Merge remote-tracking branch 'origin/develop' into develop

app/api/api_extraction_project_info.py

@@ -0,0 +1,39 @@
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from app.schemas.project_info_extraction import ProjectInfoExtractionModel
+from app.schemas.response_template import ResponseModel
+from app.service.project_info_extraction.service import ProjectInfoExtraction
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@router.post("/extraction_project_info")
+def extraction_project_info(request_data: ProjectInfoExtractionModel):
+    """
+        通过prompt 提取project_name,role ,gender ,style。
+        创建一个具有以下参数的请求体:
+        - **prompt**:
+
+        示例参数：
+        {
+          "prompt": "海边派对主题的系列设计",
+          "image_list": [
+            "https://www.minio-api.aida.com.hk/test/test123.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=vXKFLSJkYeEq2DrSZvkB%2F20250519%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250519T050808Z&X-Amz-Expires=7200&X-Amz-SignedHeaders=host&X-Amz-Signature=296ff07cc4692d0a26ddffac582064f036494af343389fe60193dc2c5dc883ff"
+          ],
+          "file_list": [
+            ""
+          ]
+        }
+    """
+    try:
+        logger.info(f"extraction_project_info request item is : @@@@@@:{request_data}")
+        service = ProjectInfoExtraction(request_data)
+        data = service.get_result()
+        logger.info(f"extraction_project_info response @@@@@@:{data}")
+    except Exception as e:
+        logger.warning(f"extraction_project_info Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    return ResponseModel(data=data)

app/api/api_route.py

@@ -5,16 +5,17 @@ from app.api import api_attribute_retrieve, api_query_image
 from app.api import api_brand_dna
 from app.api import api_brighten
 from app.api import api_chat_robot
+from app.api import api_clothing_seg
 from app.api import api_design
 from app.api import api_design_pre_processing
+from app.api import api_extraction_project_info
 from app.api import api_generate_image
 from app.api import api_image2sketch
 from app.api import api_mannequins_edit
 from app.api import api_pose_transform
 from app.api import api_prompt_generation
-from app.api import api_clothing_seg
-from app.api import api_super_resolution
 from app.api import api_recommendation
+from app.api import api_super_resolution
 from app.api import api_test
 
 router = APIRouter()
@@ -36,3 +37,4 @@ router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'],
 router.include_router(api_agent_generate_image.router, tags=['api_agent_generate_image'], prefix="/api")
 router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")
 router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api")
+router.include_router(api_extraction_project_info.router, tags=['api_extraction_project_info'], prefix="/api")

app/core/config.py

@@ -43,7 +43,7 @@ JAVA_STREAM_API_URL = os.getenv("JAVA_STREAM_API_URL", "https://api.aida.com.hk/
 settings = Settings()
 
 # minio 配置
-MINIO_URL = "www.minio.aida.com.hk:12024"
+MINIO_URL = "www.minio-api.aida.com.hk"
 MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB'
 MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR'
 MINIO_SECURE = True

app/schemas/generate_image.py

@@ -1,3 +1,5 @@
+from typing import List
+
 from pydantic import BaseModel
 
 
@@ -43,19 +45,33 @@ class GenerateRelightImageModel(BaseModel):
 """
 
 
-class BatchGenerateProductImageModel(BaseModel):
+# product任务子项
+class ProductItemModel(BaseModel):
     tasks_id: str
+    image_strength: float
     prompt: str
     image_url: str
-    image_strength: float
     product_type: str
-    batch_size: int
 
 
-class BatchGenerateRelightImageModel(BaseModel):
+# product批处理 集合
+class BatchGenerateProductImageModel(BaseModel):
+    batch_tasks_id: str
+    user_id: str
+    batch_data_list: List[ProductItemModel]
+
+
+# relight任务子项
+class RelightItemModel(BaseModel):
     tasks_id: str
     prompt: str
     image_url: str
     direction: str
     product_type: str
-    batch_size: int
+
+
+# relight批处理集合
+class BatchGenerateRelightImageModel(BaseModel):
+    batch_tasks_id: str
+    user_id: str
+    batch_data_list: List[RelightItemModel]

app/schemas/project_info_extraction.py

@@ -0,0 +1,7 @@
+from pydantic import BaseModel
+
+
+class ProjectInfoExtractionModel(BaseModel):
+    prompt: str
+    image_list: list
+    file_list: list

