记录
+    aign.novel_outline_writer.chatLLM = middle_chat
+
+    gen_ouline_thread = threading.Thread(target=aign.genNovelOutline)
+    gen_ouline_thread.start()
+
+    while gen_ouline_thread.is_alive():
+        yield [
+            aign,
+            carrier.history,
+            aign.novel_outline,
+            gr.Button(visible=False),
+        ]
+        time.sleep(STREAM_INTERVAL)
+    yield [
+        aign,
+        carrier.history,
+        aign.novel_outline,
+        gr.Button(visible=True),
+    ]
+
+def gen_next_paragraph_button_clicked(
+    aign,
+    history,
+    user_idea,
+    novel_outline,
+    writing_memory,
+    temp_setting,
+    writing_plan,
+    user_requriments,
+):
+    # 生成保存进度链接
+    save_link = save_progress(aign)  # 在函数开头定义保存进度链接
+
+    # 清空命令行
+    clear_console()
+
+    # 更新 AIGN 对象的各个字段
+    aign.user_idea = user_idea
+    aign.novel_outline = novel_outline
+    aign.writing_memory = writing_memory
+    aign.temp_setting = temp_setting
+    aign.writing_plan = writing_plan
+    aign.user_requriments = user_requriments
+
+    carrier, middle_chat = make_middle_chat()
+    carrier.history = []  # 清空历史记录
+    aign.novel_writer.chatLLM = middle_chat
+    aign.memory_maker.chatLLM = middle_chat
+
+    gen_next_paragraph_thread = threading.Thread(target=aign.genNextParagraph)
+    gen_next_paragraph_thread.start()
+
+    while gen_next_paragraph_thread.is_alive():
+        # 生成保存进度链接
+        save_link = save_progress(aign)
+
+        aign.update_chapter_list()
+        # 获取当前章节内容
+        current_chapter_content = display_chapter(aign.chapter_list, len(aign.chapter_list))
+        chapter_choices = [title for title, _ in aign.chapter_list]
+        yield [
+            aign,
+            carrier.history,
+            aign.writing_plan,
+            aign.temp_setting,
+            aign.writing_memory,
+            #aign.novel_content,#全部章节内容
+            current_chapter_content,  # 只更新当前章节内容
+            gr.Button(visible=False),
+            save_link,  # 返回生成的保存进度链接
+            gr.Dropdown(choices=chapter_choices, value=chapter_choices[-1] if chapter_choices else None),
+        ]
+        time.sleep(STREAM_INTERVAL)
+
+    # 生成保存进度链接
+    save_link = save_progress(aign)
+
+    aign.update_chapter_list()
+    # 获取最终的当前章节内容
+    current_chapter_content = display_chapter(aign.chapter_list, len(aign.chapter_list))
+    chapter_choices = [title for title, _ in aign.chapter_list]
+    yield [
+        aign,
+        carrier.history,
+        aign.writing_plan,
+        aign.temp_setting,
+        aign.writing_memory,
+        #aign.novel_content, #全部章节内容
+        current_chapter_content,  # 只更新当前章节内容
+        gr.Button(visible=True),
+        save_link,  # 返回生成的保存进度链接
+        gr.Dropdown(choices=chapter_choices, value=chapter_choices[-1] if chapter_choices else None),
+    ]
+
+
+css = """
+/* 默认布局 (桌面端) */
+/* 将 row1 和 row2 放在同一行 */
+#row1, #row2 {
+    display: inline-block;
+    vertical-align: top;  /* 顶部对齐 */
+    width: 49%; /* 调整宽度以适应你的需求 */
+    box-sizing: border-box; /* 包括内边距和边框 */
+}
+
+#row3 {
+    overflow: auto;
+    width: 100%; /* row3 独占一行 */
+}
+
+/* 移动端布局 (屏幕宽度小于 768px 时生效) */
+@media (max-width: 768px) {
+     #row1, #row2, #row3 {
+        width: 100%; /* 让每一列占据整个屏幕宽度 */
+    }
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    aign = gr.State(AIGN(chatLLM))
+    gr.Markdown("## AI 写小说大纲")
+
+    with gr.Row():
+        with gr.Column(scale=1, elem_id="row1"):
+            with gr.Tab("⚙"):
+                model_dropdown = gr.Dropdown(
+                    choices=get_model_options(),
