1051 lines
38 KiB
Python
1051 lines
38 KiB
Python
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"""
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多模态提示生成模块
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用于生成配图描述、视频脚本描述，并可选择性地生成图片
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"""
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from typing import List, Dict, Optional, Tuple
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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import json
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import re
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import base64
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import io
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from pathlib import Path
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import time
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class MultimodalPromptGenerator:
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"""多模态提示生成器"""
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def __init__(self):
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# 配图描述生成 Prompt
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self.image_prompt_template = """
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你是专业的配图描述生成专家，专门为内容创作生成详细的配图描述。
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【内容片段】
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{content_segment}
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【上下文】
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- 品牌：{brand}
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- 优势：{advantages}
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- 平台：{platform}
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- 关键词：{keyword}
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【配图描述要求】
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1. **详细描述**
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- 描述图片应该包含的主要元素（人物、物品、场景等）
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- 描述图片的风格（写实、插画、图表、截图等）
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- 描述图片的色调和氛围（明亮、专业、温馨等）
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- 描述图片的构图（居中、左右布局、上下布局等）
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2. **平台适配**
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- 小红书：生活化、美观、有吸引力
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- 抖音：视觉冲击力强、简洁明了
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- 微信公众号：专业、清晰、符合文章风格
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- B站：适合视频封面、有动感
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3. **品牌融入**
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- 如果内容涉及品牌，配图应自然融入品牌元素
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- 但不要过于商业化，保持自然
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4. **实用性**
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- 描述要具体，便于设计师或AI生图工具理解
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- 长度控制在50-150字
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- 使用中文描述
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【输出格式】
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请严格按照以下 JSON 格式输出，不要添加任何其他内容：
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{{
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"image_description": "<详细的配图描述>",
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"style": "<风格：写实/插画/图表/截图/其他>",
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"tone": "<色调：明亮/专业/温馨/商务/其他>",
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"composition": "<构图：居中/左右/上下/其他>",
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"key_elements": ["<元素1>", "<元素2>", ...],
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"platform_specific": "<平台特定要求>"
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}}
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【开始生成】
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"""
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# 视频脚本描述生成 Prompt
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self.video_script_template = """
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你是专业的视频脚本描述生成专家，专门为B站等视频平台生成详细的画面描述。
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【内容片段】
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{content_segment}
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【上下文】
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- 品牌：{brand}
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- 优势：{advantages}
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- 关键词：{keyword}
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- 时间戳：{timestamp}
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【视频画面描述要求】
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1. **画面描述**
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- 描述画面应该展示的内容（场景、人物、物品、动作等）
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- 描述画面类型（实拍、动画、截图、演示等）
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- 描述画面节奏（快切、慢镜头、定格等）
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2. **镜头语言**
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- 镜头类型（特写、中景、全景等）
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- 镜头运动（推拉、摇移、跟随等）
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- 画面转场（切换、淡入淡出、划入等）
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3. **音效和字幕**
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- 建议的音效（背景音乐、音效等）
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- 字幕要点（关键信息、强调内容）
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4. **时长建议**
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- 该片段的建议时长（秒）
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【输出格式】
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请严格按照以下 JSON 格式输出，不要添加任何其他内容：
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{{
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"scene_description": "<画面描述>",
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"shot_type": "<镜头类型：特写/中景/全景/其他>",
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"camera_movement": "<镜头运动：推拉/摇移/跟随/固定/其他>",
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"transition": "<转场：切换/淡入淡出/划入/其他>",
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"audio_suggestion": "<音效建议>",
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"subtitle_key_points": ["<字幕要点1>", "<字幕要点2>", ...],
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"duration_seconds": <建议时长（秒）>
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}}
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【开始生成】
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"""
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# 批量配图描述生成 Prompt
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self.batch_image_prompt_template = """
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你是专业的配图描述生成专家，为内容生成多个配图描述。
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【完整内容】
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{full_content}
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【品牌】{brand}
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【优势】{advantages}
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【平台】{platform}
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【关键词】{keyword}
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【要求】
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1. 识别内容中所有需要配图的位置（已标注【配图：xxx】）
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2. 为每个配图位置生成详细的配图描述
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3. 确保配图描述与内容上下文相关
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4. 保持配图风格的统一性
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【输出格式】
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请严格按照以下 JSON 格式输出，不要添加任何其他内容：
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{{
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"image_descriptions": [
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{{
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"position": "<在内容中的位置描述>",
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"original_hint": "<原始配图提示>",
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"detailed_description": "<详细配图描述>",
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"style": "<风格>",
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"tone": "<色调>",
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"key_elements": ["<元素1>", "<元素2>", ...]
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}},
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...