app/service/generate_batch_image/service.py

@@ -6,15 +6,15 @@ from app.service.generate_batch_image.service_batch_pose_transform import batch_
 async def start_product_batch_generate(data):
     generate_clothes_task = batch_generate_product.delay(data.dict())
     print(generate_clothes_task)
-    product_publish_status(data.tasks_id, f"0/{data.batch_size}", "")
-    return {"task_id": data.tasks_id, "state": generate_clothes_task.state}
+    product_publish_status(data.batch_tasks_id, f"0/{len(data.batch_data_list)}", "")
+    return {"task_id": data.batch_tasks_id, "state": generate_clothes_task.state}
 
 
 async def start_relight_batch_generate(data):
     generate_clothes_task = batch_generate_relight.delay(data.dict())
     print(generate_clothes_task)
-    relight_publish_status(data.tasks_id, f"0/{data.batch_size}", "")
-    return {"task_id": data.tasks_id, "state": generate_clothes_task.state}
+    relight_publish_status(data.batch_tasks_id, f"0/{len(data.batch_data_list)}", "")
+    return {"task_id": data.batch_tasks_id, "state": generate_clothes_task.state}
 
 
 async def start_pose_transform_batch_generate(data):

app/service/generate_batch_image/service_batch_generate_product_image.py

@@ -19,7 +19,7 @@ from celery import Celery
 from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import *
-from app.schemas.generate_image import BatchGenerateProductImageModel
+from app.schemas.generate_image import BatchGenerateProductImageModel, ProductItemModel
 from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
 from app.service.utils.oss_client import oss_get_image
 
@@ -35,24 +35,26 @@ category = "product_image"
 
 @celery_app.task
 def batch_generate_product(batch_request_data):
+    batch_size = len(batch_request_data['batch_data_list'])
     logger.info(f"batch_generate_product batch_request_data:{json.dumps(batch_request_data, indent=4)}")
-    tasks_id = batch_request_data['tasks_id']
-    user_id = tasks_id.rsplit('-', 1)[1]
-    batch_size = batch_request_data['batch_size']
-    image = pre_processing_image(batch_request_data['image_url'])
+    batch_tasks_id = batch_request_data['batch_tasks_id']
+    user_id = batch_request_data['user_id']
+    result_data_list = []
+
+    for i, data in enumerate(batch_request_data['batch_data_list']):
+        tasks_id = data['tasks_id']
+        image = pre_processing_image(data['image_url'])
         image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
         images = [image.astype(np.uint8)] * 1
-
-    prompts = [batch_request_data['prompt']] * 1
-
-    if batch_request_data['product_type'] == "single":
+        prompts = [data['prompt']] * 1
+        if data['product_type'] == "single":
             text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
             image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-        image_strength_obj = np.array(batch_request_data['image_strength'], dtype=np.float32).reshape((-1, 1))
+            image_strength_obj = np.array(data['image_strength'], dtype=np.float32).reshape((-1, 1))
         else:
             text_obj = np.array(prompts, dtype="object").reshape((1))
             image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
-        image_strength_obj = np.array(batch_request_data['image_strength'], dtype=np.float32).reshape((1))
+            image_strength_obj = np.array(data['image_strength'], dtype=np.float32).reshape((1))
         input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
         input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
         input_image_strength = grpcclient.InferInput("image_strength", image_strength_obj.shape, np_to_triton_dtype(image_strength_obj.dtype))
@@ -63,10 +65,8 @@ def batch_generate_product(batch_request_data):
 
         inputs = [input_text, input_image, input_image_strength]
 
