feat: Sticker DB

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,31 @@
+# Ignore node_modules directory
+node_modules/
+
+# Ignore environment variables file
+.env
+
+# Ignore build output directory
+dist/
+
+# Ignore Python cache directories
+__pycache__/
+*.pyc
+
+# Ignore IDE specific files
+.vscode/
+.idea/
+
+# Ignore local development files
+*.local
+.cache
+
+# Ignore logs and databases
+*.log
+*.sqlite
+
+# Ignore macOS specific files
+.DS_Store
+
+# Ignore temp files
+*.tmp
+*.temp

Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.12-slim
+WORKDIR /app
+COPY requirements.txt .
+# RUN apt-get update && apt-get install -y gcc python3-dev
+RUN pip install -r requirements.txt
+# RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+COPY ./sticker.db /tmp/sticker.db
+
+ENV HF_HOME=/tmp/.cache
+
+
+CMD python main.py
+
+EXPOSE 7860

README.md

@@ -4,7 +4,23 @@ emoji: 📚
 colorFrom: red
 colorTo: blue
 sdk: docker
+sdk_version: 4.19.0
+app_port: 7860
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 环境变量配置
+
+- COZE_API_KEY：通过 Space Settings -> Secrets 添加
+
+## 功能特性
+
+✅ 基于语义的表情包搜索
+🖼️ 支持批量上传表情包
+🔍 多语言语义理解（支持中英文）
+⚡ FAISS 实时语义检索
+
+## 参考资料
+
+向量化模型：
+https://huggingface.co/spaces/mteb/leaderboard

app/__init__.py

@@ -0,0 +1 @@
+# 初始化app包

app/api.py

@@ -0,0 +1,52 @@
+from openai import OpenAI
+from app.config import DEEPSEEK_API_KEY
+
+def get_chat_completion(
+    user_prompt: str,
+    system_prompt: str = "You are a helpful AI assistant.",
+    model: str = "deepseek/deepseek-chat-v3-0324:free",
+    api_key: str = DEEPSEEK_API_KEY,
+) -> str:
+    """
+    Get chat completion from DeepSeek model via OpenRouter API.
+    
+    Args:
+        user_prompt: The user's input prompt
+        system_prompt: The system role prompt (default is generic assistant)
+        model: The model to use (default is DeepSeek-V2.5)
+        api_key: API key for OpenRouter (default from config)
+    
+    Returns:
+        The generated response from the model
+    """
+    client = OpenAI(
+        base_url="https://openrouter.ai/api/v1",
+        api_key=api_key,
+    )
+    
+    headers = {
+        "HTTP-Referer": 'https://huggingface.co/spaces/Nekoko/NekoAI-Lab',
+        "X-Title": "Meme-Search"
+    }
+
+    completion = client.chat.completions.create(
+        extra_headers=headers,
+        model=model,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+    )
+    
+    content =  completion.choices[0].message.content
+    print('>>>> LLM Response', content)
+    return content
+
+
+# Example usage:
+if __name__ == "__main__":
+    response = get_chat_completion(
+        user_prompt="What's the capital of France?",
+        system_prompt="You are a knowledgeable geography assistant."
+    )
+    print(response)

app/config.py

@@ -0,0 +1,33 @@
+import os
+
+# 应用配置
+DATASET_ID = "Nekoko/StickerSet"
+COZE_API_TOKEN = os.getenv('COZE_API_TOKEN')
+HUGGING_FACE_TOKEN = os.getenv('HUGGING_FACE_TOKEN')
+DEEPSEEK_API_KEY = os.getenv('DEEPSEEK_API_KEY')
+MILVUS_DB_FILE = os.getenv('MILVUS_DB_FILE', "./sticker.db")
+MILVUS_DB_URL = os.getenv('MILVUS_DB_URL', "./sticker.db")
+MILVUS_DB_TOKEN = os.getenv('MILVUS_DB_TOKEN', "")
+EMBEDDING_MODEL = 'shibing624/text2vec-base-chinese'
+PUBLIC_URL = os.getenv('PUBLIC_URL')
+
+STICKER_RERANKING_SYSTEM_PROMPT = """
+## 角色
+你是专业的表情包搜索助手，能找到最符合用户关键词的表情包，并为给定表情包列表中各表情包的关键词匹配度打分（0 - 1）并排序。
+## 回复示例:
+[{ "reason": "判断的理由", "sticker_id": "1", "score": 0.8,},
+{ "reason": "判断的理由", "sticker_id": "2", "score": 0.3,}]
+
+## 功能: 分析相关性
+1. 评估表情包列表中表情包描述与关键词的匹配度，给出0到1的评分。
+2. 给出判断的理由（中文）
+
+## 限制
+- 仅处理表情包搜索相关内容，不答无关问题，分清楚关键词和表情包列表。
+- 请只输出纯JSON格式，不要包含任何Markdown标记如```json或```。
+"""
+
+
+# 服务器配置
+HOST = "0.0.0.0"
+PORT = 7860

