import uuid import httpx import logging from langchain_core.messages import ToolMessage from langchain_core.runnables import RunnableConfig from langchain_core.stores import BaseStore from minio import Minio from google import genai from pathlib import Path from datetime import datetime from langchain_core.tools import tool from google.oauth2 import service_account from langgraph.prebuilt import ToolRuntime from src.core.config import settings, MONGO_URI from src.server.deep_agent.utils.mongodb_util import ThreadImageMinIOStore from src.server.utils.new_oss_client import get_presigned_url, check_and_extract_minio_image # from google.genai.types import GenerateContentConfig, Modality # from langgraph.config import get_stream_writer # from src.server.utils.new_oss_client import oss_upload_image, oss_get_image, is_minio_file_exist, oss_upload_image_file # 初始化全局凭证和客户端 # creds = service_account.Credentials.from_service_account_file( # settings.GOOGLE_GENAI_USE_VERTEXAI, # scopes=["https://www.googleapis.com/auth/cloud-platform"], # ) # client = genai.Client( # credentials=creds, # project=settings.GOOGLE_CLOUD_PROJECT, # location=settings.GOOGLE_CLOUD_LOCATION, # vertexai=True # ) logger = logging.getLogger(__name__) minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) image_store = ThreadImageMinIOStore(MONGO_URI, "agent_tool_generate_db") def is_image_path_exist(image_path): try: return Path(image_path).exists() except: return False @tool async def generate_furniture(prompt: str, runtime: ToolRuntime): """ 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。 功能说明： - 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）。 - 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）。 参数说明： - 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。 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." 返回值： 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。 使用场景： - 从零创建新的家具设计方案 - 快速生成多种风格的家具概念图 - 室内设计初期灵感生成 - 家具产品可视化展示 注意： - 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。 - 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。 """ logger.info(f"\n[系统日志] 正在调用 generate_furniture ...") # thread_id = runtime.config.get("configurable").get("thread_id") try: # 1. 生成图像 - local flux2-klein object_name = f"furniture/sketches/{uuid.uuid4()}.png" bucket_name = "fida-public-bucket" # 替换为你的 bucket 名称 request_data = { "prompt": prompt, "bucket_name": bucket_name, "object_name": object_name, "width": 1024, "height": 1024 } async with httpx.AsyncClient(timeout=120) as client: resp = await client.post( f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict", json=request_data, ) result = resp.json() image_url = result.get("output_path", None) # image_presigned_url = get_presigned_url(oss_client=minio_client, bucket=bucket_name, object_name=object_name) if image_url: # image_store.save_image_path(thread_id=thread_id, object_path=image_url, metadata={"prompt": prompt, "generated_at": str(datetime.now())}) # image_id = str(uuid.uuid4()) # runtime.store.put( # namespace=("images", thread_id), # e.g. ("images", thread_id) # key=image_id, # value={ # "url": image_url, # "prompt": prompt, # "timestamp": str(uuid.uuid4()), # 或用 datetime # "version": "v1" # } # ) return f"Image has been generated: https://minio-api.aida.com.hk/{image_url}" else: return "Image generation failed." except Exception as e: logger.warning(f"绘图流程异常：{e}") return "generate furniture error" @tool async def edit_furniture(image_url: str, prompt: str, runtime: ToolRuntime, config: RunnableConfig): """ 使用先进的图像编辑模型（image editing model）对家具设计草图进行精准修改。 功能说明： - 输入一张家具图片（草图/效果图），根据用户提供的**详细英文提示词**，生成修改后的新家具图片。 - 修改后的图片会以 image_url 形式返回，自动加入对话上下文，后续 Agent 可以直接“看到”编辑结果并继续操作。 