1. Project Overview

Garbage Classification Assistant is a WeChat Mini Program developed with uni-app. It uses Spring Boot 2 to build the backend service and Swagger 2 to create RESTful API documentation. It provides garbage classification features including text search, voice recognition, and image recognition.

This is a personally developed WeChat Mini Program that combines text search, voice recognition, and photo-based garbage classification into a simple utility. It is suitable for personal learning or graduation projects and is easy to get started with for secondary development.

2. Open Source License

Licensed under Apache-2.0

3. Screenshots

Some Preview Images

Homepage, Category Page, Category Details, Search Page

Voice Recognition, Challenge, Challenge Levels, Challenge Details

Web Page Screenshots

4. Feature Overview

Frontend: WeChat Mini Program. Developed using the uni-app framework, uni-app is a framework for developing all frontend applications with Vue.js. Developers only need to write one set of code to publish applications across multiple platforms, including iOS, Android, H5, and various mini programs (WeChat, Alipay, Baidu, Toutiao, QQ, and DingTalk).

Backend: Uses Spring Boot 2 to build the backend service and Swagger 2 to create RESTful API documentation. MySQL is used as the database, with MyBatis-Plus serving as the data access layer.

Voice recognition and image recognition use services provided by Baidu AI Cloud.

5. Tech Stack

Tech Stack

Technology

Name

Version

uni-app

A cross-platform framework based on Vue.

2.1.0.20190713

vuejs

A progressive JavaScript framework.

--

SpringBoot

An open-source Java application framework.

2.1.6.RELEASE

Mybatis-Plus

An enhancement toolkit for MyBatis.

3.0.6

swagger2

Time picker.

2.8.0

swagger-bootstrap-ui

Enhanced UI implementation for Swagger.

1.8.8

RuoYi-fast

Rapid development of backend management systems.

4.6.1

Installation Guide

1. Development requires the relevant IDE and JDK 8 environment. For frontend development with uni-app, download HBuilder. For backend development, download Eclipse or IDEA.
2. The voice recognition and image recognition features use services provided by Baidu AI Cloud. You need to register, log in, and create your own application (which includes 50,000 free API calls). In the configuration file under backend/afterend/uniapp-api, you need to configure appid, apikey, and secretkey.
3. Database Setup: Create a database named garbageSort, then import the garbagesort.sql database file located in the project root directory.
4. Backend Startup: Step 1: Open the uniapp-api module under the backend directory using IDEA. Step 2: Configure the database connection details and the Baidu API management settings mentioned above in application.yml. Step 3: Run UniappApiApplication` to start the backend service. You can visit [http://localhost:8899/wx/doc.html](http://localhost:8899/wx/doc.html) to view the API documentation.
5. Frontend Startup: Open the frontend directory in HBuilder. From the Run menu, select Run to Mini Program Simulator -> WeChat Developer Tools. Alternatively, open forntend -> unpackage -> dist -> dev -> mp-weixin using WeChat Developer Tools to run the WeChat Mini Program. (During secondary development, modify files in HBuilder and save them to preview the changes in WeChat Developer Tools.)
6. Web Application Startup: Open the web directory using IDEA, modify the database connection settings in the application-druid.yml configuration file, and run WebApplication.java to start the web-based garbage classification management system. Once started successfully, visit [http://localhost:8082](http://localhost:8082). Username/Password: admin/admin123