app/database.py

@@ -0,0 +1,155 @@
+from pymilvus import MilvusClient
+from sentence_transformers import SentenceTransformer
+from typing import List, Dict, Any, Optional, Union
+import logging
+
+from app.config import MILVUS_DB_URL, MILVUS_DB_TOKEN, EMBEDDING_MODEL, DATASET_ID
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class Database:
+    """数据库操作类，处理与Milvus的交互"""
+    
+    def __init__(self):
+        self.client = MilvusClient(
+            uri = MILVUS_DB_URL,
+            token= MILVUS_DB_TOKEN)
+        self.model = SentenceTransformer(EMBEDDING_MODEL, trust_remote_code=True)
+        print('初始化模型完成',self.model)
+        self.collection_name = "stickers"
+    
+    def init_collection(self) -> bool:
+        """初始化 Milvus 数据库"""
+        try:
+            print('初始化 Milvus 数据库', self.client.list_collections())
+            if not len(self.client.list_collections()) > 0:
+                self.client.create_collection(
+                    collection_name=self.collection_name,
+                    dimension=768,
+                    primary_field="id",
+                    auto_id=True
+                )
+                self.client.create_index(
+                    collection_name=self.collection_name,
+                    index_type="IVF_SQ8",
+                    metric_type="COSINE",
+                    params={"nlist": 128},
+                    index_params={}
+                )
+                logger.info(f"Collection initialized: {self.collection_name}")
+            print('初始化 Milvus 数据库成功', self.client.list_collections())
+            return True
+        except Exception as e:
+            logger.error(f"Collection initialization failed: {str(e)}")
+            return False
+    
+    def encode_text(self, text: str) -> List[float]:
+        """将文本编码为向量"""
+        return self.model.encode(text).tolist()
+    
+    def store_sticker(self, title: str, description: str, tags: Union[str, List[str]], file_path: str, image_hash: str = None) -> bool:
+        """存储贴纸数据到Milvus"""
+        try:
+            vector = self.encode_text(description)
+            
+            # 处理标签格式
+            if isinstance(tags, str):
+                tags = tags.split(",")
+            
+            logger.info(f"Storing to Milvus - title: {title}, description: {description}, file_path: {file_path}, tags: {tags}, image_hash: {image_hash}")
+            self.client.insert(
+                collection_name=self.collection_name,
+                data=[{
+                    "vector": vector,
+                    "title": title,
+                    "description": description,
+                    "tags": tags,
+                    "file_name": file_path,
+                    "image_hash": image_hash
+                }]
+            )
+            logger.info("Storing to Milvus Success ✅")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to store sticker: {str(e)}")
+            return False
+    
+    def search_stickers(self, description: str, limit: int = 2) -> List[Dict[str, Any]]:
+        """搜索贴纸"""
+        if not description:
+            return []
+        
+        try:
+            text_vector = self.encode_text(description)
+            logger.info(f"Searching Milvus - query: {description}, limit: {limit}")
+            
+            results = self.client.search(
+                collection_name=self.collection_name,
+                data=[text_vector],
+                limit=limit,
+                search_params={
+                    "metric_type": "COSINE",
+                },
+                output_fields=["title", "description", "tags", "file_name"],
+            )
+            
+            logger.info(f"Search Result: {results}")
+            return results[0]
+        except Exception as e:
+            logger.error(f"Search failed: {str(e)}")
+            return []
+    
+    def get_all_stickers(self, limit: int = 1000) -> List[Dict[str, Any]]:
+        """获取所有贴纸"""
+        try:
+            results = self.client.query(
+                collection_name=self.collection_name,
+                filter="",
+                limit=limit,
+                output_fields=["title", "description", "tags", "file_name", "image_hash"]
+            )
+            
+            logger.info(f"Query All Stickers - limit: {limit}, results count: {len(results)}")
+            return results
+        except Exception as e:
+            logger.error(f"Failed to get all stickers: {str(e)}")
+            return []
+    
+    def check_image_exists(self, image_hash: str) -> bool:
+        """检查文件名是否已存在"""
+        try:
+            results = self.client.query(
+                collection_name=self.collection_name,
+                filter=f"image_hash == '{image_hash}'",
+                limit=1,
+                output_fields=["file_name", "image_hash"]
+            )
+            
+            exists = len(results) > 0
+            logger.info(f"Check file exists - hash: {image_hash}, exists: {exists}, results: {results}")
+            return exists
+        except Exception as e:
+            logger.error(f"Failed to check file exists: {str(e)}")
+            return False
+
+    def delete_sticker(self, sticker_id: int) -> str:
+        """删除贴纸"""
+        try:
+            logger.info(f"Deleting sticker - id: {sticker_id}")
+            res = self.client.delete(
+                collection_name=self.collection_name,
+                ids=[sticker_id]
+            )
+            logger.info(f"Deleted sticker - id: {sticker_id}")
+            print(res)
+            return f"Sticker with ID {sticker_id} deleted successfully"
+        except Exception as e:
+            logger.error(f"Failed to delete sticker: {str(e)}")
+            return f"Failed to delete sticker: {str(e)}"
+# 初始化 Milvus 数据库
+
+# 创建数据库实例
+db = Database()

app/gradio_formatter.py

@@ -0,0 +1,68 @@
+from typing import List, Dict, Any
+from app.image_utils import format_image_url
+import logging
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+class GradioFormatter:
+    """贴纸格式化类，处理贴纸数据的格式化逻辑"""
+    
+    @staticmethod
+    def format_all_stickers(results: List[Dict[str, Any]]) -> List[List]:
+        """格式化所有贴纸，用于Gradio UI显示"""
+        formatted_results = []
+        
+        for record in results:
+            logger.info(f"格式化所有贴纸，用于Gradio UI显示: {record}")
+            image_url = format_image_url(record['file_name'])
+            formatted_results.append([
+                str(record['id']),
+                f"![Sticker]({image_url})",
+                record.get('title', ''),
+                record['description'],
+                ", ".join(record['tags']) if isinstance(record['tags'], list) else record['tags'],
+                record['file_name'],
+                record['image_hash'] if 'image_hash' in record else ''
+            ])
+        return formatted_results
+    
+
+    
+    @staticmethod
+    def format_search_results(results: List[Dict[str, Any]]) -> List[List]:
+        """格式化搜索结果，用于Gradio UI显示"""
+        formatted_results = []
+        logger.info(f"Formatting search results: {len(results)} items")
+        
+        for hit in results:
+            image_url = format_image_url(hit['entity']['file_name'])
+            formatted_results.append([
+                f"![Sticker]({image_url})",
+                round(hit['distance'], 4),
+                hit['entity'].get('description', ''),
+                hit['entity'].get('file_name', '')
+            ])
+
+        return formatted_results
+
+    @staticmethod
+    def format_ai_search_results(results: List[Dict[str, Any]]) -> List[List]:
+        """格式化 AI 搜索结果，用于Gradio UI显示"""
+        formatted_results = []
+        logger.info(f"Formatting AI search results: {len(results)} items")
+        
+        for hit in results:
+            image_url = format_image_url(hit['entity']['file_name'])
+            formatted_results.append([
+                f"![Sticker]({image_url})",
+                hit['entity'].get('score', ''),
+                hit['entity'].get('reason', ''),
+                hit['entity'].get('description', ''),
+                hit['entity'].get('file_name', '')
+            ])
+
+        return formatted_results
+# 创建格式化器实例
+gradio_formatter = GradioFormatter()

