299 lines
14 KiB
Python
Executable File
299 lines
14 KiB
Python
Executable File
import uuid
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from typing import Optional
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import httpx
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import logging
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from langchain_core.runnables import RunnableConfig
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from minio import Minio
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# from pathlib import Path
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# from datetime import datetime
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from langchain_core.tools import tool
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from langgraph.prebuilt import ToolRuntime
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from src.core.config import settings, MONGO_URI
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# from src.server.deep_agent.utils.mongodb_util import ThreadImageMinIOStore
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logger = logging.getLogger(__name__)
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minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
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# image_store = ThreadImageMinIOStore(MONGO_URI, "agent_tool_generate_db")
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@tool
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async def generate_furniture(runtime: ToolRuntime, prompts: list[str] = None, num_images: Optional[int] = 1):
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"""
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使用图像生成模型根据用户提供的详细英文提示词，从零生成一张全新的家具设计草图。
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功能说明：
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- 输入一段详细的英文描述，即可生成一张高品质的家具设计图片（可用于草图、效果图、渲染图等）。
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- 生成后的图片会以 image_url 形式返回，自动加入对话上下文，后续 Agent 可以直接“看到”生成的家具图片并继续操作（描述、编辑、迭代等）。
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参数说明：
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- prompt (str): **必须是详细的英文提示词**，越详细越好，包含家具类型、风格、颜色、材质、尺寸比例、背景、视角、光影等具体要求。
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示例："Generate a modern minimalist dining chair made of light oak wood and white leather, with slim metal legs, photographed in a bright Scandinavian living room with natural sunlight, high detail, 8k resolution."
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- num_images (int, 可选): 要生成的图片数量，默认 1 张。最大只能是 4 张。如果输入超过 4，会自动限制为 4。
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返回值：
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返回新生成家具图片的 image_url，后续对话中 Agent 可直接引用该图片进行描述、进一步编辑或分析。
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使用场景：
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- 从零创建新的家具设计方案
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- 快速生成多种风格的家具概念图
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- 室内设计初期灵感生成
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- 家具产品可视化展示
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注意：
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- 生成的图片会自动携带到整个对话上下文中，支持后续使用 edit_furniture 等工具进行迭代修改。
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- 如果需要生成多个方案，可以多次调用本工具或在 prompt 中明确要求生成不同变体。
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"""
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if num_images is None or num_images < 1:
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num_images = 1
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elif num_images > 4:
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num_images = 4
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# current_checkpoint_id = runtime.store.get(namespace=("image_history",), key="checkpoint_id", ).value.get("current_checkpoint_id")
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logger.info(f"\n[系统日志] 正在调用 generate_furniture ")
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try:
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bucket_name = "fida-public-bucket"
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object_name = f"furniture/sketches/{uuid.uuid4()}"
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image_urls = []
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for i in range(num_images):
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image_urls.append(await generate_or_edit_image(prompt=prompts[i], bucket_name=bucket_name, object_name=f"{object_name}-{i}.png"))
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# if image_urls:
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# image_store.save_image_path(thread_id=current_checkpoint_id, object_path=image_urls, metadata={"prompt": prompt, "generated_at": str(datetime.now())})
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return image_urls
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# else:
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# return "Image generation failed."
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except Exception as e:
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logger.warning(f"绘图流程异常：{e}")
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return "generate furniture error"
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@tool
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async def edit_furniture(runtime: ToolRuntime, config: RunnableConfig, input_image_paths: list[str] = None, prompts: list[str] = None, ):
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"""
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使用先进的图像编辑模型对家具设计草图进行精准修改。
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功能说明：
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- 支持批量处理多张家具图片，根据对应的提示词生成修改后的新图片。
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- input_image_paths 和 prompts 必须一一对应，数量完全一致。
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- 最多支持同时处理 4 对图片和提示词（即最多 4 张图片）。
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参数说明：
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- input_image_paths (list[str]):
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输入图片在 MinIO 中的存储路径列表。
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示例：["furniture/designs/sofa_concept_v1.png", "projects/room_2026/chair_v2.jpg"]
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注意：路径必须是有效的 MinIO 对象路径，工具会自动下载对应图片。
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- prompts (list[str]):
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必须是列表，即使只有一个提示词也要用 ["你的提示词"] 格式。
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与图片一一对应的详细英文提示词列表。
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每个提示词描述对对应图片的具体修改要求（风格、颜色、材质、形状、添加/删除元素等）。
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示例：["Change the sofa to a modern minimalist style with dark gray fabric and metal legs, add a matching coffee table.",
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"Convert the chair to Scandinavian Nordic style with light wood and soft beige upholstery."]