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],
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"total_images": <配图总数>,
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"style_consistency": "<整体风格一致性说明>"
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}}
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【开始生成】
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"""
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# 通义万相文生图 Prompt 生成模板（核心）
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self.tongyi_prompt_template = """
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你是专业的通义万相文生图 Prompt 工程师，目标是为文章生成最匹配、高质量的配图。
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文章内容：
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{content}
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要求：
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- 输出纯中文 Prompt，长度 60–120 字，越详细越好。
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- 画面必须紧扣文章核心观点、关键场景或品牌 {brand}（可自然融入产品形态、科技元素、logo 氛围）。
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- 风格建议：高清、科技感/写实/插画/未来主义，根据文章调性自动判断。
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- 构图：主体突出、背景简洁、视觉冲击力强、色彩和谐。
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- 避免任何敏感词，确保合规。
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- 只输出纯 Prompt 文本，不要加任何解释、标题或多余内容。
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最终输出示例：
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"一张未来科技感极强的插画，中央是品牌 {brand} 的 AI 模型界面，周围环绕多模态数据流和实时知识图标，背景是深蓝星空，画面干净高清，2048分辨率"
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"""
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# 图片插入位置推荐 Prompt
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self.image_position_template = """
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阅读以下文章内容，判断最适合插入配图的位置，并给出理由。
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文章内容：
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{content}
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要求：
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|||
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- 推荐 1–2 个最佳插入点（例如"第2段结尾""总结部分前"）。
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- 每处插入点说明：为什么这里适合配图（增强理解、吸引眼球、突出品牌等）。
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- 输出格式：
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插入位置1：{具体位置}
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理由：{简短说明}
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插入位置2：{具体位置}
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理由：{简短说明}
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只输出插入建议，不要输出其他内容。
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"""
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def extract_image_placeholders(self, content: str) -> List[Dict]:
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"""
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从内容中提取配图占位符
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Args:
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content: 内容文本
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Returns:
|
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配图占位符列表，每个包含位置、原始提示等信息
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"""
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placeholders = []
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# 匹配【配图：xxx】格式
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pattern = r'【配图[：:]([^】]+)】'
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matches = re.finditer(pattern, content)
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for match in matches:
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start_pos = match.start()
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end_pos = match.end()
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hint = match.group(1).strip()
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# 获取上下文（前后各100字）
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context_start = max(0, start_pos - 100)
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context_end = min(len(content), end_pos + 100)
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context = content[context_start:context_end]
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# 获取所在段落
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paragraph_start = content.rfind('\n', 0, start_pos) + 1
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paragraph_end = content.find('\n', end_pos)
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if paragraph_end == -1:
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paragraph_end = len(content)
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paragraph = content[paragraph_start:paragraph_end]