app/image_utils.py

@@ -0,0 +1,93 @@
+import tempfile
+import random
+import requests
+import json
+import logging
+from PIL import Image
+from huggingface_hub import HfApi
+from cozepy import Coze, TokenAuth
+import hashlib
+
+from app.config import DATASET_ID, COZE_API_TOKEN, HUGGING_FACE_TOKEN
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+# 初始化API客户端
+api = HfApi()
+coze = Coze(auth=TokenAuth(token=COZE_API_TOKEN), base_url="https://api.coze.cn")
+
+
+def calculate_image_hash(Image: Image.Image) -> str:
+    """
+    参数:
+        Image
+    返回:
+        str: 图片的MD5哈希字符串
+    """
+    return hashlib.md5(Image.tobytes()).hexdigest()
+
+
+
+def get_image_description(image_url: str) -> str:
+    """获取图片描述"""
+    logger.info(f"Get image description")
+    workflow = coze.workflows.runs.create(
+        workflow_id='7479742935953752091',
+        parameters={
+            "image_url": image_url
+        }
+    )
+    logger.info(f"Image description: {workflow.data}")
+    if (workflow.data):
+        description = json.loads(workflow.data)['output']
+        return description
+    else:
+        return ""
+
+def save_image_temp(image: Image.Image) -> str:
+    """保存图片到临时文件"""
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_img:
+        image.save(temp_img.name, "PNG")
+        return temp_img.name
+
+
+def upload_to_huggingface(temp_file_path: str) -> tuple:
+    """上传图片到 HuggingFace"""
+    image_filename = f"image_{random.randint(1000, 9999)}.png"
+    file_path = f"images/{image_filename}"
+    
+    logger.info(f"Uploading image to HuggingFace: {file_path}")
+    api.upload_file(
+        path_or_fileobj=temp_file_path,
+        path_in_repo=file_path,
+        repo_id=DATASET_ID,
+        token=HUGGING_FACE_TOKEN,
+        repo_type="dataset"
+    )
+    logger.info(f"Image uploaded successfully: {file_path}")
+    return file_path, image_filename
+
+
+def get_image_cdn_url(file_path: str) -> str:
+    """获取图片CDN URL"""
+    image_url = f"https://huggingface.co/datasets/{DATASET_ID}/resolve/main/{file_path}"
+    logger.info(f"Getting CDN URL for: {image_url}")
+    
+    response = requests.head(
+        image_url, 
+        allow_redirects=True,
+        timeout=10,  
+        headers={
+            'User-Agent': 'NekoAI',
+        }
+    )
+    image_cdn_url = response.url
+    logger.info(f"CDN URL: {image_cdn_url}")
+    return image_cdn_url
+
+
+def format_image_url(file_path: str) -> str:
+    """格式化图片URL，用于显示"""
+    return f"https://huggingface.co/datasets/{DATASET_ID}/resolve/main/{file_path}"

