feat: 添加以图搜图功能（CLIP ONNX + pgvector）+ Dify会话修复 + 版本升至V3.30
This commit is contained in:
@ -2,7 +2,7 @@ version: '3.8'
|
||||
|
||||
services:
|
||||
db:
|
||||
image: postgres:15-alpine
|
||||
image: pgvector/pgvector:pg15 # 换成这个
|
||||
container_name: inventory_db
|
||||
restart: always
|
||||
environment:
|
||||
@ -10,7 +10,7 @@ services:
|
||||
POSTGRES_PASSWORD: 1234
|
||||
POSTGRES_DB: inventory_system
|
||||
volumes:
|
||||
- ./pgdata_docker:/var/lib/postgresql/data
|
||||
- ./pgdata_docker:/var/lib/postgresql/data # 这里保持不变，Docker会自动创建这个新文件夹
|
||||
ports:
|
||||
- "5435:5432"
|
||||
|
||||
@ -41,4 +41,4 @@ services:
|
||||
ports:
|
||||
- "5175:5173"
|
||||
depends_on:
|
||||
- backend
|
||||
- backend
|
||||
@ -90,6 +90,17 @@ def create_app():
|
||||
except ImportError as e:
|
||||
print(f"❌ 错误: Upload 模块导入失败: {e}")
|
||||
|
||||
# -----------------------------------------------------
|
||||
# 2.4 注册以图搜图模块 (Image Search)
|
||||
# -----------------------------------------------------
|
||||
try:
|
||||
from app.api.v1.common.image_search import image_search_bp
|
||||
app.register_blueprint(image_search_bp, url_prefix='/api/v1/common')
|
||||
app.register_blueprint(image_search_bp, url_prefix='/api/common', name='image_search_legacy')
|
||||
print("✅ Image Search 模块注册成功")
|
||||
except ImportError as e:
|
||||
print(f"❌ 错误: Image Search 模块导入失败: {e}")
|
||||
|
||||
# -----------------------------------------------------
|
||||
# 2.4 注册业务操作模块 (Transactions - 借还/维修/报废)
|
||||
# -----------------------------------------------------
|
||||
|
||||
126
inventory-backend/app/api/v1/common/image_search.py
Normal file
126
inventory-backend/app/api/v1/common/image_search.py
Normal file
@ -0,0 +1,126 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
以图搜图 API - CLIP Vision Embedding + pgvector 余弦距离检索
|
||||
"""
|
||||
|
||||
import os
|
||||
import uuid
|
||||
import json
|
||||
from flask import Blueprint, request, jsonify
|
||||
from sqlalchemy import text
|
||||
from app.extensions import db
|
||||
from app.utils.ai_vision import load_clip_model, get_image_embedding
|
||||
|
||||
# 注册蓝图
|
||||
image_search_bp = Blueprint('image_search', __name__)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# POST /api/v1/common/image-search
|
||||
# 以图搜图：上传图片 → CLIP embedding → pgvector 余弦相似度检索
|
||||
# ============================================================================
|
||||
|
||||
@image_search_bp.route('/image-search', methods=['POST'])
|
||||
def image_search():
|
||||
# ---------------------------------------------------------
|
||||
# 1. 检查文件
|
||||
# ---------------------------------------------------------
|
||||
if 'file' not in request.files:
|
||||
return jsonify({"code": 400, "msg": "未找到图片文件"}), 400
|
||||
|
||||
file = request.files['file']
|
||||
if file.filename == '':
|
||||
return jsonify({"code": 400, "msg": "未选择文件"}), 400
|
||||
|
||||
# ---------------------------------------------------------
|
||||
# 2. 安全保存临时文件
|
||||
# ---------------------------------------------------------
|
||||
ext = file.filename.rsplit('.', 1)[-1].lower()
|
||||
if ext not in {'png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp'}:
|
||||
return jsonify({"code": 400, "msg": "不支持的图片格式"}), 400
|
||||
|
||||
tmp_filename = f"{uuid.uuid4().hex}.{ext}"
|
||||
tmp_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'uploads')
|
||||
os.makedirs(tmp_dir, exist_ok=True)
|
||||
tmp_path = os.path.join(tmp_dir, tmp_filename)
|
||||
|
||||
try:
|
||||
file.save(tmp_path)
|
||||
print(f"💾 [ImageSearch] 临时文件已保存: {tmp_path}")
|
||||
|
||||
# ---------------------------------------------------------
|
||||
# 3. 提取 CLIP embedding
|
||||
# ---------------------------------------------------------
|
||||
load_clip_model()
|
||||
embedding = get_image_embedding(tmp_path)
|
||||
print(f"✅ [ImageSearch] Embedding 提取成功，维度: {len(embedding)}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ [ImageSearch] 图像处理失败: {e}")
|
||||
return jsonify({"code": 500, "msg": f"图像处理失败: {str(e)}"}), 500
|
||||
|
||||
finally:
|
||||
# ---------------------------------------------------------
|
||||
# 4. 无论成功与否，都删除临时文件
|
||||
# ---------------------------------------------------------
|
||||
if os.path.exists(tmp_path):
|
||||
try:
|
||||
os.remove(tmp_path)
|
||||
print(f"🗑️ [ImageSearch] 临时文件已清理: {tmp_path}")
|
||||
except Exception as e:
|
||||
print(f"⚠️ [ImageSearch] 临时文件删除失败: {e}")
|
||||
|
||||
# ---------------------------------------------------------
|
||||
# 5. pgvector 余弦相似度检索
|
||||
# ---------------------------------------------------------
|
||||
try:
|
||||
# 将 Python list 转为 PostgreSQL 向量格式: '[0.1, 0.2, ...]'
|
||||
query_vector_str = '[' + ','.join(str(v) for v in embedding) + ']'
|
||||
|
||||
sql = text("""
|
||||
SELECT id, name, spec_model, product_image,
|
||||
(1 - (img_embedding <=> :query_vector)) AS similarity
|
||||
FROM material_base
|
||||
WHERE img_embedding IS NOT NULL
|
||||
ORDER BY img_embedding <=> :query_vector
|
||||
LIMIT 5
|
||||
""")
|
||||
|
||||
result = db.session.execute(sql, {"query_vector": query_vector_str})
|
||||
rows = result.fetchall()
|
||||
|
||||
results = []
|
||||
for row in rows:
|
||||
product_id = row[0]
|
||||
product_name = row[1] or ""
|
||||
spec_model = row[2] or ""
|
||||
product_image = row[3]
|
||||
|
||||
# 解析图片 URL 列表，取第一张
|
||||
image_url = ""
|
||||
if product_image:
|
||||
try:
|
||||
image_list = json.loads(product_image)
|
||||
if image_list and len(image_list) > 0:
|
||||
image_url = image_list[0]
|
||||
except Exception:
|
||||
image_url = str(product_image)
|
||||
|
||||
results.append({
|
||||
"product_id": product_id,
|
||||
"product_name": product_name,
|
||||
"spec_model": spec_model,
|
||||
"image_url": image_url,
|
||||
"similarity": round(float(row[4]), 4)
|
||||
})
|
||||
|
||||
print(f"✅ [ImageSearch] 检索完成，命中 {len(results)} 条结果")
|
||||
return jsonify({
|
||||
"code": 200,
|
||||
"msg": "检索成功",
|
||||
"data": results
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ [ImageSearch] 数据库检索失败: {e}")
|
||||
return jsonify({"code": 500, "msg": f"检索失败: {str(e)}"}), 500
|
||||
@ -11,6 +11,8 @@ Dify 智能客服权限服务层
|
||||
- 跨模块越权查询：直接阻断，返回角色专属的错误信息给大模型
|
||||
"""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from flask import g, current_app
|
||||
from flask_jwt_extended import decode_token
|
||||
from app.models.system import SysRolePermission
|
||||
@ -185,7 +187,7 @@ class DifyPermissionService:
|
||||
返回:
|
||||
{
|
||||
'blocked': bool, # 是否被拦截
|
||||
'message': str | None, # AI 应返回给用户的错误信息（如果有）
|
||||
'message': Optional[str], # AI 应返回给用户的错误信息（如果有）
|
||||
}
|
||||
"""
|
||||
if DifyPermissionService.is_super_admin(role):
|
||||
|
||||
@ -20,6 +20,8 @@ import logging
|
||||
from threading import Thread
|
||||
from datetime import datetime
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from openpyxl import Workbook
|
||||
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
|
||||
|
||||
@ -346,7 +348,7 @@ def get_task_status(task_id: str) -> dict:
|
||||
# 获取导出文件路径（供下载接口调用）
|
||||
# =============================================================================
|
||||
|
||||
def get_export_filepath(task_id: str) -> str | None:
|
||||
def get_export_filepath(task_id: str) -> Optional[str]:
|
||||
"""
|
||||
根据 task_id 返回已生成文件的完整路径。
|
||||
未完成或不存在返回 None。
|
||||
|
||||
132
inventory-backend/app/utils/ai_vision.py
Normal file
132
inventory-backend/app/utils/ai_vision.py
Normal file
@ -0,0 +1,132 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
AI Vision 模块 - CLIP Vision Encoder ONNX 推理
|
||||
"""
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import onnxruntime as ort
|
||||
|
||||
# ============================================================================
|
||||
# 全局模型单例（项目启动时加载一次）
|
||||
# ============================================================================
|
||||
|
||||
MODEL_PATH = os.path.join(os.path.dirname(__file__), '..', '..', 'models', 'clip_vision.onnx')
