33a165a615
- app.py: Streamlit UI for video generation workflow - main_flow.py: CLI tool with argparse support - modules/: Business logic modules (script_gen, image_gen, video_gen, composer, etc.) - config.py: Configuration with API keys and paths - requirements.txt: Python dependencies - docs/: System prompt documentation
347 lines
12 KiB
Python
347 lines
12 KiB
Python
"""
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MatchMe Studio - Brain Module (Multi-stage Analysis & Script Generation)
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"""
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import json
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import logging
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from typing import Dict, Any, List, Optional
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from openai import OpenAI
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import config
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logger = logging.getLogger(__name__)
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# Use Volcengine (Doubao) via OpenAI Compatible Interface
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client = OpenAI(
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api_key=config.VOLC_API_KEY,
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base_url=config.VOLC_BASE_URL
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)
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# ============================================================
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# Stage 1: Analyze Materials
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# ============================================================
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ANALYZE_SYSTEM_PROMPT = """你是一位资深短视频创作总监，专精TikTok/抖音爆款内容。
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任务：深度分析用户提供的素材和需求，识别产品特性、使用场景、目标人群。
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分析维度：
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1. 产品/服务核心卖点（从素材中提取视觉特征）
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2. 视觉风格特征（颜色、质感、包装）
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3. 潜在目标受众
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4. 内容调性建议
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然后检查是否缺少关键信息，如果缺少，生成2-5个问题帮助完善需求。
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每个问题必须与短视频创作直接相关。
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输出严格JSON格式：
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{
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"analysis": "详细分析结果，包括从素材中识别到的视觉元素...",
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"detected_info": {
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"product": "识别到的产品名称和类型",
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"visual_features": ["视觉特征1", "视觉特征2"],
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"audience": "推测的目标人群",
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"style": "推测的风格"
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},
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"missing_info": ["缺少的信息1", "缺少的信息2"],
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"questions": [
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{
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"id": "q1",
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"text": "问题文字（说明为什么这个问题重要）",
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"options": ["选项A", "选项B", "选项C"],
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"allow_multiple": true,
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"allow_custom": true
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}
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],
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"ready": false
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}
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如果信息足够，ready=true，questions为空数组。
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"""
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def analyze_materials(
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prompt: str,
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image_urls: List[str] = None,
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asr_text: str = ""
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) -> Dict[str, Any]:
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"""
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Deep analysis of user materials.
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Returns analysis text and questions if info is missing.
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"""
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logger.info("Brain: Analyzing materials...")
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# Using Vision Model format (Doubao Vision)
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# Input format: messages with content list (text + image_url)
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content_parts = [{"type": "text", "text": f"用户需求: {prompt}"}]
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if asr_text:
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content_parts.append({"type": "text", "text": f"\n视频原声(ASR转写): {asr_text}"})
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if image_urls:
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content_parts.append({"type": "text", "text": "\n用户上传的素材图片（请仔细分析这些图片中的产品特征）:"})
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for url in image_urls:
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content_parts.append({
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"type": "image_url",
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"image_url": {"url": url}
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})
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messages = [
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# Note: Some vision models might not support 'system' role with images well,
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# but Doubao usually follows standard chat structure.
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# If system prompt fails, prepend it to user content.
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{"role": "system", "content": ANALYZE_SYSTEM_PROMPT},
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{"role": "user", "content": content_parts}
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]
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try:
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# Use Vision Model for Analysis
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response = client.chat.completions.create(
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model=config.VISION_MODEL_ID,
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messages=messages,
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temperature=0.7,
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max_tokens=4000
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)
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content = response.choices[0].message.content.strip()
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if content.startswith("```"):
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parts = content.split("```")
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if len(parts) > 1:
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content = parts[1]
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if content.startswith("json"): content = content[4:]
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return json.loads(content)
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except Exception as e:
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logger.error(f"Brain Analyze Error: {e}")
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raise
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# ============================================================
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# Stage 2: Refine Brief with Answers
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# ============================================================
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REFINE_SYSTEM_PROMPT = """你是短视频创作总监。
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根据原始需求、AI分析结果、用户补充回答，整合为完整的创意简报。
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注意：用户选择的风格偏好（如ASMR、剧情、视觉流等）必须作为核心创作方向贯穿整个简报。
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输出JSON:
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{
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"brief": {
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"product": "产品名称",
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"product_visual_description": "产品视觉描述（颜色、形状、包装、质感等，用于后续图片生成）",
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"selling_points": ["卖点1", "卖点2"],
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"target_audience": "目标人群",
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"platform": "投放平台",
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"style": "视频风格（必须明确，如ASMR/剧情/视觉流等）",
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"style_requirements": "该风格的具体创作要求（如ASMR需要：开盖声、质感特写、无人脸等）",
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"creativity_level": "创意程度",
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"reference": "对标账号/竞品",
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"user_assets_description": "用户上传素材的描述（用于后续继承）"
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},
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"creative_summary": "整体创意概述（50字以内，描述这个视频的核心创意方向）",
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"ready": true
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}
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"""
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def refine_brief(
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original_prompt: str,
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analysis: Dict[str, Any],
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answers: Dict[str, Any],
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image_urls: List[str] = None
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) -> Dict[str, Any]:
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"""
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Integrate user answers into a complete creative brief.
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"""
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logger.info("Brain: Refining brief with answers...")