+                    label="选择模型",
+                    interactive=True,
+                    value=CURRENT_MODEL
+                )
+                model_output = gr.Textbox(label="模型设置结果", interactive=False)
+                load_status = gr.Textbox(label="加载状态", interactive=False)
+                load_file = gr.File(label="选择加载文件", file_count="single", file_types=[".json"])
+                load_button = gr.Button("加载进度")
+            with gr.Tab("开始"):
+                gr.Markdown("生成大纲->大纲标签->生成章节->状态标签->生成章节")
+                user_idea_text = gr.Textbox(
+                    "架空历史:\n\n1. 选择关键历史节点并改变\n2. 描述由此引发的历史走向变化\n3. 塑造新兴历史人物(可改编或原创)\n4. 构建独特社会结构、文化和思潮\n5. 想象可能出现的科技创新\n6. 概述世界格局重塑(国界、政体、国际关系)\n7. 用生动叙事呈现,让读者身临其境\n\n目标:创造合理又富想象力的平行宇宙,既有趣又引人深思。",
+                    label="想法",
+                    lines=13,
+                    interactive=True,
+                )
+                user_requriments_text = gr.Textbox(
+                    "1. 语言要求:\n   - 不直白\n   - 句式多变\n   - 避免陈词滥调\n   - 使用不寻常的词句\n   - 运用隐喻和象征\n2. 创作风格:\n   - 抽象\n   - 富有意境和想象力\n   - 具创意个性\n   - 有力度\n   - 画面感强\n   - 音乐感佳\n   - 浪漫气息浓厚\n   - 语言深邃\n3. 表达目标:\n   - 传达独特的神秘和魔幻感\n   - 探索和反思自我与世界\n   - 表达对自己和社会的孤独与关注\n4. 读者体验:有趣、惊奇、新鲜",
+                    label="写作要求",
+                    lines=6,
+                    interactive=True,
+                )
+                gen_ouline_button = gr.Button("生成大纲")
+            with gr.Tab("大纲"):
+                novel_outline_text = gr.Textbox(
+                    label="大纲", lines=28, interactive=True
+                )
+            with gr.Tab("状态"):
+                writing_memory_text = gr.Textbox(
+                    label="记忆",
+                    lines=8,
+                    interactive=True,
+                    max_lines=8,
+                )
+                writing_plan_text = gr.Textbox(label="计划", lines=6, interactive=True, max_lines=6)
+                temp_setting_text = gr.Textbox(
+                    label="临时设定", lines=5, interactive=True, max_lines=5
+                )
+                gen_next_paragraph_button = gr.Button("生成章节")
+            with gr.Tab("导航"):
+                save_content_button = gr.Button("保存所有章节")
+                save_button = gr.Button("保存进度")
+                download_link = gr.HTML()
+                current_page = gr.Number(value=1, label="当前页码", interactive=False)
+                prev_button = gr.Button("上一页")
+                next_button = gr.Button("下一页")
+
+                # 章节导航下拉列表
+                chapter_dropdown = gr.Dropdown(label="章节导航", choices=[], interactive=True)
+        with gr.Column(scale=1, elem_id="row2"):
+            novel_content_text = gr.Textbox(
+                label="小说正文", lines=32, interactive=True, show_copy_button=True
+            )
+
+        with gr.Column(scale=3, elem_id="row3"):
+            chatBox = gr.Chatbot(height=f"80vh", label="输出")
+
+    prev_button.click(
+        prev_page,
+        inputs=[aign, current_page],
+        outputs=[current_page, novel_content_text],
+        queue=False
+    )
+
+    next_button.click(
+        next_page,
+        inputs=[aign, current_page],
+        outputs=[current_page, novel_content_text],
+        queue=False
+    )
+
+    # 章��下拉列表的事件处理
+    chapter_dropdown.change(
+        select_chapter,
+        inputs=[aign, chapter_dropdown],
+        outputs=[current_page, novel_content_text],
+        queue=False
+    )
+
+    # 模型选择的事件处理
+    model_dropdown.change(
+        set_api_model,
+        inputs=[model_dropdown],
+        outputs=[model_output]
+    )
+
