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@@ -4,7 +4,7 @@ This repository contains the backend code for the Personalized-SmartEd project,
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## Introduction
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The Go-Backend project is designed to provide a robust and scalable backend for the Personalized-SmartEd platform. It leverages the Go programming language to deliver high performance and concurrency.
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The Go-Backend project is designed to provide a robust and scalable backend for the Personalized-SmartEd platform. It leverages the Go programming language to deliver high performance and concurrent processing for educational services.
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## Features
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- RESTful API endpoints
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- Integration with various data sources
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## Table of Contents
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-[Introduction](#introduction)
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-[Features](#features)
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-[Installation](#installation)
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-[Usage](#usage)
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-[Project Substructure](#project-substructure)
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-[Interaction with ML-Service](#interaction-with-ml-service)
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-[Contributing](#contributing)
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## Installation
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To get started with the Go-Backend, follow these steps:
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go run main.go
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```
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## Project Substructure
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Below is the folder structure of the **Go-Backend** repository:
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```
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Go-Backend/
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├── cmd/
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│ └── main.go # Main application entry point
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├── internal/
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│ ├── config/
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│ │ └── db.go # Database configuration and initialization
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│ ├── routes/
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│ │ ├── assessment_routes.go # Routes related to assessments
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│ │ ├── classroom_routes.go # Routes related to classrooms
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│ │ ├── doubt_routes.go # Routes related to doubt solving
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│ │ ├── quiz_routes.go # Routes related to quizzes
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│ │ ├── recommendation_routes.go # Routes related to recommendations
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│ │ ├── student_routes.go # Routes related to students
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│ │ └── teacher_routes.go # Routes related to teachers
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│ └── services/
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│ └── ml_service.go # Service interacting with ML-Service repository
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├── go.mod # Go module file
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└── README.md # Project documentation
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```
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## Interaction with ML-Service
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The Go-Backend interacts with the ML-Service repository to leverage machine learning models for educational services. Here is how the interaction works:
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1.**Assessment Routes**: The backend sends student responses to the ML-Service to get assessments and predictions.
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2.**Quiz Routes**: The backend requests personalized quizzes from the ML-Service based on student profiles and learning styles.
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3.**Tutor Routes**: The backend utilizes the ML-Service to provide context-aware tutoring sessions.
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4.**Doubt Routes**: The backend sends student doubts (text and images) to the ML-Service for resolution using multimodal AI agents.
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5.**Recommendation Routes**: The backend requests study routines and learning resources from the ML-Service based on aggregated student data.
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Example of a service file interacting with ML-Service (internal/services/ml_service.go):
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