969ca660c0
铁律合规: - P0: 移除三处硬编码API密钥 → lib/secrets.py 三层查找 - P0: 统一LIB/engines调用关系(tts复用音色表,composer桥接editor) - P0: batch支持参数+batch-resume断点续传 - P0: Seed一致性(seed_value/lock_seed字段+gen --lock-seed) - P0: 跨集资产继承(inherit命令)+A/B对比(compare命令) - P1: concat逻辑修复/滤镜崩溃修复/FFmpeg路径统一/成本追踪 - P2: 状态判断修复/CLI --help+--version+模糊匹配 - 新增: 65条命令(原17条)覆盖全链路 - 新增: deps/info/note/history/report/flow/director等 - 语法OK 零重复函数 零密钥泄露
125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
# LIB · 豆包对话模型适配器（剧本拆解/分镜生成）
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# D180 · 2026-07-10
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"""火山方舟 ARK Chat API → doubao-seed 系列模型"""
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import json
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import os
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import subprocess
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import tempfile
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from lib.secrets import get_ark_key
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ARK_KEY = get_ark_key()
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ARK_CHAT_URL = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
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# 可用模型
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MODELS = {
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"pro": "doubao-seed-2-1-pro-260628", # 最强·适合复杂剧本
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"lite": "doubao-seed-2-0-lite-260215", # 轻量·适合批量
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"deepseek": "deepseek-v4-pro-260425", # 深度思<E5BAA6><E6809D><EFBFBD>·适合拆解
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}
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def _curl_api(payload):
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"""通过 subprocess curl 调用 API（绕过 Windows urllib 超时问题）"""
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import subprocess, tempfile
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tf = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False, encoding='utf-8')
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json.dump(payload, tf, ensure_ascii=False)
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tf.close()
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try:
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result = subprocess.run([
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"curl", "-s", ARK_CHAT_URL,
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"-H", f"Authorization: Bearer {ARK_KEY}",
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"-H", "Content-Type: application/json",
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"-d", f"@{tf.name}"
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], capture_output=True, text=True, timeout=120)
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os.unlink(tf.name)
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return json.loads(result.stdout) if result.stdout else {"error": result.stderr}
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except subprocess.TimeoutExpired:
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os.unlink(tf.name)
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return {"error": "timeout"}
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def chat(prompt, system="你是一个专业的短剧剧本分析专家", model="pro", temperature=0.3, max_tokens=4096):
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"""调用豆包对话模型"""
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payload = {
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"model": MODELS.get(model, model),
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": prompt}
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],
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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result = _curl_api(payload)
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if "error" in result:
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return result
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choice = result.get("choices", [{}])[0]
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return {
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"content": choice.get("message", {}).get("content", ""),
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"model": result.get("model", ""),
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"tokens": result.get("usage", {}),
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"finish": choice.get("finish_reason", "")
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}
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def breakdown_script(script_text, episode_num=1):
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"""拆解一集剧本为结构化分镜"""
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prompt = f"""请将以下短剧剧本第{episode_num}集拆解为结构化分镜。
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要求：
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1. 按镜头拆分，每个镜头包含：镜头编号、景别（特写/近景/中景/全景/POV）、时长（秒）
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2. 提取每个镜头中出现的角色、场景、道具
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3. 写出每个镜头的画面描述（50字以内）
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4. 标注镜头类型：establishing/action/reaction/closeup/transition
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输出JSON格式：
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{{
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"episode": {episode_num},
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"shots": [
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{{
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"shot_number": "S01",
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"description": "画面描述",
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"camera": "POV|特写|近景|中景|全景",
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"duration": 6,
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"type": "establishing|action|reaction|closeup|transition",
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"characters": ["角色名"],
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"scenes": ["场景名"],
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"props": ["道具名"],
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"dialogue": null
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}}
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]
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}}
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剧本内容：
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{script_text}"""
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return chat(prompt, system="你是专业的短剧剧本拆解专家，输出纯JSON，不添加任何解释。")
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def generate_keyframes(shot_description, character_refs, scene_refs):
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"""为单个镜头生成关键帧 prompt"""
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prompt = f"""基于以下镜头描述，生成即梦4.0图像生成提示词。
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镜头：{shot_description}
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可用角色：{json.dumps(character_refs, ensure_ascii=False)}
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可用场景：{json.dumps(scene_refs, ensure_ascii=False)}
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要求：
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1. 提示词用英文
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2. 包含景别、角色位置、场景细节、光照、风格
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3. 不超过150词
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4. 输出纯提示词，不加任何解释"""
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return chat(prompt, model="pro", temperature=0.5, max_tokens=300)
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# ====== CLI ======
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if __name__ == "__main__":
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import sys
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if len(sys.argv) < 2:
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print("Usage: python doubao_chat.py <command> [args]")
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print(" chat <prompt> 直接对话")
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print(" breakdown <file> 拆解剧本文件")
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sys.exit(1)
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cmd = sys.argv[1]
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if cmd == "chat":
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r = chat(sys.argv[2])
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print(r["content"] if "content" in r else r)
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elif cmd == "breakdown":
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with open(sys.argv[2], "r", encoding="utf-8") as f:
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script = f.read()
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r = breakdown_script(script)
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print(r["content"] if "content" in r else r)
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