app/services.py

@@ -0,0 +1,165 @@
+import os
+import logging
+from typing import List, Dict, Any, Optional, Union
+from PIL import Image
+from app.api import get_chat_completion
+import json
+from app.config import (
+    STICKER_RERANKING_SYSTEM_PROMPT,
+    PUBLIC_URL
+)
+
+from app.database import db
+from app.image_utils import (
+    save_image_temp, 
+    upload_to_huggingface, 
+    get_image_cdn_url, 
+    get_image_description,
+    calculate_image_hash
+)
+from app.gradio_formatter import gradio_formatter
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class StickerService:
+    """贴纸服务类，处理贴纸的上传、搜索等业务逻辑"""
+    
+    @staticmethod
+    def upload_sticker(image_file_path: str, title: str, description: str, tags: str) -> str:
+        """上传贴纸"""
+        try:
+            # 打开图片
+            image = Image.open(image_file_path)
+
+            # 检查文件名是否已存在
+            image_hash = calculate_image_hash(image)
+            if db.check_image_exists(image_hash):
+                print(f"文件已存在", image_hash)
+                raise Exception('File_Exists')
+            
+            # 上传到 HuggingFace
+            file_path, image_filename = upload_to_huggingface(image_file_path)
+            # print('>>>> image_file_path', image_file_path)
+            # print('>>>> image_filename', image_filename)
+            # print('>>>> file_path', file_path)
+
+            # 如果没有描述，获取图片描述
+            if not description:
+                image_cdn_url = ''
+                if (PUBLIC_URL):
+                    image_cdn_url = f'{PUBLIC_URL}/gradio_api/file={image_file_path}'
+                else:
+                    image_cdn_url = get_image_cdn_url(file_path)
+                print('image_cdn_url',image_cdn_url)
+                
+                description = get_image_description(image_cdn_url)
+            
+            # 清理临时文件
+            # os.unlink(temp_file_path)
+            
+            # 存储到 Milvus
+            db.store_sticker(title, description, tags, file_path, image_hash)
+            
+            return f"Upload successful! {image_filename}"
+            
+        except Exception as e:
+            logger.error(f"Upload failed: {str(e)}")
+            return f"Upload failed: {str(e)}"
+    
+    @staticmethod
+    def search_stickers(description: str, limit: int = 2, reranking : bool = False) -> List[Dict[str, Any]]:
+        """搜索贴纸"""
+        if not description:
+            return []
+        
+        try:
+            results = db.search_stickers(description, limit)
+            if (reranking):
+                # 对搜索结果进行重排
+                results = StickerService.rerank_search_results(description, results, limit)
+            return results
+        except Exception as e:
+            logger.error(f"Search failed: {str(e)}")
+            return []
+    
+    @staticmethod
+    def get_all_stickers(limit: int = 1000) -> List[List]:
+        """获取所有贴纸"""
+        try:
+            results = db.get_all_stickers(limit)
+            return gradio_formatter.format_all_stickers(results)
+        except Exception as e:
+            logger.error(f"Failed to get all stickers: {str(e)}")
+            return []
+            
+    @staticmethod
+    def delete_sticker(sticker_id: str) -> str:
+        """删除贴纸"""
+        try:
+            # 首先查询贴纸是否存在
+            result = db.delete_sticker(sticker_id)
+            
+            return f"Sticker with ID {sticker_id} deleted successfully"
+        except Exception as e:
+            logger.error(f"Delete failed: {str(e)}")
+            return f"Delete failed: {str(e)}"
+    
+
+    @staticmethod
+    def rerank_search_results(query: str, sticker_list: List[Dict[str, Any]], limit: int = 5) -> List[Dict[str, Any]]:
+        ## 使用 LLM 模型重新排序搜索结果
+        try:
+            # 构建提示词
+            system_prompt = STICKER_RERANKING_SYSTEM_PROMPT
+            # 构建用户提示词，包含查询和表情包信息
+            _sticker_list = []
+            for hit in sticker_list:
+                _sticker_list.append({
+                    "id": hit["id"],
+                    "description": hit["entity"]["description"]
+                })
+            
+            user_prompt = f"请分析关键词 '{query}' 与以下表情包的相关性:\n{_sticker_list}"
+            
+            print(f">>> 使用 LLM 模型重新排序....", user_prompt, system_prompt)
+            # 调用 LLM 模型获取重排序结果
+            response = get_chat_completion(user_prompt, system_prompt)
+            
+            # 解析 LLM 返回的 JSON 结果
+            reranked_stickers = json.loads(response)
+            
+            # 验证返回结果格式
+            if not isinstance(reranked_stickers, list):
+                raise ValueError("Invalid response format")
+                
+            # 按分数排序
+            reranked_stickers.sort(key=lambda x: float(x.get("score", 0)), reverse=True)
+
+            print(f">>> LLM 排序结果", reranked_stickers)
+
+            
+            # 将重排序结果与原始结果对应
+            rerank_results = []
+            for sticker in reranked_stickers:
+                for hit in sticker_list:
+                    if str(hit["id"]) == str(sticker["sticker_id"]):
+                        hit["entity"]["score"] = sticker["score"]
+                        hit["entity"]["reason"] = sticker["reason"]
+                        rerank_results.append(hit)
+                        break
+            
+            print(f">>> rerank_results", rerank_results)
+            return rerank_results
+
+        except Exception as e:
+            logger.error(f"Reranking failed: {str(e)}")
+            return []
+
+
+
+
+# 创建服务实例
+sticker_service = StickerService()