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placeholders.append({
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"position": start_pos,
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"hint": hint,
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"context": context,
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"paragraph": paragraph,
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"full_match": match.group(0)
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})
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return placeholders
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|||
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def generate_image_description(
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self,
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content_segment: str,
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brand: str,
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|
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advantages: str,
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platform: str,
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keyword: str,
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llm_chain
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) -> Dict:
|
|||
|
|
"""
|
|||
|
|
生成单个配图的详细描述
|
|||
|
|
|
|||
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Args:
|
|||
|
|
content_segment: 内容片段
|
|||
|
|
brand: 品牌名称
|
|||
|
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advantages: 品牌优势
|
|||
|
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platform: 平台名称
|
|||
|
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keyword: 关键词
|
|||
|
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llm_chain: LangChain 链对象
|
|||
|
|
|
|||
|
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Returns:
|
|||
|
|
配图描述字典
|
|||
|
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"""
|
|||
|
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try:
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|
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prompt = PromptTemplate.from_template(self.image_prompt_template)
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chain = prompt | llm_chain | StrOutputParser()
|
|||
|
|
|
|||
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result = chain.invoke({
|
|||
|
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"content_segment": content_segment,
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"brand": brand,
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"advantages": advantages,
|
|||
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"platform": platform,
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|||
|
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"keyword": keyword
|
|||
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})
|
|||
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|
|||
|
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# 解析结果
|
|||
|
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description_data = self._parse_image_description(result)
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|||
|
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return description_data
|
|||
|
|
|
|||
|
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except Exception as e:
|
|||
|
|
# 如果生成失败，返回基于规则的简单描述
|
|||
|
|
return self._rule_based_image_description(content_segment, platform)
|
|||
|
|
|
|||
|
|
def generate_batch_image_descriptions(
|
|||
|
|
self,
|
|||
|
|
content: str,
|
|||
|
|
brand: str,
|
|||
|
|
advantages: str,
|
|||
|
|
platform: str,
|
|||
|
|
keyword: str,
|
|||
|
|
llm_chain
|
|||
|
|
) -> Dict:
|
|||
|
|
"""
|
|||
|
|
批量生成所有配图的详细描述
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
content: 完整内容
|
|||
|
|
brand: 品牌名称
|
|||
|
|
advantages: 品牌优势
|
|||
|
|
platform: 平台名称
|
|||
|
|
keyword: 关键词
|
|||
|
|
llm_chain: LangChain 链对象
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
包含所有配图描述的字典
|
|||
|
|
"""
|
|||
|
|
# 先提取所有占位符
|
|||
|
|
placeholders = self.extract_image_placeholders(content)
|
|||
|
|
|
|||
|
|
if not placeholders:
|
|||
|
|
return {
|
|||
|
|
"image_descriptions": [],
|
|||
|
|
"total_images": 0,
|
|||
|
|
"style_consistency": "无配图需求"
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
prompt = PromptTemplate.from_template(self.batch_image_prompt_template)
|
|||
|
|
chain = prompt | llm_chain | StrOutputParser()
|
|||
|
|
|
|||
|
|
result = chain.invoke({
|
|||
|
|
"full_content": content,
|
|||
|
|
"brand": brand,
|
|||
|
|
"advantages": advantages,
|
|||
|
|
"platform": platform,
|
|||
|
|
"keyword": keyword
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 解析结果
|
|||
|
|