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使用要求（重要）：
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- input_image_paths 和 prompts 的长度必须完全相同。
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- 列表长度必须在 1 到 4 之间（最多 4 对）。
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- input_image_paths[0] 对应 prompts[0]，以此类推，一一对应进行编辑。
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使用场景：
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- 家具设计方案迭代
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- 室内设计多方案对比修改
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- 批量风格转换（现代/北欧/工业/奢华风等）
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- 材质、颜色、细节批量调整
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示例调用：
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input_image_paths = ["designs/sofa1.png", "designs/chair1.png"]
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prompts = [
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"Make the sofa more luxurious with velvet fabric and gold accents.",
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"Change the chair to a sleek modern design with black leather and chrome legs."
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]
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"""
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# image_history = runtime.store.get(namespace=("image_history",), key="checkpoint_id", )
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# last_checkpoint_id = image_history.value.get("last_checkpoint_id")
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# current_checkpoint_id = image_history.value.get("current_checkpoint_id")
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#
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# logger.info(f"\n[系统日志] 正在调用 edit_furniture ...current_checkpoint_id={current_checkpoint_id} --- last_checkpoint_id={last_checkpoint_id}")
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#
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# if image_store.get_image_path(last_checkpoint_id):
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# current_image_path = image_store.get_image_path(last_checkpoint_id).get("current_image_path", False)
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# if current_image_path:
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# if isinstance(current_image_path, list):
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# # 只取最后一张
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# current_image_path = current_image_path[-1]
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# else:
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# current_image_path = None
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# input_path = []
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try:
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# user_input_image_paths = runtime.state.get("files").get("input_image", [])
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# user_quote_image_path = runtime.state.get("files").get("quote_image", "")
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result = []
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if len(input_image_paths):
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for i in range(len(input_image_paths)):
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bucket_name = "fida-public-bucket"
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object_name = f"furniture/sketches/{uuid.uuid4()}.png"
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image_url = await generate_or_edit_image(input_path=[input_image_paths[i]], prompt=prompts[i], bucket_name=bucket_name, object_name=f"{object_name}-{i}.png")
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result.append(image_url)
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# image_url = await generate_or_edit_image(input_path=input_path, prompt=prompt, bucket_name=bucket_name, object_name=object_name)
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# if image_url:
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# image_store.save_image_path(thread_id=current_checkpoint_id, object_path=[image_url], metadata={"prompt": prompt, "generated_at": str(datetime.now())})
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return result
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# else:
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# return "Image generation failed."
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# else:
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# return "The picture to be edited does not exist."
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# else:
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# return "No recent image found, please upload or cite it"
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except Exception as e:
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logger.warning(f"edit_furniture error ：{e}")
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return "edit_furniture error"
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@tool
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async def edit_quote_upload_furniture(image_paths: list[str] = None, mode: str = "auto", prompts: list[str] = None, ):
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"""
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使用先进的图像编辑模型对家具图片进行精准批量修改。
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支持四种模式：
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- one_to_one（最常用）：多张图片 + 多个提示词，一一对应编辑
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- one_to_many：多张图片 + 1个提示词（所有图片统一修改）
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- many_to_one：1张图片 + 多个提示词（同一张图生成多个不同变体，例如不同颜色）
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- many_to_many（新增）：多张图片 + 多个提示词，一一对应（多对多交叉编辑）
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参数说明：
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- image_paths (list[str]): MinIO 图片路径列表，长度建议 1~4
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- prompts (list[str]): 详细英文提示词列表
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- mode (str): "one_to_one", "one_to_many", "many_to_one", "many_to_many", "auto"（默认自动判断）
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使用要求：
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- image_paths 长度必须在 1~4 之间
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- mode="auto" 时会根据长度智能判断
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- many_to_many 模式下：image_paths 和 prompts 的长度必须完全相同
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示例：
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示例1：many_to_many（多对多，一一对应）
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image_paths = ["sofa1.png", "chair1.png", "table1.png"]
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prompts = [
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"Change to bright yellow modern style.",
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"Change to deep green luxury style.",
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"Change to soft beige Scandinavian style."