app/ui.py

@@ -0,0 +1,131 @@
+import gradio as gr
+import logging
+from app.services import sticker_service
+from app.gradio_formatter import gradio_formatter
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s %(levelname)s %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+class StickerUI:
+    """Main class for the Sticker Search UI application."""
+    
+    def __init__(self):
+        self.demo = None
+        self._initialize_components()
+    
+    def _initialize_components(self):
+        """Initialize all UI components."""
+        # Search Tab
+        self.search_input = gr.Textbox(label="Search keywords")
+        self.limit_input = gr.Slider(1, 10, value=4, step=1, label="Max results")
+        self.search_button = gr.Button("Search")
+        self.deepseek_button = gr.Button("AI Search")
+        self.search_results = gr.Dataframe(
+            headers=["Preview", "Relevance", "Description", "Filename"],
+            datatype=["markdown", "number", "str", "str"],
+            row_count=("dynamic")
+        )
+
+        self.ai_search_results = gr.Dataframe(
+            headers=["Preview", "AI-Score", "AI-Reason", "Description", "Filename"],
+            datatype=["markdown", "number", "str", "str", "str"],
+            row_count=("dynamic")
+        )
+        
+        # Upload Tab
+        self.image_input = gr.Image(type="filepath")
+        self.title_input = gr.Textbox(label="Title")
+        self.tags_input = gr.Textbox(label="Tags (comma separated)")
+        self.desc_input = gr.Textbox(label="Description")
+        self.upload_button = gr.Button("Upload")
+        self.upload_output = gr.Textbox(label="Status")
+        
+        # All Stickers Tab
+        self.refresh_button = gr.Button("Refresh")
+        self.stickers_table = gr.Dataframe(
+            headers=["ID", "Preview", "Title", "Description", "Tags", "Filename", "Hash"],
+            datatype=["str", "markdown", "str", "str", "str", "str", "str"]
+        )
+    
+    def _setup_search_tab(self):
+        """Configure the search tab layout and functionality."""
+        with gr.Tab("Search"):
+            with gr.Row():
+                self.search_input.render()
+                self.limit_input.render()
+            
+            with gr.Row():
+                self.search_button.render()
+                self.deepseek_button.render()
+            
+            self.search_results.render()
+            self.ai_search_results.render()
+            
+            # Event handlers
+            self.search_button.click(
+                fn=lambda q, l: gradio_formatter.format_search_results(
+                    sticker_service.search_stickers(q, l)
+                ),
+                inputs=[self.search_input, self.limit_input],
+                outputs=self.search_results
+            )
+            
+            self.deepseek_button.click(
+                fn=lambda q, l: gradio_formatter.format_ai_search_results(
+                    sticker_service.search_stickers(q, l, reranking=True)
+                ),
+                inputs=[self.search_input, self.limit_input],
+                outputs=self.ai_search_results
+            )
+    
+    def _setup_upload_tab(self):
+        """Configure the upload tab layout and functionality."""
+        with gr.Tab("Upload"):
+            with gr.Row():
+                self.image_input.render()
+            
+            with gr.Row():
+                self.title_input.render()
+                self.tags_input.render()
+            
+            with gr.Row():
+                self.desc_input.render()
+                self.upload_button.render()
+            
+            self.upload_output.render()
+            
+            self.upload_button.click(
+                fn=sticker_service.upload_sticker,
+                inputs=[
+                    self.image_input,
+                    self.title_input,
+                    self.desc_input,
+                    self.tags_input
+                ],
+                outputs=self.upload_output
+            )
+    
+    def _setup_all_stickers_tab(self):
+        """Configure the all stickers tab layout and functionality."""
+        with gr.Tab("All Stickers"):
+            self.refresh_button.render()
+            self.stickers_table.render()
+            
+            self.refresh_button.click(
+                fn=sticker_service.get_all_stickers,
+                outputs=self.stickers_table
+            )
+    
+    def create_ui(self):
+        """Create and configure the complete UI."""
+        with gr.Blocks(title="Neko Sticker Search 🔍") as self.demo:
+            self._setup_search_tab()
+            self._setup_upload_tab()
+            self._setup_all_stickers_tab()
+        
+        return self.demo