batch_data = self._parse_batch_image_descriptions(result, placeholders)
|
|||
|
|
return batch_data
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
# 如果批量生成失败，逐个生成
|
|||
|
|
descriptions = []
|
|||
|
|
for placeholder in placeholders:
|
|||
|
|
desc = self.generate_image_description(
|
|||
|
|
placeholder["paragraph"],
|
|||
|
|
brand,
|
|||
|
|
advantages,
|
|||
|
|
platform,
|
|||
|
|
keyword,
|
|||
|
|
llm_chain
|
|||
|
|
)
|
|||
|
|
desc["position"] = placeholder["hint"]
|
|||
|
|
desc["original_hint"] = placeholder["hint"]
|
|||
|
|
descriptions.append(desc)
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"image_descriptions": descriptions,
|
|||
|
|
"total_images": len(descriptions),
|
|||
|
|
"style_consistency": "逐个生成，风格可能不完全统一"
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def generate_video_script_description(
|
|||
|
|
self,
|
|||
|
|
content_segment: str,
|
|||
|
|
brand: str,
|
|||
|
|
advantages: str,
|
|||
|
|
keyword: str,
|
|||
|
|
timestamp: str,
|
|||
|
|
llm_chain
|
|||
|
|
) -> Dict:
|
|||
|
|
"""
|
|||
|
|
生成视频脚本的画面描述
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
content_segment: 内容片段
|
|||
|
|
brand: 品牌名称
|
|||
|
|
advantages: 品牌优势
|
|||
|
|
keyword: 关键词
|
|||
|
|
timestamp: 时间戳（如"00:30-01:00"）
|
|||
|
|
llm_chain: LangChain 链对象
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
视频画面描述字典
|
|||
|
|
"""
|
|||
|
|
try:
|
|||
|
|
prompt = PromptTemplate.from_template(self.video_script_template)
|
|||
|
|
chain = prompt | llm_chain | StrOutputParser()
|
|||
|
|
|
|||
|
|
result = chain.invoke({
|
|||
|
|
"content_segment": content_segment,
|
|||
|
|
"brand": brand,
|
|||
|
|
"advantages": advantages,
|
|||
|
|
"keyword": keyword,
|
|||
|
|
"timestamp": timestamp
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 解析结果
|
|||
|
|
script_data = self._parse_video_script(result)
|
|||
|
|
return script_data
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
# 如果生成失败，返回基于规则的简单描述
|
|||
|
|
return self._rule_based_video_script(content_segment, timestamp)
|
|||
|
|
|
|||
|
|
def _parse_image_description(self, result: str) -> Dict:
|
|||
|
|
"""解析配图描述结果"""
|
|||
|
|
json_match = re.search(r'\{.*\}', result, re.DOTALL)
|
|||
|
|
if json_match:
|
|||
|
|
try:
|
|||
|
|
data = json.loads(json_match.group())
|
|||
|
|
if "image_description" in data:
|
|||
|
|
return data
|
|||
|
|
except json.JSONDecodeError:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
# 如果无法解析，返回简单描述
|
|||
|
|
return {
|
|||
|
|
"image_description": result[:200] if result else "配图描述生成失败",
|
|||
|
|
"style": "写实",
|
|||
|
|
"tone": "专业",
|
|||
|
|
"composition": "居中",
|
|||
|
|
"key_elements": [],
|
|||
|
|
"platform_specific": ""
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _parse_batch_image_descriptions(self, result: str, placeholders: List[Dict]) -> Dict:
|
|||
|
|
"""解析批量配图描述结果"""
|
|||
|
|
json_match = re.search(r'\{.*\}', result, re.DOTALL)
|
|||
|
|
if json_match:
|
|||
|
|
try:
|
|||
|
|
data = json.loads(json_match.group())
|
|||
|
|
if "image_descriptions" in data:
|
|||
|
|
# 确保每个描述都有位置信息
|
|||
|
|
for i, desc in enumerate(data["image_descriptions"]):
|
|||
|
|
if i < len(placeholders):
|
|||
|
|
if "position" not in desc:
|
|||
|
|
desc["position"] = placeholders[i]["hint"]
|
|||
|
|
if "original_hint" not in desc:
|
|||
|
|
desc["original_hint"] = placeholders[i]["hint"]
|
|||
|
|
return data
|
|||
|
|
except json.JSONDecodeError:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
# 如果无法解析，返回空结果
|
|||
|
|
return {
|
|||
|
|
"image_descriptions": [],
|
|||
|
|
"total_images": 0,
|
|||
|
|
"style_consistency": "解析失败"
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _parse_video_script(self, result: str) -> Dict:
|
|||
|
|
"""解析视频脚本描述结果"""
|
|||
|
|
json_match = re.search(r'\{.*\}', result, re.DOTALL)
|
|||
|
|
if json_match:
|
|||
|
|
try:
|
|||
|
|
data = json.loads(json_match.group())
|
|||
|
|
if "scene_description" in data:
|
|||
|
|
return data
|
|||
|
|
except json.JSONDecodeError:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
# 如果无法解析，返回简单描述
|
|||
|
|
return {
|
|||
|
|
"scene_description": result[:200] if result else "画面描述生成失败",
|
|||
|
|
"shot_type": "中景",
|
|||
|
|
"camera_movement": "固定",
|
|||
|
|
"transition": "切换",
|
|||
|
|
"audio_suggestion": "背景音乐",
|
|||
|
|
"subtitle_key_points": [],
|
|||
|
|
"duration_seconds": 5
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _rule_based_image_description(self, content_segment: str, platform: str) -> Dict:
|
|||
|
|
"""基于规则的简单配图描述（备用方案）"""
|
|||
|
|
# 简单的关键词提取
|
|||
|
|
keywords = []
|
|||
|
|
if "对比" in content_segment or "比较" in content_segment:
|
|||
|
|
keywords.append("对比图表")
|
|||
|
|
if "步骤" in content_segment or "流程" in content_segment:
|
|||
|
|
keywords.append("流程图")
|
|||
|
|