|
||||
|
||||
# 加载选项：CPU 推理，禁用依赖库的启动开销
|
||||
_session_options = ort.SessionOptions()
|
||||
_session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||||
|
||||
ort_session: ort.InferenceSession = None
|
||||
|
||||
|
||||
def load_clip_model():
|
||||
"""启动时调用：全局加载 CLIP Vision 模型"""
|
||||
global ort_session
|
||||
if ort_session is not None:
|
||||
return ort_session
|
||||
|
||||
if not os.path.exists(MODEL_PATH):
|
||||
raise FileNotFoundError(f"CLIP Vision 模型未找到: {MODEL_PATH}")
|
||||
|
||||
ort_session = ort.InferenceSession(MODEL_PATH, sess_options=_session_options, providers=['CPUExecutionProvider'])
|
||||
print(f"✅ [AI Vision] CLIP 模型加载成功: {MODEL_PATH}")
|
||||
return ort_session
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# CLIP 预处理常量
|
||||
# ============================================================================
|
||||
|
||||
# ImageNet 标准归一化（CLIP 官方）
|
||||
IMAGENET_MEAN = [0.485, 0.456, 0.406]
|
||||
IMAGENET_STD = [0.229, 0.224, 0.225]
|
||||
|
||||
# 模型输入尺寸
|
||||
INPUT_SIZE = 224
|
||||
|
||||
|
||||
def _center_crop_and_resize(image: Image.Image) -> Image.Image:
|
||||
"""
|
||||
CLIP 官方预处理：中心裁剪抗干扰
|
||||
- 将图片最短边缩放到 224
|
||||
- 从正中间切取 224x224 区域
|
||||
"""
|
||||
w, h = image.size
|
||||
|
||||
# 计算缩放后的目标尺寸
|
||||
if w < h:
|
||||
new_w = INPUT_SIZE
|
||||
new_h = int(h * INPUT_SIZE / w)
|
||||
else:
|
||||
new_h = INPUT_SIZE
|
||||
new_w = int(w * INPUT_SIZE / h)
|
||||
|
||||
# 缩放
|
||||
image = image.resize((new_w, new_h), Image.BILINEAR)
|
||||
|
||||
# 中心裁剪
|
||||
left = (new_w - INPUT_SIZE) // 2
|
||||
top = (new_h - INPUT_SIZE) // 2
|
||||
right = left + INPUT_SIZE
|
||||
bottom = top + INPUT_SIZE
|
||||
|
||||
return image.crop((left, top, right, bottom))
|
||||
|
||||
|
||||
def _normalize(image_np: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
对 224x224x3 图像进行 CLIP 标准归一化
|
||||
image_np: shape (H, W, C), dtype uint8, 值域 [0, 255]
|
||||
返回: shape (C, H, W), dtype float32, 值域 [0, 1]
|
||||
"""
|
||||
# HWC -> CHW
|
||||
image_np = image_np.transpose(2, 0, 1).astype(np.float32) / 255.0
|
||||
|
||||
# 归一化
|
||||
for i, (mean, std) in enumerate(zip(IMAGENET_MEAN, IMAGENET_STD)):
|
||||
image_np[i] = (image_np[i] - mean) / std
|
||||
|
||||
return image_np
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 主函数：提取图像 embedding
|
||||
# ============================================================================
|
||||
|
||||
def get_image_embedding(image_path: str) -> list:
|
||||
"""
|
||||
提取图像的 512 维 CLIP embedding 向量
|
||||
|
||||
参数:
|
||||
image_path: 图像文件路径（支持本地路径或 URL）
|
||||
|
||||
返回:
|
||||
list: 512 维浮点向量
|
||||
"""
|
||||
if ort_session is None:
|
||||
load_clip_model()
|
||||
|
||||
# 加载图像
|
||||
try:
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
except Exception as e:
|
||||
raise ValueError(f"图像加载失败: {image_path}, 错误: {e}")
|
||||
|
||||
# 中心裁剪
|
||||
image = _center_crop_and_resize(image)
|
||||
|
||||
# 归一化
|
||||
input_data = _normalize(np.array(image))
|
||||
|
||||
# 添加 batch 维度: (C, H, W) -> (1, C, H, W)
|
||||
input_data = np.expand_dims(input_data, axis=0)
|
||||
|
||||
# 推理
|
||||
outputs = ort_session.run(None, {'images': input_data.astype(np.float32)})
|
||||
|
||||
# 输出通常是 (1, 512) 的向量，取第一项并展平为 list
|
||||
embedding = outputs[0][0].tolist()
|
||||
|
||||
return embedding
|
||||
@ -10,6 +10,10 @@ flask-cors==4.0.0
|
||||
