新增图片上下文存储
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@@ -2,48 +2,20 @@ import uuid
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import httpx
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import logging
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from langchain_core.messages import ToolMessage
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from langchain_core.runnables import RunnableConfig
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from langchain_core.stores import BaseStore
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from minio import Minio
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from google import genai
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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 google.oauth2 import service_account
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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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from src.server.utils.new_oss_client import get_presigned_url, check_and_extract_minio_image
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# from google.genai.types import GenerateContentConfig, Modality
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# from langgraph.config import get_stream_writer
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# from src.server.utils.new_oss_client import oss_upload_image, oss_get_image, is_minio_file_exist, oss_upload_image_file
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# 初始化全局凭证和客户端
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# creds = service_account.Credentials.from_service_account_file(
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# settings.GOOGLE_GENAI_USE_VERTEXAI,
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# scopes=["https://www.googleapis.com/auth/cloud-platform"],
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# )
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# client = genai.Client(
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# credentials=creds,
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# project=settings.GOOGLE_CLOUD_PROJECT,
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# location=settings.GOOGLE_CLOUD_LOCATION,
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# vertexai=True
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# )
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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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def is_image_path_exist(image_path):
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try:
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return Path(image_path).exists()
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except:
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return False
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@tool
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async def generate_furniture(prompt: str, runtime: ToolRuntime):
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"""
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@@ -70,41 +42,16 @@ async def generate_furniture(prompt: str, runtime: ToolRuntime):
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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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logger.info(f"\n[系统日志] 正在调用 generate_furniture ...")
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# thread_id = runtime.config.get("configurable").get("thread_id")
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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 ...当前checkpoint_id={current_checkpoint_id}")
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try:
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# 1. 生成图像 - local flux2-klein
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object_name = f"furniture/sketches/{uuid.uuid4()}.png"
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bucket_name = "fida-public-bucket" # 替换为你的 bucket 名称
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request_data = {
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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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# image_presigned_url = get_presigned_url(oss_client=minio_client, bucket=bucket_name, object_name=object_name)
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image_url = await generate_or_edit_image(prompt=prompt)
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if image_url:
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# image_store.save_image_path(thread_id=thread_id, object_path=image_url, metadata={"prompt": prompt, "generated_at": str(datetime.now())})
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# image_id = str(uuid.uuid4())
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# runtime.store.put(
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# namespace=("images", thread_id), # e.g. ("images", thread_id)
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# key=image_id,
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# value={
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# "url": image_url,
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# "prompt": prompt,
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# "timestamp": str(uuid.uuid4()), # 或用 datetime
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# "version": "v1"
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# }
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# )
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return f"Image has been generated: https://minio-api.aida.com.hk/{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 image_url
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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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@@ -113,141 +60,88 @@ async def generate_furniture(prompt: str, runtime: ToolRuntime):
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@tool
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async def edit_furniture(image_url: str, prompt: str, runtime: ToolRuntime, config: RunnableConfig):
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async def edit_furniture(prompt: str, runtime: ToolRuntime, config: RunnableConfig):
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"""
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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。
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功能说明：
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- 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。
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- 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。
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- 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。
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参数说明：
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- 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）。
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- 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）。
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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, make the overall lighting warmer and more luxurious."
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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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- 家具设计迭代
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- 室内设计方案修改
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- 风格转换（现代/北欧/工业风等）
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- 材质/颜色调整
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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。
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"""
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logger.info(f"\n[系统日志] 正在调用 edit_furniture ...")
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thread_id = runtime.config.get("configurable").get("thread_id")
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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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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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input_path = []
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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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else:
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current_image_path = None
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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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extract_result = check_and_extract_minio_image(url=image_url)
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if extract_result['state']:
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current_image_path = extract_result['data']
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if current_image_path:
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if len(user_input_image_paths) or current_image_path:
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for path in user_input_image_paths:
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input_path.append(path)
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if user_quote_image_path:
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input_path.append(user_quote_image_path)
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if not len(user_input_image_paths) and not user_quote_image_path:
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input_path = [current_image_path]
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object_name = f"furniture/sketches/{uuid.uuid4()}.png"
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bucket_name = "fida-public-bucket" # 替换为你的 bucket 名称
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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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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_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=thread_id, object_path=image_url, metadata={"prompt": prompt, "generated_at": str(datetime.now())})
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return f"Image has been generated: https://minio-api.aida.com.hk/{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 image_url
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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 extract_result['message']
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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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# def create_generate_furniture_tool(workspace_dir):
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# @tool
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# async def generate_furniture(prompt: str) -> str:
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# """
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# 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。
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# """
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# print(f"\n[系统日志] 正在调用 Nano Banana (Gemini Image Gen) ...")
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#
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# try:
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# response = client.models.generate_content(
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# model="gemini-2.5-flash-image",
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# contents=(f"Generate a professional furniture design sketch: {prompt}"),
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# config=GenerateContentConfig(
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# response_modalities=[Modality.TEXT, Modality.IMAGE],
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# ),
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# )
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#
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# image_bytes = None
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# for part in response.candidates[0].content.parts:
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# if part.inline_data:
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# image_bytes = part.inline_data.data
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# break
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#
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# if not image_bytes:
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# return "未能生成图像数据。"
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# # 1. 定义OSS存储路径和本地保存路径
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# object_name = f"furniture/sketches/{uuid.uuid4()}.png"
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# bucket = "fida-test" # 替换为你的 bucket 名称
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# filename = os.path.join(workspace_dir, f"{bucket}/{object_name}")
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#
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# # 2. 创建本地目录（确保目录存在）
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# local_dir = os.path.dirname(filename)
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# if not os.path.exists(local_dir):
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# os.makedirs(local_dir, exist_ok=True)
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#
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# # 3. 保存图片到本地文件（新增核心逻辑）
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# try:
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# with open(filename, "wb") as f:
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# f.write(image_bytes)
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# print(f"[系统日志] 图片已保存到本地：{filename}")
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# except Exception as save_e:
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# logger.warning(f"保存图片到本地失败：{save_e}")
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# # 本地保存失败不中断上传流程，仅记录日志
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#
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# # 4. 上传图片到OSS（原有逻辑）
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# upload_res = oss_upload_image(
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# oss_client=minio_client,
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# bucket=bucket,
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# object_name=object_name,
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# image_bytes=image_bytes
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# )
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#
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# if upload_res:
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# image_url = f"{bucket}/{object_name}"
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# return image_url
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# else:
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# return f"图片生成成功（本地路径：{filename}），但上传至存储服务器失败。"
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#
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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 "绘图流程异常"
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#
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# return generate_furniture
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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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