if "数据" in content_segment or "统计" in content_segment:
|
|||
|
|
keywords.append("数据图表")
|
|||
|
|
if "产品" in content_segment or "功能" in content_segment:
|
|||
|
|
keywords.append("产品展示")
|
|||
|
|
|
|||
|
|
if not keywords:
|
|||
|
|
keywords = ["相关配图"]
|
|||
|
|
|
|||
|
|
style_map = {
|
|||
|
|
"小红书": "生活化、美观",
|
|||
|
|
"抖音": "视觉冲击力强",
|
|||
|
|
"微信公众号": "专业、清晰",
|
|||
|
|
"B站": "适合视频封面"
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"image_description": f"展示{keywords[0]}的配图，风格：{style_map.get(platform, '专业')}",
|
|||
|
|
"style": "写实",
|
|||
|
|
"tone": "专业",
|
|||
|
|
"composition": "居中",
|
|||
|
|
"key_elements": keywords,
|
|||
|
|
"platform_specific": style_map.get(platform, "")
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _rule_based_video_script(self, content_segment: str, timestamp: str) -> Dict:
|
|||
|
|
"""基于规则的简单视频脚本描述（备用方案）"""
|
|||
|
|
return {
|
|||
|
|
"scene_description": f"展示相关内容：{content_segment[:50]}...",
|
|||
|
|
"shot_type": "中景",
|
|||
|
|
"camera_movement": "固定",
|
|||
|
|
"transition": "切换",
|
|||
|
|
"audio_suggestion": "背景音乐",
|
|||
|
|
"subtitle_key_points": [content_segment[:30] + "..."],
|
|||
|
|
"duration_seconds": 5
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def format_image_descriptions_for_display(self, descriptions: List[Dict]) -> str:
|
|||
|
|
"""
|
|||
|
|
格式化配图描述用于显示
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
descriptions: 配图描述列表
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
格式化后的文本
|
|||
|
|
"""
|
|||
|
|
if not descriptions:
|
|||
|
|
return "无配图需求"
|
|||
|
|
|
|||
|
|
formatted = []
|
|||
|
|
for i, desc in enumerate(descriptions, 1):
|
|||
|
|
formatted.append(f"### 配图 {i}")
|
|||
|
|
formatted.append(f"**位置**：{desc.get('position', 'N/A')}")
|
|||
|
|
formatted.append(f"**原始提示**：{desc.get('original_hint', 'N/A')}")
|
|||
|
|
formatted.append(f"**详细描述**：{desc.get('detailed_description', desc.get('image_description', 'N/A'))}")
|
|||
|
|
formatted.append(f"**风格**：{desc.get('style', 'N/A')}")
|
|||
|
|
formatted.append(f"**色调**：{desc.get('tone', 'N/A')}")
|
|||
|
|
formatted.append(f"**关键元素**：{', '.join(desc.get('key_elements', []))}")
|
|||
|
|
formatted.append("")
|
|||
|
|
|
|||
|
|
return "\n".join(formatted)
|
|||
|
|
|
|||
|
|
def format_video_script_for_display(self, script: Dict) -> str:
|
|||
|
|
"""
|
|||
|
|
格式化视频脚本描述用于显示
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
script: 视频脚本描述字典
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
格式化后的文本
|
|||
|
|
"""
|
|||
|
|
formatted = []
|
|||
|
|
formatted.append(f"**画面描述**：{script.get('scene_description', 'N/A')}")
|
|||
|
|
formatted.append(f"**镜头类型**：{script.get('shot_type', 'N/A')}")
|
|||
|
|
formatted.append(f"**镜头运动**：{script.get('camera_movement', 'N/A')}")
|
|||
|
|
formatted.append(f"**转场**：{script.get('transition', 'N/A')}")
|
|||
|
|
formatted.append(f"**音效建议**：{script.get('audio_suggestion', 'N/A')}")
|
|||
|
|
formatted.append(f"**字幕要点**：{', '.join(script.get('subtitle_key_points', []))}")
|
|||
|
|
formatted.append(f"**建议时长**：{script.get('duration_seconds', 'N/A')}秒")
|
|||
|
|
|
|||
|
|
return "\n".join(formatted)
|
|||
|
|
|
|||
|
|
def generate_tongyi_image_prompt(
|
|||
|
|
self,
|
|||
|
|
content: str,
|
|||
|
|
brand: str,
|
|||
|
|
llm_chain
|
|||
|
|
) -> str:
|
|||
|
|
"""
|
|||
|
|
生成通义万相文生图 Prompt（高质量中文）
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
content: 文章内容
|
|||
|
|
brand: 品牌名称
|
|||
|
|
llm_chain: LangChain 链对象
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
生成的 Prompt 文本
|
|||
|
|
"""
|
|||
|
|
try:
|
|||
|
|
prompt = PromptTemplate.from_template(self.tongyi_prompt_template)
|
|||
|
|
chain = prompt | llm_chain | StrOutputParser()
|
|||
|
|
|
|||
|
|
result = chain.invoke({
|
|||
|
|
"content": content,
|
|||
|
|
"brand": brand
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 清理结果，只保留 Prompt 文本
|
|||
|
|
result = result.strip()
|
|||
|
|
# 移除可能的引号
|
|||
|
|
if result.startswith('"') and result.endswith('"'):
|
|||
|
|
result = result[1:-1]
|
|||
|
|
if result.startswith("'") and result.endswith("'"):
|
|||
|
|
result = result[1:-1]
|
|||
|
|
|
|||
|
|
return result
|
|||
|
|
except Exception as e:
|
|||
|
|
# 如果生成失败，返回基于内容的简单 Prompt
|
|||
|
|
return f"一张关于{content[:50]}的专业配图，风格：高清、现代、科技感，品牌：{brand}"
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_image_size_for_platform(platform: str) -> str:
|
|||
|
|
"""
|
|||
|
|
根据平台返回合适的图片尺寸
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
platform: 平台名称（如"知乎（专业问答）"、"小红书（生活种草）"等）
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
图片尺寸字符串，格式为 "宽*高"
|
|||
|
|
"""
|
|||
|
|
# 通义万相（wanx-v1）允许的尺寸（来自接口报错提示）
|
|||
|
|
# ['1024*1024', '720*1280', '1280*720', '768*1152']
|
|||
|
|
#
|
|||
|
|
# 说明：
|
|||
|
|
# - 文章/资讯配图：优先 16:9（1280*720）
|