redis==5.0.1
|
||||
# 图片处理核心库
|
||||
Pillow>=10.0.0
|
||||
# ONNX 模型本地 CPU 推理
|
||||
onnxruntime>=1.16.0
|
||||
# 数值计算（ONNX 推理依赖）
|
||||
numpy>=1.24.0
|
||||
# [旧] 条形码生成库 (建议保留，防止旧代码报错)
|
||||
python-barcode>=0.14.0
|
||||
# [新增] 二维码生成库 (标签打印必需，包含PIL支持)
|
||||
|
||||
@ -11,6 +11,15 @@
|
||||
<script type="module" src="/src/main.ts"></script>
|
||||
<script>
|
||||
window.initDifyChatbot = function() {
|
||||
// 【关键】增加保护检查：确保 DOM 已经就绪
|
||||
if (document.readyState === 'loading') {
|
||||
document.addEventListener('DOMContentLoaded', performInit);
|
||||
} else {
|
||||
performInit();
|
||||
}
|
||||
};
|
||||
|
||||
function performInit() {
|
||||
var currentToken = localStorage.getItem('access_token') || localStorage.getItem('token') || '';
|
||||
var username = localStorage.getItem("username") || '';
|
||||
|
||||
@ -19,17 +28,16 @@
|
||||
return;
|
||||
}
|
||||
|
||||
// 【新增 1】彻底清理浏览器内存中残留的 Dify 全局对象
|
||||
// 彻底清理浏览器内存中残留的 Dify 全局对象
|
||||
window.difyChatbot = undefined;
|
||||
delete window.difyChatbot;
|
||||
|
||||
// 【新增 2】清理旧的 DOM 节点
|
||||
// 清理旧的 DOM 节点
|
||||
var oldScript = document.getElementById('6T0eTgukUEqzK0iW');
|
||||
if (oldScript) oldScript.remove();
|
||||
document.querySelectorAll('[id^="dify-chatbot-"]').forEach(function(el) { el.remove(); });
|
||||
|
||||
// 【核心破解 3】动态化 user_id，打破 Dify 会话锁定机制
|
||||
// 取 token 的最后 8 位拼在用户名后。只要 Token 变了，Dify 就会开启新会话，强制读取新 Token。
|
||||
// 动态化 user_id，打破 Dify 会话锁定机制
|
||||
var dynamicUserId = username + '_' + currentToken.slice(-8);
|
||||
|
||||
window.difyChatbotConfig = {
|
||||
@ -39,22 +47,20 @@
|
||||
"user_token": currentToken
|
||||
},
|
||||
systemVariables: {
|
||||
"user_id": dynamicUserId // <- 这里使用了动态 ID
|
||||
"user_id": dynamicUserId
|
||||
},
|
||||
userVariables: {},
|
||||
};
|
||||
|
||||
// 【新增 4】在脚本 URL 后加上时间戳，破除浏览器强缓存
|
||||
// 重新挂载
|
||||
var script = document.createElement('script');
|
||||
script.src = 'http://172.16.0.198:8080/embed.min.js?t=' + new Date().getTime();
|
||||
script.id = '6T0eTgukUEqzK0iW';
|
||||
script.defer = true;
|
||||
document.head.appendChild(script);
|
||||
|
||||
console.log('✅ Dify chatbot 已挂载新会话，当前绑定 ID:', dynamicUserId);
|
||||
};
|
||||
|
||||
setTimeout(window.initDifyChatbot, 100);
|
||||
console.log('✅ Dify chatbot 已挂载新会话，当前绑定 ID:', dynamicUserId);
|
||||
}
|
||||
</script>
|
||||
|
||||
<!--<script-->
|
||||
@ -71,7 +77,7 @@
|
||||
|
||||
/* 变成"独立悬浮窗口" */
|
||||
#dify-chatbot-bubble-window {
|
||||
/* 👇 解除原本锁定在右下角的限制，将其定位在屏幕中间偏左上 */
|
||||
/* 解除原本锁定在右下角的限制，将其定位在屏幕中间偏左上 */
|
||||
top: 15vh !important;
|
||||
left: 20vw !important;
|
||||
bottom: auto !important;
|
||||
@ -82,9 +88,9 @@
|
||||
height: 70vh !important;
|
||||
|
||||
border-radius: 12px !important;
|
||||
box-shadow: 0 12px 48px rgba(0, 0, 0, 0.2) !important; /* 增加超大弥散阴影，浮现感更强 */
|
||||
box-shadow: 0 12px 48px rgba(0, 0, 0, 0.2) !important;
|
||||
|
||||
/* 👇 开启右下角拖拽，并强制留出 16px 的白边给拖拽手柄 */
|
||||
/* 开启右下角拖拽，并强制留出 16px 的白边给拖拽手柄 */
|
||||
resize: both !important;
|
||||
overflow: hidden !important;
|
||||
padding-bottom: 16px !important;
|
||||
@ -124,4 +130,4 @@
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
</html>
|
||||
@ -20,6 +20,10 @@ onMounted(() => {
|
||||
if (userStore.token) {
|
||||
userStore.refreshUserPermissions()
|
||||
}
|
||||
// 当 Vue 根组件挂载完毕，确保 Dify 图标一定会被加载
|
||||
if (typeof (window as any).initDifyChatbot === 'function') {