-    image_url_list = []
-    for i in range(batch_size):
         try:
-            if batch_request_data['product_type'] == "single":
+            if data['product_type'] == "single":
                 result = grpc_client.infer(model_name=GPI_MODEL_NAME_SINGLE, inputs=inputs, priority=100)
                 image = result.as_numpy("generated_cnet_image")
             else:
@@ -77,7 +77,7 @@ def batch_generate_product(batch_request_data):
             if 'mask_list' in str(e):
                 e_text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
                 e_image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-                e_image_strength_obj = np.array(batch_request_data['image_strength'], dtype=np.float32).reshape((-1, 1))
+                e_image_strength_obj = np.array(data['image_strength'], dtype=np.float32).reshape((-1, 1))
 
                 e_input_text = grpcclient.InferInput("prompt", e_text_obj.shape, np_to_triton_dtype(e_text_obj.dtype))
                 e_input_image = grpcclient.InferInput("input_image", e_image_obj.shape, "UINT8")
@@ -96,18 +96,29 @@ def batch_generate_product(batch_request_data):
 
         if isinstance(image_result, Image.Image):
             image_url = upload_SDXL_image(image_result, user_id=user_id, category=f"{category}", file_name=f"{tasks_id}-batch-{i}.png")
-            image_url_list.append(image_url)
+            data['product_img'] = image_url
+            result_data_list.append(data)
         else:
             image_url = image_result
-        if DEBUG is False:
-            if i + 1 < batch_size:
-                publish_status(tasks_id, f"{i + 1}/{batch_size}", image_url)
-                logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
+            data['product_img'] = image_url
+            result_data_list.append(data)
+
+        # 发送每条结果
+        if DEBUG:
+            logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+            print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
         else:
-                publish_status(tasks_id, f"OK", image_url_list)
-                logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
+            publish_status(tasks_id, f"{i + 1}/{batch_size}", data)
+            logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+
+    # 任务完成，发送所有数据结果
+    if DEBUG:
+        print(result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+        print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+    else:
+        publish_status(batch_tasks_id, f"OK", result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
 
 
 def pre_processing_image(image_url):
@@ -180,12 +191,52 @@ def publish_status(task_id, progress, result):
 
 
 if __name__ == '__main__':
+    # rd = BatchGenerateProductImageModel(
+    #     tasks_id="123-15-51-89",
+    #     image_strength=0.7,
+    #     prompt=" The best quality, masterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
+    #     image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+    #     product_type="overall",
+    #     batch_size=20
+    # )
+    # batch_generate_product(rd.dict())
+    # rd = {
+    #     "user_id": "89",
+    #     "batch_data_list": [
+    #         {
+    #             "tasks_id": "A-123-15-51-89",
+    #             "image_strength": 0.7,
+    #             "prompt": " The best quality, ma123sterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
+    #             "image_url": "aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+    #             "product_type": "overall",
+    #         },
+    #         {
+    #             "tasks_id": "B-123-15-51-89",
+    #             "image_strength": 0.7,
+    #             "prompt": " The best quality, masterpiece, real image.Outwear123,high quality clothing details,8K realistic,HDR",
+    #             "image_url": "aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+    #             "product_type": "overall",
+    #         }
+    #     ]
+    # }
     rd = BatchGenerateProductImageModel(
-        tasks_id="123-15-51-89",
+        batch_tasks_id="abcd",
+        user_id="89",
+        batch_data_list=[
+            ProductItemModel(
+                tasks_id="123-5464",
                 image_strength=0.7,
-        prompt=" The best quality, masterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
-        image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
                 product_type="overall",
-        batch_size=20
+                image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+                prompt="123"
+            ),
+            ProductItemModel(
+                tasks_id="123-5464123",
+                image_strength=0.7,
+                product_type="overall",
+                image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+                prompt="123"
+            )
+        ]
     )
     batch_generate_product(rd.dict())

app/service/generate_batch_image/service_batch_generate_relight_image.py

@@ -18,7 +18,7 @@ from celery import Celery
 from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import *
-from app.schemas.generate_image import BatchGenerateRelightImageModel
+from app.schemas.generate_image import BatchGenerateRelightImageModel, RelightItemModel
 from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
 from app.service.utils.oss_client import oss_get_image
 
@@ -34,17 +34,22 @@ category = "relight_image"
 