|||
|
|
# - 社交图文（小红书等）：优先竖图（768*1152，更接近 2:3/3:4 的观感）
|
|||
|
|
# - 短视频封面/竖图：9:16（720*1280）
|
|||
|
|
# - 方图：1:1（1024*1024）
|
|||
|
|
#
|
|||
|
|
# 平台名称到图片尺寸的映射（仅使用允许尺寸）
|
|||
|
|
platform_size_map = {
|
|||
|
|
# 文章类平台 - 使用16:9横图（适合文章配图）
|
|||
|
|
"知乎（专业问答）": "1280*720", # 16:9
|
|||
|
|
"微信公众号（长文）": "1280*720", # 16:9
|
|||
|
|
"CSDN（技术博客）": "1280*720", # 16:9
|
|||
|
|
"头条号（资讯软文）": "1280*720", # 16:9
|
|||
|
|
"百家号（资讯）": "1280*720", # 16:9
|
|||
|
|
"网易号（资讯）": "1280*720", # 16:9
|
|||
|
|
"企鹅号（资讯）": "1280*720", # 16:9
|
|||
|
|
"新浪新闻（资讯）": "1280*720", # 16:9
|
|||
|
|
"搜狐号（资讯）": "1280*720", # 16:9
|
|||
|
|
"一点号（资讯）": "1280*720", # 16:9
|
|||
|
|
"东方财富（财经）": "1280*720", # 16:9
|
|||
|
|
"原创力文档（文档）": "1280*720", # 16:9
|
|||
|
|
"邦阅网（外贸）": "1280*720", # 16:9
|
|||
|
|
"新浪博客（博客）": "1280*720", # 16:9
|
|||
|
|
"简书（文艺）": "1280*720", # 16:9
|
|||
|
|
|
|||
|
|
# 视频类平台 - 使用16:9横图（适合视频封面）
|
|||
|
|
"B站（视频脚本）": "1280*720", # 16:9
|
|||
|
|
|
|||
|
|
# 社交类平台 - 使用1:1方图
|
|||
|
|
"小红书（生活种草）": "768*1152", # 2:3（更接近小红书常见版式）
|
|||
|
|
"QQ空间（社交）": "1024*1024", # 1:1
|
|||
|
|
|
|||
|
|
# 短视频平台 - 使用9:16竖图
|
|||
|
|
"抖音图文（短内容）": "720*1280", # 9:16
|
|||
|
|
|
|||
|
|
# 技术平台 - 使用16:9横图
|
|||
|
|
"GitHub（README/文档）": "1280*720", # 16:9
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 精确匹配
|
|||
|
|
if platform in platform_size_map:
|
|||
|
|
return platform_size_map[platform]
|
|||
|
|
|
|||
|
|
# 模糊匹配（包含关键词）
|
|||
|
|
if "知乎" in platform or "问答" in platform:
|
|||
|
|
return "1280*720" # 16:9
|
|||
|
|
elif "小红书" in platform or "种草" in platform:
|
|||
|
|
return "768*1152" # 2:3
|
|||
|
|
elif "抖音" in platform or "短视频" in platform:
|
|||
|
|
return "720*1280" # 9:16
|
|||
|
|
elif "公众号" in platform or "微信" in platform:
|
|||
|
|
return "1280*720" # 16:9
|
|||
|
|
elif "csdn" in platform or "技术" in platform or "博客" in platform:
|
|||
|
|
return "1280*720" # 16:9
|
|||
|
|
elif "b站" in platform or "视频" in platform or "bilibili" in platform:
|
|||
|
|
return "1280*720" # 16:9
|
|||
|
|
elif "资讯" in platform or "新闻" in platform or "文章" in platform:
|
|||
|
|
return "1280*720" # 16:9
|
|||
|
|
elif "社交" in platform or "空间" in platform:
|
|||
|
|
return "1024*1024" # 1:1
|
|||
|
|
else:
|
|||
|
|
# 默认使用16:9（适合大多数文章类平台）
|
|||
|
|
return "1280*720" # 16:9
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def normalize_tongyi_image_size(size: str) -> str:
|
|||
|
|
"""
|
|||
|
|
将任意 size 规范化为通义万相允许的尺寸。
|
|||
|
|
允许尺寸：1024*1024, 720*1280, 1280*720, 768*1152
|
|||
|
|
"""
|
|||
|
|
allowed = ("1024*1024", "720*1280", "1280*720", "768*1152")
|
|||
|
|
if size in allowed:
|
|||
|
|
return size
|
|||
|
|
|
|||
|
|
import re
|
|||
|
|
|
|||
|
|
m = re.match(r"^\s*(\d+)\s*\*\s*(\d+)\s*$", str(size))
|
|||
|
|
if not m:
|
|||
|
|
return "1024*1024"
|
|||
|
|
|
|||
|
|
w = int(m.group(1))
|
|||
|
|
h = int(m.group(2))
|
|||
|
|
if w <= 0 or h <= 0:
|
|||
|
|
return "1024*1024"
|
|||
|
|
|
|||
|
|
target_ratio = w / h
|
|||
|
|
candidates = []
|
|||
|
|
for s in allowed:
|
|||
|
|
aw, ah = map(int, s.split("*"))
|
|||
|
|
candidates.append((s, abs((aw / ah) - target_ratio), abs((aw * ah) - (w * h))))
|
|||
|
|
|
|||
|
|
# 先按比例最接近，其次按面积接近
|
|||
|
|
candidates.sort(key=lambda x: (x[1], x[2]))
|
|||
|
|
return candidates[0][0]
|
|||
|
|
|
|||
|
|
def generate_image_with_tongyi(
|
|||
|
|
self,
|
|||
|
|
prompt: str,
|
|||
|
|
api_key: str,
|
|||
|
|
model: str = "wanx-v1",
|
|||
|
|
size: str = "1024*1024",
|
|||
|
|
n: int = 1
|
|||
|
|
) -> Dict:
|
|||
|
|
"""
|
|||
|
|
使用通义万相生成图片
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
prompt: 图片生成提示词（中文）
|
|||
|
|
api_key: 阿里云 DashScope API Key
|
|||
|
|
model: 模型名称，默认 wanx-v1
|
|||
|
|
size: 图片尺寸，默认 1024*1024
|
|||
|
|
n: 生成数量，默认 1
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
包含生成结果的字典：
|
|||
|
|
{
|
|||
|
|
"success": bool,
|
|||
|
|
"image_url": str, # 成功时返回图片URL
|
|||
|
|
"task_id": str, # 任务ID
|
|||
|
|
"error": str # 失败时返回错误信息
|
|||
|
|
}
|
|||
|
|
"""
|
|||
|
|
try:
|
|||
|
|
def _safe_get(obj, key: str, default=None):
|
|||
|
|
"""兼容 DashScope 返回对象/字典，且避免 __getattr__ 抛 KeyError。"""
|
|||
|
|
if obj is None:
|
|||
|
|
return default
|
|||
|
|
if isinstance(obj, dict):
|
|||
|
|
return obj.get(key, default)
|
|||
|
|
try:
|
|||
|
|
return getattr(obj, key)
|
|||
|
|
except Exception:
|
|||
|
|
return default
|
|||
|
|
|
|||
|
|
import dashscope
|
|||
|
|
from dashscope import ImageSynthesis
|
|||
|
|
|
|||
|
|
dashscope.api_key = api_key
|
|||
|
|
|
|||
|
|
# 兜底：确保 size 是允许值
|
|||
|
|
size = self.normalize_tongyi_image_size(size)
|
|||
|
|
|
|||
|
|
# 调用通义万相API
|
|||
|
|
response = ImageSynthesis.call(
|
|||
|
|
model=model,
|
|||
|
|
prompt=prompt,
|
|||
|
|
n=n,
|
|||
|
|
size=size
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