|
||||
(window as any).initDifyChatbot()
|
||||
}
|
||||
})
|
||||
|
||||
// ================================================================
|
||||
@ -235,7 +239,7 @@ const handleLogout = () => {
|
||||
<footer v-if="!isLoginPage" class="app-footer">
|
||||
<span class="version-tag">
|
||||
<el-icon style="vertical-align: middle; margin-right: 4px"><InfoFilled /></el-icon>
|
||||
当前版本:V3.29（添加AI助手版）
|
||||
当前版本:V3.30（添加AI助手版）
|
||||
</span>
|
||||
</footer>
|
||||
|
||||
|
||||
@ -3,7 +3,6 @@ import request from '@/utils/request'
|
||||
/**
|
||||
* 上传文件通用接口
|
||||
* @param data File 对象 或 FormData 对象
|
||||
* 适配说明：list.vue 中 customUpload 已经封装了 FormData，所以这里支持直接传 FormData
|
||||
*/
|
||||
export function uploadFile(data: File | FormData) {
|
||||
let formData: FormData
|
||||
@ -11,14 +10,12 @@ export function uploadFile(data: File | FormData) {
|
||||
if (data instanceof FormData) {
|
||||
formData = data
|
||||
} else {
|
||||
// 如果传入的是原始 File 对象，则手动封装
|
||||
formData = new FormData()
|
||||
// @ts-ignore
|
||||
formData.append('file', data)
|
||||
}
|
||||
|
||||
return request({
|
||||
// 注意：这里 /v1/common/upload 需要与后端 BluePrint 注册的 url_prefix 对应
|
||||
url: '/v1/common/upload',
|
||||
method: 'post',
|
||||
data: formData,
|
||||
@ -29,13 +26,50 @@ export function uploadFile(data: File | FormData) {
|
||||
}
|
||||
|
||||
/**
|
||||
* 删除文件通用接口 (新增)
|
||||
* 删除文件通用接口
|
||||
* @param filename 文件名 (例如: a1b2c3d4.jpg)
|
||||
*/
|
||||
export function deleteFile(filename: string) {
|
||||
return request({
|
||||
// 对应后端路由: @upload_bp.route('/files/<filename>', methods=['DELETE'])
|
||||
url: `/v1/common/files/${filename}`,
|
||||
method: 'delete'
|
||||
})
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// 以图搜图 API
|
||||
// ============================================================================
|
||||
|
||||
/** 以图搜图返回的物料项 */
|
||||
export interface ImageSearchItem {
|
||||
product_id: number
|
||||
product_name: string
|
||||
spec_model: string
|
||||
image_url: string
|
||||
similarity: number
|
||||
}
|
||||
|
||||
/** 以图搜图响应结构 */
|
||||
export interface ImageSearchResponse {
|
||||
code: number
|
||||
msg: string
|
||||
data: ImageSearchItem[]
|
||||
}
|
||||
|
||||
/**
|
||||
* 以图搜图
|
||||
* @param file 图片文件 (File 对象或 Blob)
|
||||
*/
|
||||
export function imageSearch(file: File | Blob) {
|
||||
const formData = new FormData()
|
||||
formData.append('file', file)
|
||||
|
||||
return request<ImageSearchResponse>({
|
||||
url: '/v1/common/image-search',
|
||||
method: 'post',
|
||||
data: formData,
|
||||
headers: {
|
||||
'Content-Type': 'multipart/form-data'
|
||||
}
|
||||
})
|
||||
}
|
||||
458
inventory-web/src/components/ImageSearchDialog.vue
Normal file
458
inventory-web/src/components/ImageSearchDialog.vue
Normal file
@ -0,0 +1,458 @@
|
||||
<template>
|
||||
<el-dialog
|
||||
v-model="visible"
|
||||
title="以图搜图"
|
||||
width="680px"
|
||||
destroy-on-close
|
||||
:close-on-click-modal="false"
|
||||
@close="handleClose"
|
||||
>
|
||||
<div class="image-search-body">
|
||||
<!-- 左侧：图片上传 -->
|
||||
<div class="upload-section">
|
||||
<el-upload
|
||||
ref="uploadRef"
|
||||
class="image-uploader"
|
||||
drag
|
||||
:auto-upload="false"
|
||||
:show-file-list="false"
|
||||
accept="image/*"
|
||||
:on-change="handleFileChange"
|
||||
>
|
||||
