aida agent (基础版)搭建完成
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import httpx
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async def generate_image(
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bucket_name="fida-public-bucket",
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object_name=f"furniture/sketches/123456.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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):
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request_data = {
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"input_image_paths": [],
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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://20.1.1.33:14202/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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import asyncio
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import concurrent.futures
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import random
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import numpy as np
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import tritonclient.grpc as grpcclient
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from langchain.tools import tool
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from PIL import Image
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from pydantic import BaseModel, Field
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from tritonclient.utils import np_to_triton_dtype
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from uuid_utils import uuid7
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from app.core.config import settings
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from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
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# 模型配置
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GSL_MODEL_URL = f"{settings.B_4_X_4090_SERVICE_HOST}:10041"
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GSL_MODEL_NAME = "stable_diffusion_xl_transparent"
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# 线程池用于执行同步推理
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executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
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def _generate_logo_sync(prompt: str) -> Image.Image:
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"""同步生成 Logo 的内部函数"""
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seed = random.randint(0, 2**32 - 1)
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grpc_client = grpcclient.InferenceServerClient(url=GSL_MODEL_URL)
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# 准备输入
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prompts = [prompt]
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text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
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input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
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input_text.set_data_from_numpy(text_obj)
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negative_prompts = "bad, ugly"
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text_obj_neg = np.array(negative_prompts, dtype="object").reshape((-1, 1))
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input_text_neg = grpcclient.InferInput("negative_prompt", text_obj_neg.shape, np_to_triton_dtype(text_obj_neg.dtype))
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input_text_neg.set_data_from_numpy(text_obj_neg)
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seed_input = np.array(seed, dtype="object").reshape((-1, 1))
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input_seed = grpcclient.InferInput("seed", seed_input.shape, np_to_triton_dtype(seed_input.dtype))
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input_seed.set_data_from_numpy(seed_input)
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inputs = [input_text, input_text_neg, input_seed]
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# 同步推理
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result = grpc_client.infer(model_name=GSL_MODEL_NAME, inputs=inputs)
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image = result.as_numpy("generated_image")
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return Image.fromarray(np.squeeze(image.astype(np.uint8)))
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async def generate_logo(prompt: str) -> Image.Image:
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"""异步生成透明背景的 Logo 图片"""
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(executor, _generate_logo_sync, prompt)
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class GenerateLogoToolInput(BaseModel):
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"""Input schema for the Generate Logo Tool."""
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prompt: str = Field(description="Simple keyword for logo generation, e.g., 'cat', 'flower', 'dog'")
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user_id: str = Field(description="User ID for image storage", default="agent")
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@tool(args_schema=GenerateLogoToolInput)
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async def generate_logo_tool(prompt: str, user_id: str = "agent") -> str:
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"""Generate a transparent background logo image based on a simple keyword."""
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image = await generate_logo(prompt=prompt)
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# 上传到 minio（使用线程池避免阻塞事件循环）
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file_name = f"{uuid7()}.png"
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loop = asyncio.get_event_loop()
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image_url = await loop.run_in_executor(executor, upload_SDXL_image, image, user_id, "logo", file_name)
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return image_url
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if __name__ == "__main__":
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result = asyncio.run(generate_logo_tool.ainvoke({"prompt": "golden retriever"}))
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print(f"Logo saved to: {result}")
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import asyncio
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import concurrent.futures
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import io
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import logging
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import os
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import httpx
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from dotenv import load_dotenv
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from PIL import Image
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from uuid_utils import uuid7
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from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
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load_dotenv()
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logger = logging.getLogger()
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PEXELS_API_KEY = os.environ.get("PEXELS_API_KEY", "")
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PEXELS_BASE_URL = os.environ.get("PEXELS_BASE_URL", "")
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# 线程池用于执行同步上传
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executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
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async def search_photos(query: str, per_page: int = 4, user_id: str = "agent") -> list[dict]:
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"""从 Pexels 搜索图片并上传到 minio
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Args:
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query: 搜索关键词
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per_page: 返回图片数量 (1-80)
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user_id: 用户 ID
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Returns:
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图片信息列表，每项包含 image_url 和 minio_path
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"""
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# 搜索图片
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async with httpx.AsyncClient(timeout=30) as client:
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response = await client.get(
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f"{PEXELS_BASE_URL}/search",