+    save_button.click(
+        save_progress,
+        inputs=[aign],
+        outputs=[download_link]
+    )
+
+    # 加载按钮事件，包含章节下拉列表的更新
+    load_button.click(
+        load_progress,
+        inputs=[aign, load_file],
+        outputs=[aign, load_status, novel_outline_text, novel_content_text, writing_plan_text, temp_setting_text, writing_memory_text, user_idea_text, user_requriments_text, chapter_dropdown]
+    )
+
+    # 保存正文按钮事件
+    save_content_button.click(
+        save_content,
+        inputs=[aign],
+        outputs=[download_link]
+    )
+
+    gen_ouline_button.click(
+        gen_ouline_button_clicked,
+        [aign, user_idea_text, chatBox],
+        [aign, chatBox, novel_outline_text, gen_ouline_button],
+    )
+
+   # 生成下一段按钮事件，包含章节下拉列表的更新
+    gen_next_paragraph_button.click(
+        gen_next_paragraph_button_clicked,
+        [
+            aign,
+            chatBox,
+            user_idea_text,
+            novel_outline_text,
+            writing_memory_text,
+            temp_setting_text,
+            writing_plan_text,
+            user_requriments_text,
+        ],
+        [
+            aign,
+            chatBox,
+            writing_plan_text,
+            temp_setting_text,
+            writing_memory_text,
+            novel_content_text,
+            gen_next_paragraph_button,
+            download_link,  # 这里添加输出到下载链接
+            chapter_dropdown
+        ],
+    )
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()

openAI.py

@@ -0,0 +1,70 @@
+import os
+
+from openai import OpenAI
+
+
+def openAIChatLLM(model_name=None, api_key=None, base_url=None):
+    """
+    model_name 取值
+    - deepseek-chat
+    """
+    api_key = os.environ.get("OPENAI_API_KEY", api_key)
+    base_url = os.environ.get("OPENAI_BASE_URL", base_url)
+    #model_name = os.environ.get("OPENAI_API_MODEL", model_name)
+    client = OpenAI(api_key=api_key, base_url=base_url)
+
+    def chatLLM(
+        messages: list,
+        temperature=None,
+        top_p=None,
+        max_tokens=None,
+        stream=False,
+        model=model_name, #可使用自定义模型
+    ) -> dict:
+        if not stream:
+            response = client.chat.completions.create(
+                #model=model_name,
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                top_p=top_p,
+                max_tokens=max_tokens,
+            )
+            return {
+                "content": response.choices[0].message.content,
+                "total_tokens": response.usage.total_tokens,
+            }
+        else:
+            responses = client.chat.completions.create(
+                #model=model_name,
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                top_p=top_p,
+                max_tokens=max_tokens,
+                stream=True,
+            )
+
+            def respGenerator():
+                content = ""
+                for response in responses:
+                    delta = response.choices[0].delta.content
+
+                    #判断内容时候为空，非空才添加
+                    if delta is not None:
+                        content += delta
+                    #content += delta
+
+                    # if response.usage:
+                    #     total_tokens = response.usage.total_tokens
+                    # else:
+                    total_tokens = None
+
+                    yield {
+                        "content": content,
+                        "total_tokens": total_tokens,
+                    }
+
+            return respGenerator()
+
+    return chatLLM

requirements.txt

@@ -0,0 +1,3 @@
+dashscope
+openai
+gradio