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user_content = f"""
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原始需求: {original_prompt}
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AI分析结果: {json.dumps(analysis, ensure_ascii=False)}
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用户补充回答: {json.dumps(answers, ensure_ascii=False)}
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用户上传的素材URL: {json.dumps(image_urls or [], ensure_ascii=False)}
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"""
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try:
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# Use Text LLM for reasoning/refining if no new images involved
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# But to keep it simple, we can stick to BRAIN_MODEL_ID (Doubao Pro)
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response = client.chat.completions.create(
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model=config.BRAIN_MODEL_ID,
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messages=[
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{"role": "system", "content": REFINE_SYSTEM_PROMPT},
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{"role": "user", "content": user_content}
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],
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temperature=0.5,
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max_tokens=3000
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)
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content = response.choices[0].message.content.strip()
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if content.startswith("```"):
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parts = content.split("```")
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if len(parts) > 1:
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content = parts[1]
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if content.startswith("json"): content = content[4:]
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return json.loads(content)
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except Exception as e:
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logger.error(f"Brain Refine Error: {e}")
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raise
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# ============================================================
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# Stage 3: Generate Script
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# ============================================================
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SCRIPT_SYSTEM_PROMPT = """你是顶级短视频编导，专精{style}风格内容创作。
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根据创意简报生成爆款脚本。必须严格遵循用户选择的风格要求。
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脚本结构要求：
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1. creative_summary: 整体创意概述（这条视频的核心创意是什么）
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2. hook: 前3秒钩子设计（必须抓眼球，符合{style}风格）
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3. scenes: 3-8个分镜
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4. cta: 结尾行动号召（纯文本字符串）
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每个分镜(scene)必须包含：
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- id: 分镜编号
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- duration: 时长(5/10/15秒，符合视频模型参数)
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- timeline: 时间轴 (如 "0:00-0:05")
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- image_prompt: 【关键】用于AI生图的详细英文prompt，必须包含：
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* 产品的具体视觉描述（继承自brief中的product_visual_description）
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* 8k, hyper-realistic, cinematic lighting
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* 色调、环境、构图、焦点
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* 风格要求（如ASMR需要：macro shot, satisfying texture, no human face）
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- keyframe: {
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"color_tone": "色调",
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"environment": "环境/背景",
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"foreground": "前景元素",
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"focus": "视觉焦点",
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"subject": "主体描述",
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"composition": "构图方式"
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}
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- camera_movement: 运镜描述（如：slow zoom in, pan left, static）
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- story_beat: 这个分镜在整体故事中的作用
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- voiceover: 旁白文字（{style}风格，如ASMR应简短或无旁白，用音效代替）
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- sound_design: 音效设计（如：开盖声、水滴声、环境白噪音）
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- rhythm: {"change": "保持/加快/放慢", "multiplier": 1.0}
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旁白要求：
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- 必须连贯，形成完整的叙事
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- 符合{style}风格（ASMR风格应极简或无旁白）
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- 每句旁白要能独立成句，但连起来是完整故事
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输出严格JSON格式。
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"""
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def generate_script(
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brief: Dict[str, Any],
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image_urls: List[str] = None,
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regenerate_feedback: str = ""
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) -> Dict[str, Any]:
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"""
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Generate complete video script with scenes.
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"""
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logger.info("Brain: Generating script...")
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style = brief.get("style", "现代广告")
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system_prompt = SCRIPT_SYSTEM_PROMPT.replace("{style}", style)
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content_parts = [{"type": "text", "text": f"创意简报: {json.dumps(brief, ensure_ascii=False)}"}]
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if regenerate_feedback:
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content_parts.append({"type": "text", "text": f"\n用户反馈(请据此调整): {regenerate_feedback}"})
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if image_urls:
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content_parts.append({"type": "text", "text": "\n用户上传的参考素材（生成的image_prompt必须参考这些素材中的产品外观）:"})
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for url in image_urls:
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content_parts.append({
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"type": "image_url",
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"image_url": {"url": url}
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})
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try:
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response = client.chat.completions.create(
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model=config.VISION_MODEL_ID, # Use Vision model to see reference images if available
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": content_parts}
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],
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temperature=0.8,
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max_tokens=8000
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)
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content = response.choices[0].message.content.strip()
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if content.startswith("```"):
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parts = content.split("```")
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if len(parts) > 1:
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content = parts[1]
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if content.startswith("json"): content = content[4:]
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return json.loads(content)
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except Exception as e:
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logger.error(f"Brain Script Error: {e}")
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raise
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# ============================================================
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# Stage 4: Regenerate Single Scene
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# ============================================================
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def regenerate_scene(
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full_script: Dict[str, Any],
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scene_id: int,
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feedback: str,
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brief: Dict[str, Any] = None
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) -> Dict[str, Any]:
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"""
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Regenerate a single scene based on feedback.
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"""
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logger.info(f"Brain: Regenerating scene {scene_id}...")
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style = brief.get("style", "现代广告") if brief else "现代广告"
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system_prompt = f"""你是短视频编导，专精{style}风格。根据用户反馈重新生成指定分镜。
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保持与其他分镜的风格连贯性。
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image_prompt必须继承产品的视觉描述。
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只输出新的scene对象(JSON)。
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"""
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user_content = f"""
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完整脚本: {json.dumps(full_script, ensure_ascii=False)}
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创意简报: {json.dumps(brief, ensure_ascii=False) if brief else "无"}
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需要重新生成的分镜ID: {scene_id}
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用户反馈: {feedback}
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"""
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try:
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response = client.chat.completions.create(
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model=config.BRAIN_MODEL_ID,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_content}
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],
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temperature=0.8,
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max_tokens=2000
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)
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content = response.choices[0].message.content.strip()
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if content.startswith("```"):
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parts = content.split("```")
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if len(parts) > 1:
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content = parts[1]
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if content.startswith("json"): content = content[4:]
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return json.loads(content)
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except Exception as e:
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logger.error(f"Brain Regenerate Scene Error: {e}")
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raise
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