zhoujie
b635108b89
- main.py: 4360 → 146 lines (96.6% reduction), entry layer only - services/: rate_limiter, autostart, persona, connection, profile, hotspot, content, engagement, scheduler, queue_ops (10 business modules) - ui/app.py: all Gradio UI code extracted into build_app(cfg, analytics) - Fix: with gr.Blocks() indented inside build_app function - Fix: cfg.all property (not get_all method) - Fix: STATUS_LABELS, get_persona_keywords, fetch_proactive_notes imports - Fix: queue_ops module-level set_publish_callback moved into configure() - Fix: pub_queue.format_*() wrapped as queue_format_table/calendar helpers - All 14 files syntax-verified, build_app() runtime-verified - 58/58 tasks complete"
209 lines
8.0 KiB
Python
209 lines
8.0 KiB
Python
"""
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services/content.py
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文案生成、图片生成、一键导出、发布到小红书
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"""
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import os
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import re
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import time
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import platform
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import subprocess
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import logging
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from PIL import Image
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from config_manager import ConfigManager, OUTPUT_DIR
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from llm_service import LLMService
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from sd_service import SDService, get_sd_preset
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from mcp_client import get_mcp_client
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from services.connection import _get_llm_config
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from services.persona import _resolve_persona
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logger = logging.getLogger("autobot")
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cfg = ConfigManager()
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def generate_copy(model, topic, style, sd_model_name, persona_text):
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"""生成文案（自动适配 SD 模型的 prompt 风格，支持人设）"""
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api_key, base_url, _ = _get_llm_config()
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if not api_key:
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return "", "", "", "", "❌ 请先配置并连接 LLM 提供商"
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try:
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svc = LLMService(api_key, base_url, model)
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persona = _resolve_persona(persona_text) if persona_text else None
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data = svc.generate_copy(topic, style, sd_model_name=sd_model_name, persona=persona)
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cfg.set("model", model)
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tags = data.get("tags", [])
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return (
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data.get("title", ""),
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data.get("content", ""),
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data.get("sd_prompt", ""),
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", ".join(tags) if tags else "",
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"✅ 文案生成完毕",
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)
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except Exception as e:
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logger.error("文案生成失败: %s", e)
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return "", "", "", "", f"❌ 生成失败: {e}"
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def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img, quality_mode, persona_text=None):
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"""生成图片（可选 ReActor 换脸，支持质量模式预设，支持人设视觉优化）"""
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if not model:
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return None, [], "❌ 未选择 SD 模型"
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try:
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svc = SDService(sd_url)
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# 判断是否启用换脸
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face_image = None
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if face_swap_on:
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# Gradio 可能传 PIL.Image / numpy.ndarray / 文件路径 / None
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if face_img is not None:
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if isinstance(face_img, Image.Image):
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face_image = face_img
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elif isinstance(face_img, str) and os.path.isfile(face_img):
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face_image = Image.open(face_img).convert("RGB")
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else:
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# numpy array 等其他格式
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try:
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import numpy as np
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if isinstance(face_img, np.ndarray):
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face_image = Image.fromarray(face_img).convert("RGB")
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logger.info("头像从 numpy array 转换为 PIL Image")
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except Exception as e:
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logger.warning("头像格式转换失败 (%s): %s", type(face_img).__name__, e)
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# 如果 UI 没传有效头像，从本地文件加载
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if face_image is None:
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face_image = SDService.load_face_image()
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if face_image is not None:
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logger.info("换脸头像已就绪: %dx%d", face_image.width, face_image.height)
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else:
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logger.warning("换脸已启用但未找到有效头像")
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persona = _resolve_persona(persona_text) if persona_text else None
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images = svc.txt2img(
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prompt=prompt,
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negative_prompt=neg_prompt,
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model=model,
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steps=int(steps),
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cfg_scale=float(cfg_scale),
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face_image=face_image,
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quality_mode=quality_mode,
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persona=persona,
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)
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preset = get_sd_preset(quality_mode)
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swap_hint = " (已换脸)" if face_image else ""
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return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint} [{quality_mode}]"
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except Exception as e:
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logger.error("图片生成失败: %s", e)