 @celery_app.task
 def batch_generate_relight(batch_request_data):
+    batch_size = len(batch_request_data['batch_data_list'])
     logger.info(f"batch_generate_relight batch_request_data: {json.dumps(batch_request_data, indent=4)}")
+    batch_tasks_id = batch_request_data['batch_tasks_id']
+    user_id = batch_request_data['user_id']
+    result_data_list = []
     negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
-    direction = batch_request_data['direction']
     seed = "1"
-    prompt = batch_request_data['prompt']
-    product_type = batch_request_data['product_type']
-    image_url = batch_request_data['image_url']
+
+    for i, data in enumerate(batch_request_data['batch_data_list']):
+        direction = data['direction']
+
+        prompt = data['prompt']
+        product_type = data['product_type']
+        image_url = data['image_url']
         image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url.split('/', 1)[1], data_type="cv2")
-    tasks_id = batch_request_data['tasks_id']
-    user_id = tasks_id.rsplit('-', 1)[1]
-    batch_size = batch_request_data['batch_size']
+        tasks_id = data['tasks_id']
 
         prompts = [prompt] * 1
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
@@ -79,10 +84,8 @@ def batch_generate_relight(batch_request_data):
         input_direction.set_data_from_numpy(direction_obj)
 
         inputs = [input_text, input_natext, input_image, input_seed, input_direction]
-    image_url_list = []
-    for i in range(batch_size):
         try:
-            if batch_request_data['product_type'] == "single":
+            if data['product_type'] == "single":
                 result = grpc_client.infer(model_name=GRI_MODEL_NAME_SINGLE, inputs=inputs, priority=100)
                 image = result.as_numpy("generated_relight_image")
             else:
@@ -121,18 +124,29 @@ def batch_generate_relight(batch_request_data):
                 logger.error(e)
         if isinstance(image_result, Image.Image):
             image_url = upload_SDXL_image(image_result, user_id=user_id, category=f"{category}", file_name=f"{tasks_id}-batch-{i}.png")
-            image_url_list.append(image_url)
+            data['relight_img'] = image_url
+
+            result_data_list.append(data)
         else:
             image_url = image_result
-        if DEBUG is False:
-            if i + 1 < batch_size:
-                publish_status(tasks_id, f"{i + 1}/{batch_size}", image_url)
-                logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
+            data['relight_img'] = image_url
+            result_data_list.append(data)
+
+        # 发送每条结果
+        if DEBUG:
+            logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+            print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
         else:
-                publish_status(tasks_id, f"OK", image_url_list)
-                logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
+            publish_status(tasks_id, f"{i + 1}/{batch_size}", data)
+            logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+    # 任务完成，发送所有数据结果
+    if DEBUG:
+        print(result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+        print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+    else:
+        publish_status(batch_tasks_id, f"OK", result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
 
 
 def publish_status(task_id, progress, result):
@@ -151,12 +165,44 @@ def publish_status(task_id, progress, result):
 
 if __name__ == '__main__':
     rd = BatchGenerateRelightImageModel(
-        tasks_id="123-89",
-        # prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
-        prompt="Colorful black",
-        image_url='aida-users/89/clothing_seg/283c5c82-1a92-11f0-b72a-0242ac150002.png',
-        direction="Right Light",
+        batch_tasks_id="abcd",
+        user_id="89",
+        batch_data_list=[
+            RelightItemModel(
+                tasks_id="123-5464",
                 product_type="overall",
-        batch_size=10
+                image_url="aida-users/89/product_image/02894523-19b5-46eb-a9c6-2f512f5fec84-0-89.png",
+                prompt="Colorful black",
+                direction="Right Light",
+            ),
+            RelightItemModel(
+                tasks_id="123-5464123",
+                product_type="overall",
+                image_url="aida-users/89/product_image/02894523-19b5-46eb-a9c6-2f512f5fec84-0-89.png",
+                direction="Right Light",
+                prompt="Colorful black",
+            )
+        ]
     )
     batch_generate_relight(rd.dict())
+    # X = {
+    #     "batch_tasks_id": "abcd",
+    #     "user_id": "89",
+    #     "batch_data_list": [
+    #         {
+    #             "tasks_id": "123-5464",
+    #             "product_type": "overall",
+    #             "image_url": "aida-users/89/product_image/02894523-19b5-46eb-a9c6-2f512f5fec84-0-89.png",
+    #             "prompt": "Colorful black",
+    #             "direction": "Right Light",
+    #         },
+    #         {
+    #             "tasks_id": "123-5464",
+    #             "product_type": "overall",
+    #             "image_url": "aida-users/89/product_image/02894523-19b5-46eb-a9c6-2f512f5fec84-0-89.png",
+    #             "prompt": "Colorful black",
+    #             "direction": "Right Light",
+    #         }
+    #
+    #     ]
+    # }