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headers={"Authorization": PEXELS_API_KEY},
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params={
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"query": query,
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"per_page": per_page,
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"orientation": "square",
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"size": "medium",
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},
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)
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if response.status_code != 200:
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raise Exception(f"Pexels API error: {response.status_code} - {response.text}")
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data = response.json()
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photos = data.get("photos", [])
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# 下载并上传到 minio
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results = []
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for photo in photos:
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try:
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# 下载图片（使用 large 尺寸）
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image_url = photo["src"]["original"]
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async with httpx.AsyncClient(timeout=60) as dl_client:
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dl_response = await dl_client.get(image_url)
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image = Image.open(io.BytesIO(dl_response.content))
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# 上传到 minio（使用线程池避免阻塞事件循环）
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file_name = f"{uuid7()}.jpg"
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loop = asyncio.get_event_loop()
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minio_url = await loop.run_in_executor(executor, upload_SDXL_image, image, user_id, "explorer", file_name)
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results.append({"image_url": image_url, "minio_path": minio_url})
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logger.info(f"[Explorer] 上传成功: {minio_url}")
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except Exception as e:
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logger.error(f"[Explorer] 上传失败: {e}")
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return results
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@@ -0,0 +1,90 @@
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import asyncio
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import concurrent.futures
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import io
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import logging
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import os
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import httpx
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from PIL import Image
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from uuid_utils import uuid7
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from dotenv import load_dotenv
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from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
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load_dotenv()
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# Unsplash API 配置
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UNSPLASH_ACCESS_KEY = os.environ.get("UNSPLASH_ACCESS_KEY", "")
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UNSPLASH_BASE_URL = os.environ.get("UNSPLASH_BASE_URL", "")
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logger = logging.getLogger()
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# 线程池用于执行同步上传
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executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
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async def get_random_photos(query: str, count: int = 4, user_id: str = "agent") -> list[dict]:
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"""从 Unsplash 获取随机图片并上传到 minio
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Args:
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query: 搜索关键词
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count: 返回图片数量 (1-30)
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user_id: 用户 ID
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Returns:
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图片信息列表，每项包含 image_url 和 minio_path
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"""
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# 获取随机图片
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async with httpx.AsyncClient(timeout=30) as client:
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response = await client.get(
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f"{UNSPLASH_BASE_URL}/search/photos",
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headers={"Authorization": f"Client-ID {UNSPLASH_ACCESS_KEY}"},
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params={
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"query": query,
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"per_page": count,
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"page": 1,
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},
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)
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if response.status_code != 200:
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raise Exception(f"Unsplash API error: {response.status_code} - {response.text}")
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data = response.json()
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# /search/photos 返回 {"results": [...], "total": ...}
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photos = data.get("results", [])
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# 下载并上传到 minio
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results = []
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for photo in photos:
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try:
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# 下载图片
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image_url = photo["urls"]["raw"]
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async with httpx.AsyncClient(timeout=60) as dl_client:
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dl_response = await dl_client.get(image_url)
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image = Image.open(io.BytesIO(dl_response.content))
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# 上传到 minio（使用线程池避免阻塞事件循环）
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file_name = f"{uuid7()}.jpg"
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loop = asyncio.get_event_loop()
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minio_url = await loop.run_in_executor(executor, upload_SDXL_image, image, user_id, "explorer", file_name)
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results.append({"image_url": image_url, "minio_path": minio_url})
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logger.info(f"[Explorer] 上传成功: {minio_url}")
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except Exception as e:
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logger.error(f"[Explorer] 上传失败: {e}")
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return results
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if __name__ == "__main__":
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import asyncio
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async def test():
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"""测试 Unsplash 搜索"""
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query = "summer dress fresh natural style"
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print(f"搜索关键词: {query}")
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print("=" * 50)
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results = await get_random_photos(query, count=4, user_id="test")
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print(f"\n找到 {len(results)} 张图片:")
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for i, item in enumerate(results, 1):
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print(f" {i}. 原图: {item.get('image_url', '')}")
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print(f" Minio: {item.get('minio_path', '')}")
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asyncio.run(test())
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