<div v-if="!previewUrl" class="upload-placeholder">
|
||||
<el-icon class="upload-icon" :size="48"><UploadFilled /></el-icon>
|
||||
<div class="upload-text">点击或拖拽图片上传</div>
|
||||
<div class="upload-hint">支持 jpg/png/gif 等格式</div>
|
||||
</div>
|
||||
<div v-else class="preview-wrapper">
|
||||
<img :src="previewUrl" class="preview-image" />
|
||||
<div class="preview-overlay">
|
||||
<el-button size="small" @click.stop="clearImage">重新选择</el-button>
|
||||
</div>
|
||||
</div>
|
||||
</el-upload>
|
||||
|
||||
<div v-if="searching" class="loading-tip">
|
||||
<el-icon class="is-loading"><Loading /></el-icon>
|
||||
<span>正在识别图片并检索...</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 右侧：搜索结果 -->
|
||||
<div class="result-section">
|
||||
<div v-if="!searched && !searching" class="result-empty">
|
||||
<el-icon :size="40" color="#c0c4cc"><Picture /></el-icon>
|
||||
<p>上传图片后自动检索</p>
|
||||
</div>
|
||||
|
||||
<div v-else-if="searched && results.length === 0" class="result-empty">
|
||||
<el-icon :size="40" color="#c0c4cc"><WarningFilled /></el-icon>
|
||||
<p>未找到相似物料</p>
|
||||
<p class="result-hint">请尝试更换图片</p>
|
||||
</div>
|
||||
|
||||
<div v-else class="result-list">
|
||||
<div
|
||||
v-for="(item, index) in results"
|
||||
:key="item.product_id"
|
||||
class="result-item"
|
||||
>
|
||||
<div class="item-rank">{{ index + 1 }}</div>
|
||||
<div class="item-image">
|
||||
<img
|
||||
v-if="item.image_url"
|
||||
:src="fullImageUrl(item.image_url)"
|
||||
@error="handleImgError($event)"
|
||||
/>
|
||||
<div v-else class="image-placeholder">
|
||||
<el-icon :size="24" color="#c0c4cc"><Picture /></el-icon>
|
||||
</div>
|
||||
</div>
|
||||
<div class="item-info">
|
||||
<div class="item-name">{{ item.product_name || '未命名物料' }}</div>
|
||||
<div class="item-spec">{{ item.spec_model || '无规格' }}</div>
|
||||
<div class="item-similarity">
|
||||
<span class="similarity-label">相似度</span>
|
||||
<span class="similarity-value">{{ (item.similarity * 100).toFixed(2) }}%</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="item-actions">
|
||||
<el-button
|
||||
type="primary"
|
||||
size="small"
|
||||
@click="handleUse(item)"
|
||||
>
|
||||
使用此物料
|
||||
</el-button>
|
||||
<el-button
|
||||
size="small"
|
||||
@click="handleView(item)"
|
||||
>
|
||||
查看详情
|
||||
</el-button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<template #footer>
|
||||
<el-button @click="handleClose">关闭</el-button>
|
||||
</template>
|
||||
</el-dialog>
|
||||
</template>
|
||||
|
||||
<script setup lang="ts">
|
||||
import { ref, watch } from 'vue'
|
||||
import { UploadFilled, Loading, Picture, WarningFilled } from '@element-plus/icons-vue'
|
||||
import { ElMessage } from 'element-plus'
|
||||
import { imageSearch, type ImageSearchItem } from '@/api/common/upload'
|
||||
|
||||
interface Props {
|
||||
modelValue: boolean
|
||||
}
|
||||
|
||||
const props = defineProps<Props>()
|
||||
|
||||
const emit = defineEmits<{
|
||||
(e: 'update:modelValue', val: boolean): void
|
||||
(e: 'use', item: ImageSearchItem): void
|
||||
(e: 'view', item: ImageSearchItem): void
|
||||
}>()
|
||||
|
||||
const visible = ref(props.modelValue)
|
||||
const uploadRef = ref()
|
||||
const previewUrl = ref('')
|
||||
const currentFile = ref<File | null>(null)
|
||||
const searching = ref(false)
|
||||
const searched = ref(false)
|
||||
const results = ref<ImageSearchItem[]>([])