embedding_test.py

@@ -0,0 +1,241 @@
+import os
+import logging
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any
+from sentence_transformers import SentenceTransformer
+from pymilvus import MilvusClient, DataType
+import time
+import gradio as gr
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s %(levelname)s %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+models = [
+    'shibing624/text2vec-base-chinese',
+    'BAAI/bge-small-zh',
+    'BAAI/bge-base-zh',
+    'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2',
+    'all-MiniLM-L6-v2',
+    'all-MiniLM-L12-v2',
+    'multi-qa-mpnet-base-dot-v1',
+    # 'bge-small-en-v1.5', 不兼容
+    'all-mpnet-base-v2',
+    'jinaai/jina-embeddings-v3',
+]
+
+
+searchers = {}
+
+
+class BaseEmbeddingModel(ABC):
+    @abstractmethod
+    def encode(self, text: str) -> List[float]:
+        pass
+
+    @property
+    @abstractmethod
+    def dimension(self) -> int:
+        pass
+
+    @property
+    @abstractmethod
+    def model_name(self) -> str:
+        pass
+
+class SentenceTransformerModel(BaseEmbeddingModel):
+    def __init__(self, model_name: str):
+        self.model = SentenceTransformer(model_name, trust_remote_code=True)
+        self._model_name = model_name
+
+    def encode(self, text: str) -> List[float]:
+        result = self.model.encode(text).tolist()
+        return result
+
+    @property
+    def dimension(self) -> int:
+        return self.model.get_sentence_embedding_dimension()
+
+    @property
+    def model_name(self) -> str:
+        return self._model_name
+
+class StickerSearcher:
+    def __init__(self, model: BaseEmbeddingModel):
+        self.model = model
+        self.client = MilvusClient(uri='./sticker.db')
+        self.collection_name = f'test_{model.model_name.replace("/", "_").replace("-", "_")}'
+
+    def init_collection(self) -> bool:
+        try:
+            self.client.drop_collection(collection_name=self.collection_name)
+            self.client.create_collection(
+                collection_name=self.collection_name,
+                dimension=self.model.dimension,
+                primary_field_name='id',
+                auto_id=True
+            )
+            self.client.create_index(
+                collection_name=self.collection_name,
+                index_type='IVF_SQ8',
+                metric_type='COSINE',
+                params={'nlist': 128},
+                index_params={}
+            )
+            self.client.load_collection(self.collection_name)
+            logger.info(f'Collection initialized: {self.collection_name}')
+            return True
+        except Exception as e:
+            logger.error(f'Collection init failed: {str(e)}')
+            return False
+
+    def store_vector(self, title: str, description: str, tags: List[str], file_path: str):
+        vector = self.model.encode(description)
+        data = [{
+            'vector': vector,
+            'title': title,
+            'description': description,
+            'tags': tags,
+            'file_name': file_path
+        }]
+        self.client.insert(self.collection_name, data)
+
+    def search(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        start_time = time.time()
+        query_vector = self.model.encode(query)
+        encode_time = time.time() - start_time
+        start_search_time = time.time()
+        results = self.client.search(
+            collection_name=self.collection_name,
+            data=[query_vector],
+            limit=limit,
+            output_fields=['title', 'description', 'tags', 'file_name']
+        )
+        search_time = time.time() - start_search_time
+        total_time = encode_time + search_time
+        logger.info(f'模型 {self.model.model_name} Encoding耗时: ${encode_time:.4f}，搜索耗时: {search_time:.4f} 秒, 总耗时: {total_time:.4f} 秒')
+        return results[0]
+
+def create_gradio_ui():
+
+    async def search_model(model_name: str, query: str):
+        try:
+            if model_name in searchers:
+                return searchers[model_name].search(query)
+            logger.error(f'Model not loaded: {model_name}')
+            return []
+        except Exception as e:
+            logger.error(f'Search failed: {model_name} | Error: {str(e)}')
+            return []
+
+    async def search_all_models(query):
+        if not query:
+            return []
+
+        print(f'>>>> Searching From Models {query}')
+        results = []
+        for model_name in models:
+            result = await search_model(model_name, query)
+            results.append(result)
+
+        formatted_results = []
+        max_results = max(len(r) for r in results)
+
+        for i in range(max_results):
+            row = [i + 1]
+            for model_results in results:
+                if i < len(model_results):
+                    result = model_results[i]
+                    image_url = f'https://huggingface.co/datasets/Nekoko/StickerSet/resolve/main/{result["entity"]["file_name"]}'
+                    row.append(f'![Sticker]({image_url})\n相似度: {result["distance"]:.4f}')
+                else:
+                    row.append('-')
+            formatted_results.append(row)
+        return formatted_results
+
+    def init_collections():
+        try:
+            client = MilvusClient(uri='./sticker.db')
+            stickers = client.query(
+                collection_name='stickers',
+                filter='',
+                limit=1000,
+                output_fields=['title', 'description', 'tags', 'file_name']
+            )
+            logger.info(f'Stickers loaded: {len(stickers)}')
+
+            def init_model(model_name):
+                try:
+                    searcher = StickerSearcher(SentenceTransformerModel(model_name))
+                    if searcher.init_collection():
+                        searchers[model_name] = searcher
+                        for sticker in stickers:
+                            searcher.store_vector(
+                                sticker.get('title'),
+                                sticker.get('description'),
+                                sticker.get('tags'),
+                                sticker.get('file_name')
+                                )
+                        logger.info(f'Model initialized: {model_name}')
+                except Exception as e:
+                    logger.error(f'Model init failed: {model_name} | Error: {str(e)}')
+
+            for model_name in models:
+                print(f'>>>> 初始化模型 {model_name}')
+                start_time = time.time()
+                init_model(model_name)
+                print(f'>>>> 初始化模型 {model_name} 完成 ✅，耗时 {time.time() - start_time:.4f} 秒')
+            print(f'>>>> 初始化所有模型完成 ✅')
+            return '初始化成功！'
+        except Exception as e:
+            logger.error(f'Data init failed: {str(e)}')
+            return f'初始化失败: {str(e)}'
+
+    with gr.Blocks(title='Neko Sticker Search 🔍', css='.gradio-container img { width: 200px !important; height: 200px !important; object-fit: contain; }') as demo:
+        with gr.Row():
+            search_input = gr.Textbox(label='搜索关键词')
+            search_button = gr.Button('搜索')
+
+
+        headers = ['序号'] + [f'🧊{model.split("/")[-1]}' for i, model in enumerate(models)]
+        results_table = gr.Dataframe(
+            headers=headers,
+            datatype=['number'] + ['markdown'] * len(models),
+            row_count=5,
+            col_count=len(models) + 1
+        )
+
+        status_box = gr.Textbox(label='状态', interactive=False)
+        refresh_button = gr.Button('刷新数据')
+
+        refresh_button.click(fn=init_collections, outputs=status_box)
+        # 由于这里只是简单的搜索操作，可以直接使用同步方式调用
+        search_button.click(
+            fn=search_all_models, 
+            inputs=[search_input], 
+            outputs=results_table
+)
+
+    return demo
+
+if __name__ == '__main__':
+
+    # 提前加载所有模型
+    start_time = time.time()
+    for index, model_name in enumerate(models):
+        try:
+            start_time = time.time()
+            searchers[model_name] = StickerSearcher(SentenceTransformerModel(model_name))
+            print(f'>>>> 预加载模型 {model_name} 完成 ✅, 耗时 {time.time() - start_time:.4f} 秒')
+        except Exception as e:
+            logger.error(f'Model preload failed: {model_name} | Error: {str(e)}')
+
+    logger.info(f'>>>> 预加载模型完成 ✅: {models}, 耗时 {time.time() - start_time:.4f} 秒')
+
+
+
+    demo = create_gradio_ui()
+    demo.launch()

main.py

@@ -0,0 +1,78 @@
+import gradio as gr
+from fastapi import FastAPI
+import uvicorn
+import logging
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+# 创建FastAPI应用
+app = FastAPI()
+
+# 使用app/database.py中的数据库实例
+from app.database import db
+
+def init_milvus():
+    """初始化 Milvus 数据库"""
+    db.init_collection()
+
+# 图像处理相关功能已经在app/image_utils.py中实现
+
+
+
+# 使用app/services.py中的服务
+from app.services import sticker_service
+
+# 使用app/services.py中的服务，不再需要重复实现这些功能
+
+# 导入UI模块
+from app.ui import StickerUI
+
+# 创建Gradio界面
+
+# FastAPI 路由
+@app.get("/api/list_stickers")
+def api_get_stickers():
+
+    sticker_list = sticker_service.get_all_stickers()
+    print('>>> GET Sticker_list', sticker_list)
+    return sticker_list
+
+
+@app.post("/api/search_stickers")
+async def api_search_stickers(request: dict):
+    description = request.get('description', '')
+    if len(description) > 0:
+        sticker_list = sticker_service.search_stickers(
+            description=description,
+            limit=1,
+        )
+        print('>>> GET Sticker_list', sticker_list)
+        return sticker_list
+    return []  # 当描述为空时返回空列表
+
+
+
+@app.post("/api/delete_sticker")
+async def api_delete_stickers(request: dict):
+    """Delete sticker by ID"""
+    try:
+        sticker_id = request.get('id')
+        if not sticker_id:
+            return {"status": "error", "message": "Missing sticker ID"}
+            
+        result = sticker_service.delete_sticker(sticker_id)
+        return {"status": "success", "message": result}
+    except Exception as e:
+        logger.error(f"Delete failed: {str(e)}")
+        return {"status": "error", "message": f"Delete failed: {str(e)}"}
+
+
+
+# 启动应用
+if __name__ == "__main__":
+    init_milvus()
+    ui = StickerUI()
+    app = gr.mount_gradio_app(app, ui.create_ui(), '/')
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,10 @@
+huggingface_hub>=0.19.4
+requests>=2.31.0
+cozepy>=0.1.0
+gradio==5.22.0
+sentence_transformers>=2.2.2
+fastapi>=0.104.1
+uvicorn>=0.24.0
+Pillow>=10.1.0
+pymilvus>=2.3.3
+openai==1.69.0