app/service/generate_image/service_pose_transform.py

@@ -29,9 +29,6 @@ logger = logging.getLogger()
 
 class PoseTransformService:
     def __init__(self, request_data):
-        if DEBUG is False:
-            self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
-            self.channel = self.connection.channel()
         self.grpc_client = grpcclient.InferenceServerClient(url=PT_MODEL_URL)
         self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
         self.category = "pose_transform"
@@ -40,7 +37,8 @@ class PoseTransformService:
         self.image = pre_processing_image(request_data.image_url)
         self.tasks_id = request_data.tasks_id
         self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
-        self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '', 'video_url': '', 'image_url': ''}
+        self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '',
+                                    'video_url': '', 'image_url': ''}
 
         self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
         self.redis_client.expire(self.tasks_id, 600)
@@ -55,16 +53,20 @@ class PoseTransformService:
 
             # 第一帧图像
             first_image = Image.fromarray(result_data[0])
-            first_image_url = upload_first_image(first_image, user_id=self.user_id, category=f"{self.category}_first_img", file_name=f"{self.tasks_id}.png")
+            first_image_url = upload_first_image(first_image, user_id=self.user_id,
+                                                 category=f"{self.category}_first_img",
+                                                 file_name=f"{self.tasks_id}.png")
 
             # 上传GIF
             gif_buffer = BytesIO()
             imageio.mimsave(gif_buffer, result_data, format='GIF', fps=5)
             gif_buffer.seek(0)
-            gif_url = upload_gif(gif_buffer=gif_buffer, user_id=self.user_id, category=f"{self.category}_gif", file_name=f"{self.tasks_id}.gif")
+            gif_url = upload_gif(gif_buffer=gif_buffer, user_id=self.user_id, category=f"{self.category}_gif",
+                                 file_name=f"{self.tasks_id}.gif")
 
             # 上传video
-            video_url = upload_video(frames=result_data, user_id=self.user_id, category=f"{self.category}_video", file_name=f"{self.tasks_id}.mp4")
+            video_url = upload_video(frames=result_data, user_id=self.user_id, category=f"{self.category}_video",
+                                     file_name=f"{self.tasks_id}.mp4")
 
             self.pose_transform_data['status'] = "SUCCESS"
             self.pose_transform_data['message'] = "success"
@@ -82,7 +84,8 @@ class PoseTransformService:
         try:
             pose_num = [self.pose_num] * 1
             pose_num_obj = np.array(pose_num, dtype="object").reshape((-1, 1))
-            input_pose_num = grpcclient.InferInput("pose_num", pose_num_obj.shape, np_to_triton_dtype(pose_num_obj.dtype))
+            input_pose_num = grpcclient.InferInput("pose_num", pose_num_obj.shape,
+                                                   np_to_triton_dtype(pose_num_obj.dtype))
             input_pose_num.set_data_from_numpy(pose_num_obj)
 
             image_files = [self.image.astype(np.uint8)] * 1
@@ -90,7 +93,8 @@ class PoseTransformService:
             input_image_files = grpcclient.InferInput("image_file", image_files_obj.shape, "UINT8")
             input_image_files.set_data_from_numpy(image_files_obj)
 
-            ctx = self.grpc_client.async_infer(model_name="animatex_1", inputs=[input_pose_num, input_image_files], callback=self.callback, client_timeout=60000)
+            ctx = self.grpc_client.async_infer(model_name="animatex_1", inputs=[input_pose_num, input_image_files],
+                                               callback=self.callback, client_timeout=60000)
             time_out = 60000
             while time_out > 0:
                 pose_transform_data, _ = self.read_tasks_status()
@@ -100,7 +104,7 @@ class PoseTransformService:
                 elif pose_transform_data['status'] == "SUCCESS":
                     break
                 time_out -= 1
-                time.sleep(0.1)
+                time.sleep(1)
             pose_transform_data, _ = self.read_tasks_status()
             return pose_transform_data
         except Exception as e:
@@ -111,9 +115,22 @@ class PoseTransformService:
         finally:
             dict_pose_transform_data, str_pose_transform_data = self.read_tasks_status()
             if DEBUG is False:
-                self.channel.basic_publish(exchange='', routing_key=PS_RABBITMQ_QUEUES, body=str_pose_transform_data)
-                self.connection.close()
-            logger.info(f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_pose_transform_data, indent=4)}")
+                publish_status(str_pose_transform_data)
+            logger.info(
+                f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_pose_transform_data, indent=4)}")
+
+
+def publish_status(message):
+    connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+    channel = connection.channel()
+    channel.queue_declare(queue=PS_RABBITMQ_QUEUES, durable=True)
+    channel.basic_publish(exchange='',
+                          routing_key=PS_RABBITMQ_QUEUES,
+                          body=json.dumps(message),
+                          properties=pika.BasicProperties(
+                              delivery_mode=2,
+                          ))
+    connection.close()
 