|
||||
|
||||
watch(() => props.modelValue, (val) => {
|
||||
visible.value = val
|
||||
if (!val) {
|
||||
resetState()
|
||||
}
|
||||
})
|
||||
|
||||
watch(visible, (val) => {
|
||||
emit('update:modelValue', val)
|
||||
})
|
||||
|
||||
const handleFileChange = (uploadFile: any) => {
|
||||
const file = uploadFile.raw
|
||||
if (!file) return
|
||||
|
||||
// 校验格式
|
||||
const allowedTypes = ['image/jpeg', 'image/png', 'image/gif', 'image/webp', 'image/bmp']
|
||||
if (!allowedTypes.includes(file.type)) {
|
||||
ElMessage.warning('仅支持 jpg/png/gif/webp/bmp 格式')
|
||||
return
|
||||
}
|
||||
|
||||
currentFile.value = file
|
||||
previewUrl.value = URL.createObjectURL(file)
|
||||
|
||||
// 自动触发搜索
|
||||
doSearch(file)
|
||||
}
|
||||
|
||||
const doSearch = async (file: File) => {
|
||||
if (searching.value) return
|
||||
|
||||
searching.value = true
|
||||
searched.value = false
|
||||
results.value = []
|
||||
|
||||
try {
|
||||
const res = await imageSearch(file)
|
||||
if (res.code === 200) {
|
||||
results.value = res.data || []
|
||||
} else {
|
||||
ElMessage.error(res.msg || '检索失败')
|
||||
}
|
||||
} catch (err: any) {
|
||||
console.error('image search error:', err)
|
||||
ElMessage.error(err.message || '网络错误，请重试')
|
||||
} finally {
|
||||
searching.value = false
|
||||
searched.value = true
|
||||
}
|
||||
}
|
||||
|
||||
const clearImage = () => {
|
||||
previewUrl.value = ''
|
||||
currentFile.value = null
|
||||
results.value = []
|
||||
searched.value = false
|
||||
uploadRef.value?.clearFiles()
|
||||
}
|
||||
|
||||
const fullImageUrl = (path: string) => {
|
||||
if (!path) return ''
|
||||
// 相对路径转完整 URL
|
||||
if (path.startsWith('http')) return path
|
||||
const baseUrl = import.meta.env.VITE_API_BASE_URL || window.location.origin
|
||||
return baseUrl + path
|
||||
}
|
||||
|
||||
const handleImgError = (e: Event) => {
|
||||
const img = e.target as HTMLImageElement
|
||||
img.style.display = 'none'
|
||||
}
|
||||
|
||||
const handleUse = (item: ImageSearchItem) => {
|
||||
emit('use', item)
|
||||
handleClose()
|
||||
}
|
||||
|
||||
const handleView = (item: ImageSearchItem) => {
|
||||
emit('view', item)
|
||||
}
|
||||
|
||||
const handleClose = () => {
|
||||
visible.value = false
|
||||
}
|
||||
|
||||
const resetState = () => {
|
||||
previewUrl.value = ''
|
||||
currentFile.value = null
|
||||
searching.value = false
|
||||
searched.value = false
|
||||
results.value = []
|
||||
}
|
||||
</script>
|
||||
|
||||
<style scoped>
|
||||
.image-search-body {
|
||||
display: flex;
|
||||
gap: 24px;
|
||||
min-height: 380px;
|
||||
}
|
||||
|
||||
/* ── 左侧上传区 ── */
|
||||
.upload-section {
|
||||
flex: 0 0 220px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
.image-uploader {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
:deep(.el-upload) {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
:deep(.el-upload-dragger) {
|
||||
width: 100%;
|
||||
height: 280px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
background: #fafafa;
|
||||
border: 2px dashed #dcdfe6;
|
||||
border-radius: 8px;
|
||||
transition: border-color 0.2s;
|
||||
}
|
||||
|
||||
:deep(.el-upload-dragger:hover) {
|
||||
border-color: #409eff;
|
||||
}
|
||||
|
||||
.upload-placeholder {
|
||||
text-align: center;
|
||||
color: #909399;
|
||||
}
|
||||
|
||||
.upload-icon {
|
||||