参数说明： - image_url (str): 原始家具图片的 URL（支持公开 http/https 链接或 data:image/...;base64 格式）。 - prompt (str): **必须是详细的英文提示词**，描述想要的具体修改（风格、颜色、材质、形状、添加/删除元素、比例等）。 示例："Change the sofa to a modern minimalist style with dark gray fabric and metal legs, add a matching coffee table, make the overall lighting warmer and more luxurious." 返回值： 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。 使用场景： - 家具设计迭代 - 室内设计方案修改 - 风格转换（现代/北欧/工业风等） - 材质/颜色调整 注意：如果需要多次迭代编辑，直接在下一次调用时传入上一次返回的新 image_url 即可。 """ logger.info(f"\n[系统日志] 正在调用 edit_furniture ...") thread_id = runtime.config.get("configurable").get("thread_id") input_path = [] try: user_input_image_paths = runtime.state.get("files").get("input_image", []) user_quote_image_path = runtime.state.get("files").get("quote_image", "") extract_result = check_and_extract_minio_image(url=image_url) if extract_result['state']: current_image_path = extract_result['data'] if len(user_input_image_paths) or current_image_path: for path in user_input_image_paths: input_path.append(path) if user_quote_image_path: input_path.append(user_quote_image_path) if not len(user_input_image_paths) and not user_quote_image_path: input_path = [current_image_path] object_name = f"furniture/sketches/{uuid.uuid4()}.png" bucket_name = "fida-public-bucket" # 替换为你的 bucket 名称 request_data = { "input_image_paths": input_path, "prompt": prompt, "bucket_name": bucket_name, "object_name": object_name, "width": 1024, "height": 1024 } async with httpx.AsyncClient(timeout=120) as client: resp = await client.post( f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict", json=request_data, ) result = resp.json() image_url = result.get("output_path", None) if image_url: # image_store.save_image_path(thread_id=thread_id, object_path=image_url, metadata={"prompt": prompt, "generated_at": str(datetime.now())}) return f"Image has been generated: https://minio-api.aida.com.hk/{image_url}" else: return "Image generation failed." else: return "The picture to be edited does not exist." else: return extract_result['message'] except Exception as e: logger.warning(f"edit_furniture error ：{e}") return "edit_furniture error" # def create_generate_furniture_tool(workspace_dir): # @tool # async def generate_furniture(prompt: str) -> str: # """ # 使用 Gemini 图像生成模型根据详细的英文提示词生成家具设计草图。 # """ # print(f"\n[系统日志] 正在调用 Nano Banana (Gemini Image Gen) ...") # # try: # response = client.models.generate_content( # model="gemini-2.5-flash-image", # contents=(f"Generate a professional furniture design sketch: {prompt}"), # config=GenerateContentConfig( # response_modalities=[Modality.TEXT, Modality.IMAGE], # ), # ) # # image_bytes = None # for part in response.candidates[0].content.parts: # if part.inline_data: # image_bytes = part.inline_data.data # break # # if not image_bytes: # return "未能生成图像数据。" # # 1. 定义OSS存储路径和本地保存路径 # object_name = f"furniture/sketches/{uuid.uuid4()}.png" # bucket = "fida-test" # 替换为你的 bucket 名称 # filename = os.path.join(workspace_dir, f"{bucket}/{object_name}") # # # 2. 创建本地目录（确保目录存在） # local_dir = os.path.dirname(filename) # if not os.path.exists(local_dir): # os.makedirs(local_dir, exist_ok=True) # # # 3. 保存图片到本地文件（新增核心逻辑） # try: # with open(filename, "wb") as f: # f.write(image_bytes) # print(f"[系统日志] 图片已保存到本地：{filename}") # except Exception as save_e: # logger.warning(f"保存图片到本地失败：{save_e}") # # 本地保存失败不中断上传流程，仅记录日志 # # # 4. 上传图片到OSS（原有逻辑） # upload_res = oss_upload_image( # oss_client=minio_client, # bucket=bucket, # object_name=object_name, # image_bytes=image_bytes # ) # # if upload_res: # image_url = f"{bucket}/{object_name}" # return image_url # else: # return f"图片生成成功（本地路径：{filename}），但上传至存储服务器失败。" # # except Exception as e: # logger.warning(f"绘图流程异常：{e}") # return "绘图流程异常" # # return generate_furniture