 
 def infer_cancel(tasks_id):
@@ -125,7 +142,8 @@ def infer_cancel(tasks_id):
 
 
 def pre_processing_image(image_url):
-    image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
+    image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:],
+                          data_type="PIL")
     # 目标图片的尺寸
     target_width = 512
     target_height = 768

app/service/project_info_extraction/service.py

@@ -0,0 +1,61 @@
+from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+from langchain_community.chat_models import ChatTongyi
+from langchain_core.prompts import PromptTemplate
+
+from app.schemas.project_info_extraction import ProjectInfoExtractionModel
+
+style = ['NEW_CHINESE', 'COUNTRY_STYLE', 'FUTURISM', 'MINIMALISM', 'LOLITA', 'Y2K', 'BUSINESS', 'MERLAD',
+         'OUTDOOR_FUNCTIONAL', 'ROCK', 'DOPAMINE', 'GOTHIC', 'POST_APOCALYPTIC', 'ROMANTIC', 'WABI_SABI']
+position = ['Overall', 'Tops', 'Bottoms', 'Outwear', 'Blouse', 'Dress', 'Trousers', 'Skirt']
+gender = ['Female', 'Male']
+age_group = ['Adult', 'Child']
+process = ['SERIES_DESIGN', 'SINGLE_DESIGN']
+
+
+class ProjectInfoExtraction:
+    def __init__(self, request_data):
+        # llm generate brand info init
+        self.model = ChatTongyi(model="qwen2.5-14b-instruct", api_key="sk-7658298c6b99443c98184a5e634fe6ab")
+
+        self.response_schemas = [
+            ResponseSchema(name="project_name", description="项目的名称."),
+            ResponseSchema(name="process", description="项目的类型，单品还是系列."),
+            ResponseSchema(name="ageGroup", description="项目设计服装的受众对象."),
+            ResponseSchema(name="gender", description="项目设计服装的受众性别."),
+            ResponseSchema(name="position", description="项目单品设计的部位."),
+            ResponseSchema(name="style", description="项目的设计风格.")
+        ]
+        self.output_parser = StructuredOutputParser.from_response_schemas(self.response_schemas)
+        self.format_instructions = self.output_parser.get_format_instructions()
+        self.prompt = PromptTemplate(
+            template="你是一个时装品牌的设计师助理。根据用户输入提取出"
+                     "[project_name] : 项目的名称,"
+                     f"[process] : 项目的类型，从{process}选择."
+                     f"[ageGroup] : 服装的受众，从{age_group}选择."
+                     f"[gender] : 服装的适用性别，从{gender}选择"
+                     f"[position] : single_design的部位，如果[process]是SINGLE_DESIGN,从{position}中选择，如果[process]是SERIES_DESIGN，这项为空"
+                     f"[style] : 设计的风格，从{style}中选择"
+                     ".\n{format_instructions}\n{question}",
+            input_variables=["question"],
+            partial_variables={"format_instructions": self.format_instructions}
+        )
+        self._input = self.prompt.format_prompt(question=request_data.prompt)
+
+        self.result_data = {}
+
+    def get_result(self):
+        self.llm_extraction_project_info()
+        return self.result_data
+
+    def llm_extraction_project_info(self):
+        output = self.model(self._input.to_messages())
+        project_info = self.output_parser.parse(output.content)
+        self.result_data = project_info
+
+
+if __name__ == '__main__':
+    request_data = ProjectInfoExtractionModel(
+        prompt="海边派对主题的衬衫设计"
+    )
+    service = ProjectInfoExtraction(request_data)
+    print(service.get_result())