sticker copy.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65df0d7b09c24a836da3b90a7e3677c8e15e21b9fe42a02b18d1671d375d3df0
+size 77824

sticker-server/README.md

@@ -0,0 +1,45 @@
+# Sticker MCP Server
+
+This is an MCP (Model Context Protocol) server that allows large language models to search and send stickers based on emotion descriptions.
+
+## Features
+
+- Search for stickers based on emotion descriptions
+- Return sticker images in response to queries
+- Integrate with existing sticker search API
+
+## Installation
+
+1. Install dependencies:
+
+   ```
+   cd sticker-server
+   npm install
+   ```
+
+2. Make the server executable:
+
+   ```
+   npm run build
+   ```
+
+3. Configure the MCP settings:
+
+   - For Claude Desktop app: Copy the contents of `mcp_settings.json` to `~/Library/Application Support/Claude/claude_desktop_config.json`
+   - For Claude VSCode extension: Copy the contents of `mcp_settings.json` to `~/Library/Application Support/Trae/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json`
+
+   If the file already exists, merge the "sticker" entry into the existing "mcpServers" object.
+
+## Usage
+
+Once the MCP server is configured, you can ask the language model to express emotions using stickers. For example:
+
+- "Show me a happy sticker"
+- "I'm feeling sad, can you send a sticker?"
+- "Send a cat sticker"
+
+The language model will use the `send_sticker` tool to search for and display an appropriate sticker based on your request.
+
+## How it Works
+
+The server connects to a local sticker search API running at `http://localhost:7860/api/search_stickers` and provides the search results to the language model, which can then display the stickers in its responses.

sticker-server/index.ts

@@ -0,0 +1,125 @@
+#!/usr/bin/env node
+import { Server } from "@modelcontextprotocol/sdk/server/index.js";
+import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
+import {
+  ErrorCode,
+  ListResourcesRequestSchema,
+  ListResourcesResult,
+  ListResourceTemplatesRequestSchema,
+  McpError,
+  ReadResourceRequestSchema,
+} from "@modelcontextprotocol/sdk/types.js";
+
+interface Resource {
+  name: string;
+  description: string;
+  inputSchema: {
+    type: string;
+    properties: Record<
+      string,
+      {
+        type: string;
+        description: string;
+      }
+    >;
+    required?: string[];
+  };
+}
+
+class StickerServer {
+  private server: Server;
+
+  constructor() {
+    this.server = new Server(
+      {
+        name: "sticker-server",
+        version: "1.0.0",
+      },
+      {
+        capabilities: {
+          resources: {},
+        },
+      }
+    );
+
+    this.setupResourceHandlers();
+
+    this.server.onerror = (error: Error) => console.error("[MCP Error]", error);
+    process.on("SIGINT", async () => {
+      await this.server.close();
+      process.exit(0);
+    });
+  }
+
+  private setupResourceHandlers(): void {
+    this.server.setRequestHandler(
+      ListResourceTemplatesRequestSchema,
+      async () => ({
+        resourceTemplates: [
+          {
+            uriTemplate:
+              "mcp://sticker-server/send_sticker?description={description}",
+            name: "send sticker",
+            mimeType: "text",
+            description:
+              "请正常回复用户的问题，只有在正常回答用户问题后，根据情境需要，使用此 Resource 读取一个表达特定情绪或描述的贴纸的 URL，并将图片展示到对话内，需要展示图片，而不是 URL",
+          },
+        ],
+      })
+    );
+
+    // Read resource contents
+    this.server.setRequestHandler(
+      ReadResourceRequestSchema,
+      async (request) => {
+        try {
+          const uri = request.params.uri;
+          // mcp://sticker-server/send_sticker?description={description}
+          const description = new URL(uri).searchParams.get("description");
+          const response = await fetch(
+            "http://127.0.0.1:7860/api/search_stickers",
+            {
+              method: "POST",
+              headers: {
+                "Content-Type": "application/json",
+              },
+              body: JSON.stringify({ description }),
+            }
+          );
+          const stickerList = await response.json();
+          console.log("uri", uri, "stickerList", stickerList);
+          const imageUrl = stickerList?.[0]?.image_url || "找不到贴纸呢～";
+          await new Promise((resolve) => setTimeout(resolve, 1000));
+          return {
+            contents: [
+              {
+                uri,
+                type: "text",
+                text: imageUrl,
+              },
+            ],
+          };
+        } catch (error) {
+          return {
+            contents: [
+              {
+                uri: "",
+                type: "text",
+                text: JSON.stringify(error),
+              },
+            ],
+          };
+        }
+      }
+    );
+  }
+
+  async run(): Promise<void> {
+    const transport = new StdioServerTransport();
+    await this.server.connect(transport);
+    console.error("Sticker MCP server running on stdio");
+  }
+}
+
+const server = new StickerServer();
+server.run().catch(console.error);