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]
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mode = "many_to_many"
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示例2：many_to_one（同一张图多个颜色版本）
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image_paths = ["sofa_original.png"]
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prompts = ["yellow version", "green version", "blue version", "black version"]
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mode = "many_to_one"
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"""
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try:
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# ====================== 参数校验（直接返回错误信息） ======================
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if not image_paths or len(image_paths) < 1 or len(image_paths) > 4:
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return f"参数错误：image_paths 必须提供，且长度需要在 1 到 4 张之间。目前收到 {len(image_paths) if image_paths else 0} 张。"
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if not prompts:
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return "参数错误：prompts 不能为空，请至少提供一个修改提示词。"
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if mode not in ["one_to_one", "one_to_many", "many_to_one", "many_to_many", "auto"]:
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return f"参数错误：mode 参数无效。可用值：one_to_one, one_to_many, many_to_one, many_to_many, auto。当前收到：{mode}"
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# Auto 模式智能判断
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if mode == "auto":
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if len(image_paths) == 1 and len(prompts) > 1:
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mode = "many_to_one"
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elif len(prompts) == 1:
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mode = "one_to_many"
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elif len(image_paths) == len(prompts):
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mode = "many_to_many" # 新增：数量相等时优先 many_to_many
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else:
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mode = "one_to_one"
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# 各模式严格校验
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if mode == "many_to_one":
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if len(image_paths) != 1:
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return f"参数错误：many_to_one 模式只能传入 1 张图片，当前传入了 {len(image_paths)} 张。"
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if len(prompts) < 1:
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return "参数错误：many_to_one 模式下 prompts 至少需要 1 个。"
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elif mode == "one_to_many":
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if len(prompts) != 1:
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return f"参数错误：one_to_many 模式下 prompts 必须只有 1 个，当前有 {len(prompts)} 个。"
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elif mode in ["one_to_one", "many_to_many"]:
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if len(prompts) != len(image_paths):
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return (f"参数错误：{mode} 模式下 image_paths 和 prompts 数量必须完全一致。\n"
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f"当前 image_paths 有 {len(image_paths)} 张，prompts 有 {len(prompts)} 个。")
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# ====================== 执行编辑 ======================
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result = []
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bucket_name = "fida-public-bucket"
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if mode == "many_to_one":
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# 同一张图片 + 多个 prompt
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base_image = image_paths[0]
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for i, prompt in enumerate(prompts):
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object_name = f"furniture/sketches/{uuid.uuid4()}.png"
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image_url = await generate_or_edit_image(
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input_path=[base_image],
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prompt=prompt,
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bucket_name=bucket_name,
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object_name=f"{object_name}-var{i}.png"
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)
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result.append(image_url)
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else:
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# one_to_one、many_to_many、one_to_many 统一处理
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for i in range(len(image_paths)):
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# 根据模式决定当前使用的 prompt
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if mode == "one_to_many":
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current_prompt = prompts[0]
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else:
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current_prompt = prompts[i] # one_to_one 和 many_to_many 都用对应位置的 prompt
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object_name = f"furniture/sketches/{uuid.uuid4()}.png"
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image_url = await generate_or_edit_image(
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input_path=[image_paths[i]],
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prompt=current_prompt,
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bucket_name=bucket_name,
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object_name=f"{object_name}-{i}.png"
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)
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result.append(image_url)
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return result
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except Exception as e:
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logger.error(f"edit_quote_upload_furniture 执行异常: {e}", exc_info=True)
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return f"工具执行失败：{str(e)}，请检查参数后重试。"
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async def generate_or_edit_image(input_path=None, bucket_name="fida-public-bucket",
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object_name=f"furniture/sketches/{uuid.uuid4()}.png",
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prompt="Generate a modern minimalist dining chair made of light "
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"oak wood and white leather, with slim metal legs, photographed "
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"in a bright Scandinavian living room with natural sunlight, high detail, "
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"8k resolution."):
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if input_path is None:
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input_path = []
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request_data = {
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"input_image_paths": input_path,
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"prompt": prompt,
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"bucket_name": bucket_name,
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"object_name": object_name,
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"width": 1024,
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"height": 1024
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}
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async with httpx.AsyncClient(timeout=120) as client:
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resp = await client.post(
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f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict",
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json=request_data,
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)
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result = resp.json()
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image_url = result.get("output_path", None)
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return image_url
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