color: #c0c4cc;
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
|
||||
.upload-text {
|
||||
font-size: 14px;
|
||||
font-weight: 500;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
|
||||
.upload-hint {
|
||||
font-size: 12px;
|
||||
color: #c0c4cc;
|
||||
}
|
||||
|
||||
.preview-wrapper {
|
||||
position: relative;
|
||||
width: 100%;
|
||||
height: 280px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
border-radius: 8px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.preview-image {
|
||||
max-width: 100%;
|
||||
max-height: 100%;
|
||||
object-fit: contain;
|
||||
}
|
||||
|
||||
.preview-overlay {
|
||||
position: absolute;
|
||||
bottom: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
padding: 10px;
|
||||
background: rgba(0, 0, 0, 0.5);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.preview-overlay .el-button {
|
||||
color: #fff;
|
||||
border-color: rgba(255, 255, 255, 0.6);
|
||||
}
|
||||
|
||||
.loading-tip {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
color: #409eff;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
/* ── 右侧结果区 ── */
|
||||
.result-section {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.result-empty {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
height: 280px;
|
||||
color: #909399;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.result-empty p {
|
||||
margin: 8px 0 0;
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.result-hint {
|
||||
font-size: 12px !important;
|
||||
color: #c0c4cc;
|
||||
}
|
||||
|
||||
.result-list {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
.result-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
padding: 10px 12px;
|
||||
background: #f5f7fa;
|
||||
border-radius: 8px;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.result-item:hover {
|
||||
background: #ecf5ff;
|
||||
}
|
||||
|
||||
.item-rank {
|
||||
flex: 0 0 24px;
|
||||
width: 24px;
|
||||
height: 24px;
|
||||
background: #409eff;
|
||||
color: #fff;
|
||||
border-radius: 50%;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-size: 12px;
|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
.item-image {
|
||||
flex: 0 0 60px;
|
||||
width: 60px;
|
||||
height: 60px;
|
||||
border-radius: 6px;
|
||||
overflow: hidden;
|
||||
background: #fff;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
border: 1px solid #ebeef5;
|
||||
}
|
||||
|
||||
.item-image img {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
object-fit: cover;
|
||||
}
|
||||
|
||||
.image-placeholder {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.item-info {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.item-name {
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: #303133;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.item-spec {
|
||||
font-size: 12px;
|
||||
color: #909399;
|
||||
margin-top: 4px;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.item-similarity {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 6px;
|
||||
}
|
||||
|
||||
.similarity-label {
|
||||
font-size: 12px;
|
||||
color: #909399;
|
||||
}
|
||||
|
||||
.similarity-value {
|
||||
font-size: 14px;
|
||||
font-weight: 700;
|
||||
color: #67c23a;
|
||||
}
|
||||
|
||||
.item-actions {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
flex: 0 0 auto;
|
||||
}
|
||||
</style>
|
||||
Reference in New Issue
Block a user