status_code = _safe_get(response, "status_code", None)
|
|||
|
|
if status_code == 200:
|
|||
|
|
output = _safe_get(response, "output", None)
|
|||
|
|
|
|||
|
|
# 有些情况下 status_code==200 但任务实际 FAILED（results 为空）
|
|||
|
|
task_status = ""
|
|||
|
|
if _safe_get(output, "task_status", None) is not None:
|
|||
|
|
task_status = str(_safe_get(output, "task_status") or "")
|
|||
|
|
elif _safe_get(output, "taskStatus", None) is not None:
|
|||
|
|
task_status = str(_safe_get(output, "taskStatus") or "")
|
|||
|
|
|
|||
|
|
results = _safe_get(output, "results", None)
|
|||
|
|
code = _safe_get(output, "code", None)
|
|||
|
|
message = _safe_get(output, "message", None)
|
|||
|
|
|
|||
|
|
if task_status and task_status.upper() not in ("SUCCEEDED", "SUCCESS"):
|
|||
|
|
error_detail = f"任务状态：{task_status}"
|
|||
|
|
if code:
|
|||
|
|
error_detail += f"，错误码：{code}"
|
|||
|
|
if message:
|
|||
|
|
error_detail += f"，消息：{message}"
|
|||
|
|
error_detail += f"，size={size}"
|
|||
|
|
return {
|
|||
|
|
"success": False,
|
|||
|
|
"error": error_detail,
|
|||
|
|
"prompt": prompt,
|
|||
|
|
"response": str(output) if output is not None else "无输出",
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if results and len(results) > 0:
|
|||
|
|
image_url = _safe_get(results[0], "url", None)
|
|||
|
|
if image_url is None and isinstance(results[0], dict):
|
|||
|
|
image_url = results[0].get("url")
|
|||
|
|
|
|||
|
|
task_id = _safe_get(output, "task_id", "") or _safe_get(output, "taskId", "") or ""
|
|||
|
|
|
|||
|
|
# 验证 image_url 不为空
|
|||
|
|
if not image_url:
|
|||
|
|
return {
|
|||
|
|
"success": False,
|
|||
|
|
"error": f"生成成功但图片URL为空（size={size}）",
|
|||
|
|
"prompt": prompt,
|
|||
|
|
"response": str(output) if output is not None else "无输出"
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"success": True,
|
|||
|
|
"image_url": image_url,
|
|||
|
|
"task_id": task_id,
|
|||
|
|
"prompt": prompt
|
|||
|
|
}
|
|||
|
|
else:
|
|||
|
|
# 详细错误信息
|
|||
|
|
error_detail = f"生成成功但未返回图片URL（size={size}）"
|
|||
|
|
if code:
|
|||
|
|
error_detail += f"，错误码：{code}"
|
|||
|
|
if message:
|
|||
|
|
error_detail += f"，消息：{message}"
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"success": False,
|
|||
|
|
"error": error_detail,
|
|||
|
|
"prompt": prompt,
|
|||
|
|
"response": str(output) if output is not None else "无输出"
|
|||
|
|
}
|
|||
|
|
else:
|
|||
|
|
# 详细错误信息
|
|||
|
|
error_msg = f"API调用失败，状态码：{status_code}"
|
|||
|
|
resp_message = _safe_get(response, "message", None)
|
|||
|
|
resp_code = _safe_get(response, "code", None)
|
|||
|
|
resp_request_id = _safe_get(response, "request_id", None) or _safe_get(response, "requestId", None)
|
|||
|
|
|
|||
|
|
if resp_message:
|
|||
|
|
error_msg += f"，消息：{resp_message}"
|
|||
|
|
if resp_code:
|
|||
|
|
error_msg += f"，错误码：{resp_code}"
|
|||
|
|
if resp_request_id:
|
|||
|
|
error_msg += f"，请求ID：{resp_request_id}"
|
|||
|
|
error_msg += f"，size={self.normalize_tongyi_image_size(size)}"
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"success": False,
|
|||
|
|
"error": error_msg,
|
|||
|
|
"prompt": prompt,
|
|||
|
|
"status_code": status_code
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
except ImportError:
|
|||
|
|
return {
|
|||
|
|
"success": False,
|
|||
|
|
"error": "未安装 dashscope 库，请运行：pip install dashscope",
|
|||
|
|
"prompt": prompt
|
|||
|
|
}
|
|||
|
|
except Exception as e:
|
|||
|
|
return {
|
|||
|
|
"success": False,
|
|||
|
|
"error": f"生成图片时出错：{str(e)}",
|
|||
|
|
"prompt": prompt
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def suggest_image_positions(
|
|||
|
|
self,
|
|||
|
|
content: str,
|
|||
|
|
llm_chain
|
|||
|
|
) -> List[Dict]:
|
|||
|
|
"""
|
|||
|
|
推荐图片插入位置
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
content: 文章内容
|
|||
|
|
llm_chain: LangChain 链对象
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
插入位置推荐列表，每个包含位置和理由
|
|||
|
|
"""
|
|||
|
|
try:
|
|||
|
|
prompt = PromptTemplate.from_template(self.image_position_template)
|
|||
|
|
chain = prompt | llm_chain | StrOutputParser()
|
|||
|
|
|
|||
|
|
result = chain.invoke({
|
|||
|
|
"content": content
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 解析结果
|
|||
|
|
positions = []
|
|||
|
|
lines = result.strip().split('\n')
|
|||
|
|
current_position = None
|
|||
|
|
current_reason = None
|
|||
|
|
|
|||
|
|
for line in lines:
|
|||
|
|
line = line.strip()
|
|||
|
|
if line.startswith('插入位置') or '位置' in line:
|
|||
|
|
if current_position:
|
|||
|
|
positions.append({
|
|||
|
|
"position": current_position,
|
|||
|
|
"reason": current_reason or "增强内容理解"
|
|||
|
|
})
|
|||
|
|
# 提取位置信息
|
|||
|
|
if '：' in line:
|
|||
|
|
current_position = line.split('：', 1)[1].strip()
|
|||
|
|
elif ':' in line:
|
|||
|
|
current_position = line.split(':', 1)[1].strip()