sticker-server/mcp_settings.json

@@ -0,0 +1,10 @@
+{
+  "mcpServers": {
+    "sticker": {
+      "command": "node",
+      "args": ["/Users/bytedance/dev/NekoAI-Lab/sticker-server/dist/index.js"],
+      "disabled": false,
+      "autoApprove": []
+    }
+  }
+}

sticker-server/package-lock.json

@@ -0,0 +1,1871 @@
+{
+  "name": "sticker-server",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "sticker-server",
+      "version": "1.0.0",
+      "dependencies": {
+        "@modelcontextprotocol/sdk": "*",
+        "axios": "^1.6.0",
+        "node-fetch": "^3.3.2"
+      },
+      "devDependencies": {
+        "@types/node": "^22.13.14",
+        "ts-node": "^10.9.2",
+        "ts-node-dev": "^2.0.0",
+        "typescript": "^5.3.3"
+      }
+    },
+    "node_modules/@cspotcode/source-map-support": {
+      "version": "0.8.1",
+      "resolved": "https://bnpm.byted.org/@cspotcode/source-map-support/-/source-map-support-0.8.1.tgz",
+      "integrity": "sha512-IchNf6dN4tHoMFIn/7OE8LWZ19Y6q/67Bmf6vnGREv8RSbBVb9LPJxEcnwrcwX6ixSvaiGoomAUvu4YSxXrVgw==",
+      "dev": true,
+      "dependencies": {
+        "@jridgewell/trace-mapping": "0.3.9"
+      },
+      "engines": {
+        "node": ">=12"
+      }
+    },
+    "node_modules/@jridgewell/resolve-uri": {
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+}

sticker-server/package.json

@@ -0,0 +1,22 @@
+{
+  "name": "sticker-server",
+  "version": "1.0.0",
+  "description": "MCP Server for searching and sending stickers based on emotions",
+  "main": "dist/index.js",
+  "type": "module",
+  "scripts": {
+    "start": "node index.js",
+    "build": "tsc -p tsconfig.json"
+  },
+  "dependencies": {
+    "@modelcontextprotocol/sdk": "*",
+    "axios": "^1.6.0",
+    "node-fetch": "^3.3.2"
+  },
+  "devDependencies": {
+    "@types/node": "^22.13.14",
+    "ts-node": "^10.9.2",
+    "ts-node-dev": "^2.0.0",
+    "typescript": "^5.3.3"
+  }
+}

sticker-server/tsconfig.json

@@ -0,0 +1,14 @@
+{
+  "compilerOptions": {
+    "target": "ES2024",
+    "module": "NodeNext",
+    "outDir": "./dist",
+    "rootDir": ".",
+    "strict": true,
+    "esModuleInterop": true,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true
+  },
+  "include": ["**/*.ts"],
+  "exclude": ["node_modules"]
+}

sticker.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:705b4affd214a39db0184067d46b599ae5649c110ff700a7c277015dc4d21bea
+size 716800

upload.py

@@ -0,0 +1,76 @@
+"""
+批量处理贴纸图片的工具模块
+
+功能：
+1. 批量处理指定目录下的图片文件
+2. 使用MD5哈希进行图片去重
+3. 生成图片嵌入向量并存储到数据库
+4. 可选地将处理后的图片移动到输出目录
+"""
+import os
+from typing import List
+from pathlib import Path
+
+from app.services import sticker_service
+
+
+def batch_process_stickers(image_dir: str = 'images.tmp', output_dir: str = 'processed.tmp') -> None:
+    """
+    批量处理目录中的贴纸图片
+    
+    参数:
+        image_dir: 包含贴纸图片的目录路径
+        output_dir: 存储处理后图片的输出目录(默认为'processed')
+    
+    功能:
+        1. 遍历目录中的所有图片文件
+        2. 使用MD5哈希进行去重
+        3. 为每张图片生成嵌入向量并存储到数据库
+        4. 可选地将处理后的图片移动到输出目录
+    """
+    # 创建输出目录(如果不存在)
+    os.makedirs(output_dir, exist_ok=True)
+    # 用于存储已处理图片的哈希集合
+    # 遍历目录中的所有文件
+    # 打印图片数量和列表
+    print('>>> 待处理图片数量', len(os.listdir(image_dir)))
+    for img_path in Path(image_dir).glob('*.*'):
+        # 只处理jpg/jpeg/png格式的图片
+        print('>>>> 正在处理图片', img_path)
+        if img_path.suffix.lower() not in ['.jpg', '.jpeg', '.png']:
+            continue
+        
+        try:
+            # 加载图片并准备元数据
+            from PIL import Image
+            img = Image.open(img_path)
+            title = img_path.stem  # 使用文件名作为标题
+            
+            # 调用上传服务
+            sticker_service.upload_sticker(img, title, "", "")
+            
+            # 可选操作: 将处理后的图片移动到输出目录
+            output_path = os.path.join(output_dir, img_path.name)
+            os.rename(str(img_path), output_path)
+            
+            print(f"已处理: {img_path}")
+        except Exception as e:
+            print(f"处理异常 {img_path}: {str(e)}")
+
+
+if __name__ == '__main__':
+    """
+    命令行入口
+    用法: python batch_upload.py <图片目录> [--output 输出目录]
+    """
+    import argparse
+    
+    # 设置命令行参数解析
+    parser = argparse.ArgumentParser(description='批量处理贴纸图片(带去重功能)')
+    parser.add_argument('image_dir', help='包含贴纸图片的目录')
+    parser.add_argument('--output', '-o', default='processed.tmp', 
+                       help='处理后图片的输出目录(默认为processed)')
+    
+    # 解析参数并执行批量处理
+    args = parser.parse_args()
+    batch_process_stickers(args.image_dir, args.output)