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return None, [], f"❌ 绘图失败: {e}"
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def one_click_export(title, content, images):
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"""导出文案和图片到本地"""
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if not title:
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return "❌ 无法导出：没有标题"
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safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20]
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folder_name = f"{int(time.time())}_{safe_title}"
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folder_path = os.path.join(OUTPUT_DIR, folder_name)
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os.makedirs(folder_path, exist_ok=True)
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with open(os.path.join(folder_path, "文案.txt"), "w", encoding="utf-8") as f:
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f.write(f"{title}\n\n{content}")
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saved_paths = []
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if images:
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for idx, img in enumerate(images):
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path = os.path.join(folder_path, f"图{idx+1}.jpg")
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if isinstance(img, Image.Image):
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if img.mode != "RGB":
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img = img.convert("RGB")
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img.save(path, format="JPEG", quality=95)
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saved_paths.append(os.path.abspath(path))
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# 尝试打开文件夹
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try:
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abs_path = os.path.abspath(folder_path)
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if platform.system() == "Windows":
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os.startfile(abs_path)
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elif platform.system() == "Darwin":
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subprocess.call(["open", abs_path])
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else:
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subprocess.call(["xdg-open", abs_path])
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except Exception:
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pass
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return f"✅ 已导出至: {folder_path} ({len(saved_paths)} 张图片)"
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def publish_to_xhs(title, content, tags_str, images, local_images, mcp_url, schedule_time):
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"""通过 MCP 发布到小红书（含输入校验和临时文件自动清理）"""
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# === 发布前校验 ===
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if not title:
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return "❌ 缺少标题"
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if len(title) > 20:
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return f"❌ 标题超长：当前 {len(title)} 字，小红书限制 ≤20 字，请精简后再发布"
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client = get_mcp_client(mcp_url)
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ai_temp_files: list = [] # 追踪本次写入的临时文件，用于 finally 清理
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try:
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# 收集图片路径
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image_paths = []
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# 先保存 AI 生成的图片到临时目录
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if images:
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temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish")
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os.makedirs(temp_dir, exist_ok=True)
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for idx, img in enumerate(images):
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if isinstance(img, Image.Image):
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path = os.path.abspath(os.path.join(temp_dir, f"ai_{idx}.jpg"))
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if img.mode != "RGB":
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img = img.convert("RGB")
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img.save(path, format="JPEG", quality=95)
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image_paths.append(path)
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ai_temp_files.append(path) # 登记临时文件
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# 添加本地上传的图片
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if local_images:
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for img_file in local_images:
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img_path = img_file.name if hasattr(img_file, 'name') else str(img_file)
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if os.path.exists(img_path):
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image_paths.append(os.path.abspath(img_path))
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# === 图片校验 ===
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if not image_paths:
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return "❌ 至少需要 1 张图片才能发布"
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if len(image_paths) > 18:
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return f"❌ 图片数量超限：当前 {len(image_paths)} 张，小红书限制 ≤18 张，请减少图片"
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for p in image_paths:
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if not os.path.exists(p):
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return f"❌ 图片文件不存在：{p}"
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# 解析标签
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tags = [t.strip().lstrip("#") for t in tags_str.split(",") if t.strip()] if tags_str else None
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# 定时发布
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schedule = schedule_time if schedule_time and schedule_time.strip() else None
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result = client.publish_content(
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title=title,
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content=content,
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images=image_paths,
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tags=tags,
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schedule_at=schedule,
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)
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if "error" in result:
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return f"❌ 发布失败: {result['error']}"
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return f"✅ 发布成功！\n{result.get('text', '')}"
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except Exception as e:
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logger.error("发布失败: %s", e)
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return f"❌ 发布异常: {e}"
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finally:
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# 清理本次写入的 AI 临时图片（无论成功/失败）
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for tmp_path in ai_temp_files:
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try:
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if os.path.exists(tmp_path):
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os.remove(tmp_path)
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except OSError as cleanup_err:
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logger.warning("临时文件清理失败 %s: %s", tmp_path, cleanup_err)
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