|
|||
|
|
elif line.startswith('理由') or '理由' in line:
|
|||
|
|
if '：' in line:
|
|||
|
|
current_reason = line.split('：', 1)[1].strip()
|
|||
|
|
elif ':' in line:
|
|||
|
|
current_reason = line.split(':', 1)[1].strip()
|
|||
|
|
|
|||
|
|
# 添加最后一个位置
|
|||
|
|
if current_position:
|
|||
|
|
positions.append({
|
|||
|
|
"position": current_position,
|
|||
|
|
"reason": current_reason or "增强内容理解"
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 如果没有解析到位置，使用基于规则的方法
|
|||
|
|
if not positions:
|
|||
|
|
positions = self._rule_based_positions(content)
|
|||
|
|
|
|||
|
|
return positions
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
# 如果生成失败，使用基于规则的方法
|
|||
|
|
return self._rule_based_positions(content)
|
|||
|
|
|
|||
|
|
def _rule_based_positions(self, content: str) -> List[Dict]:
|
|||
|
|
"""基于规则的简单位置推荐（备用方案）"""
|
|||
|
|
positions = []
|
|||
|
|
|
|||
|
|
# 按段落分割
|
|||
|
|
paragraphs = content.split('\n\n')
|
|||
|
|
|
|||
|
|
# 推荐位置1：标题后（如果有标题）
|
|||
|
|
if paragraphs and len(paragraphs[0]) < 100:
|
|||
|
|
positions.append({
|
|||
|
|
"position": "标题后，第一段前",
|
|||
|
|
"reason": "吸引读者注意力，增强视觉冲击力"
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 推荐位置2：中间关键段落
|
|||
|
|
if len(paragraphs) > 3:
|
|||
|
|
mid_index = len(paragraphs) // 2
|
|||
|
|
positions.append({
|
|||
|
|
"position": f"第{mid_index + 1}段后",
|
|||
|
|
"reason": "在关键内容处插入配图，增强理解"
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 如果没有找到合适位置，至少推荐一个
|
|||
|
|
if not positions:
|
|||
|
|
positions.append({
|
|||
|
|
"position": "文章开头",
|
|||
|
|
"reason": "增强视觉吸引力"
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
return positions[:2] # 最多返回2个位置
|
|||
|
|
|
|||
|
|
def embed_images_in_markdown(
|
|||
|
|
self,
|
|||
|
|
content: str,
|
|||
|
|
image_data: List[Dict]
|
|||
|
|
) -> str:
|
|||
|
|
"""
|
|||
|
|
将图片嵌入到 Markdown 文章中
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
content: 原始文章内容（Markdown格式）
|
|||
|
|
image_data: 图片数据列表，每个包含：
|
|||
|
|
{
|
|||
|
|
"image_url": str, # 图片URL
|
|||
|
|
"prompt": str, # 生成时的Prompt
|
|||
|
|
"position": str, # 插入位置描述（可选）
|
|||
|
|
"alt_text": str # 图片alt文本（可选）
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
嵌入图片后的 Markdown 内容
|
|||
|
|
"""
|
|||
|
|
if not image_data:
|
|||
|
|
return content
|
|||
|
|
|
|||
|
|
# 如果内容中有配图占位符，替换它们
|
|||
|
|
placeholders = self.extract_image_placeholders(content)
|
|||
|
|
|
|||
|
|
result_content = content
|
|||
|
|
|
|||
|
|
# 方法1：如果有占位符，按顺序替换
|
|||
|
|
if placeholders and len(placeholders) <= len(image_data):
|
|||
|
|
for i, placeholder in enumerate(placeholders):
|
|||
|
|
if i < len(image_data):
|
|||
|
|
img = image_data[i]
|
|||
|
|
alt_text = img.get("alt_text", img.get("prompt", "配图")[:50])
|
|||
|
|
markdown_image = f"\n\n\n\n"
|
|||
|
|
result_content = result_content.replace(placeholder["full_match"], markdown_image, 1)
|
|||
|
|
|
|||
|
|
# 方法2：如果没有占位符或图片数量多于占位符，在推荐位置插入
|
|||
|
|
elif image_data:
|
|||
|
|
# 按段落分割
|
|||
|
|
paragraphs = result_content.split('\n\n')
|
|||
|
|
|
|||
|
|
# 在合适位置插入图片
|
|||
|
|
insert_positions = []
|
|||
|
|
if len(paragraphs) > 1:
|
|||
|
|
# 第一张图：标题后
|
|||
|
|
insert_positions.append(1)
|
|||
|
|
# 后续图片：均匀分布
|
|||
|
|
if len(image_data) > 1:
|
|||
|
|
step = max(1, len(paragraphs) // len(image_data))
|
|||
|
|
for i in range(1, min(len(image_data), len(paragraphs) // step)):
|
|||
|
|
insert_positions.append(min((i + 1) * step, len(paragraphs) - 1))
|
|||
|
|
|
|||
|
|
# 插入图片
|
|||
|
|
offset = 0
|
|||
|
|
for idx, img in enumerate(image_data):
|
|||
|
|
if idx < len(insert_positions):
|
|||
|
|
pos = insert_positions[idx] + offset
|
|||
|
|
if pos < len(paragraphs):
|
|||
|
|
alt_text = img.get("alt_text", img.get("prompt", "配图")[:50])
|
|||
|
|
markdown_image = f"\n\n\n\n"
|
|||
|
|
paragraphs.insert(pos, markdown_image)
|
|||
|
|
offset += 1
|
|||
|
|
|
|||
|
|
result_content = '\n\n'.join(paragraphs)
|
|||
|
|
|
|||
|
|
return result_content
|
|||
|
|
|
|||
|
|
def generate_tongyi_prompt_from_content(
|
|||
|
|
self,
|
|||
|
|
content: str,
|
|||
|
|
brand: str,
|
|||
|
|
advantages: str,
|
|||
|
|
platform: str,
|
|||
|
|
keyword: str,
|
|||
|
|
llm_chain
|
|||
|
|
) -> str:
|
|||
|
|
"""
|
|||
|
|
从文章内容生成通义万相 Prompt（完整流程的第一步）
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
content: 文章内容
|
|||
|
|
brand: 品牌名称
|
|||
|
|
advantages: 品牌优势
|
|||
|
|
platform: 平台名称
|
|||
|
|
keyword: 关键词
|
|||
|
|
llm_chain: LangChain 链对象
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
生成的 Prompt 文本
|
|||
|
|
"""
|
|||
|
|
# 提取文章核心内容（前500字 + 后200字，确保覆盖主要观点）
|
|||
|
|
content_summary = content[:500] + "..." + content[-200:] if len(content) > 700 else content
|
|||
|
|
|
|||
|
|
return self.generate_tongyi_image_prompt(content_summary, brand, llm_chain)
|