🎯 AI-Based Review Classifier

📌 Overview

The AI-Based Review Classifier is a machine learning-powered web application designed to classify customer reviews as Positive, Negative, or Neutral. The system integrates a Flask-based AI model with a Node.js backend and supports real-time classification.

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🚀 Features

• ✅ Machine Learning-Based Sentiment Analysis
• ✅ RESTful API for Classification
• ✅ Preprocessing & Model Training Scripts
• ✅ Flask API for AI Model Deployment
• ✅ Secure Node.js Backend
• ✅ Scalable & Modular Architecture
• ✅ Automated Training & Evaluation
• ✅ CI/CD & Deployment Support

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🏗️ System Architecture

User ➝ Frontend ➝ Backend (Node.js) ➝ AI Model (Flask) ➝ Database (MongoDB)

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📂 Project Structure

(for %i in (file1.txt test.js ) do type nul > %i)    #To create multiple files at once
AI-Based-Review-Classifier/
│── 📂 backend/                   # Backend (Node.js & Express)
│   ├── 📂 src/                    # Source Code
│   │   ├── 📂 config/             # Configuration files
│   │   │   ├── db.js              # MongoDB connection
│   │   │   ├── envConfig.js       # Environment variables handler
│   │   ├── 📂 controllers/        # Route controllers
│   │   │   ├── authController.js  # Authentication logic
│   │   │   ├── reviewController.js# Review-related logic
│   │   ├── 📂 middleware/         # Middlewares
│   │   │   ├── authMiddleware.js  # JWT authentication
│   │   │   ├── errorMiddleware.js # Error handling
│   │   ├── 📂 models/             # Mongoose models
│   │   │   ├── User.js            # User schema
│   │   │   ├── Review.js          # Review schema
│   │   ├── 📂 routes/             # API route handlers
│   │   │   ├── authRoutes.js      # Authentication routes
│   │   │   ├── reviewRoutes.js    # Review management routes
│   │   ├── 📂 services/           # Business logic services
│   │   │   ├── authService.js     # User authentication logic
│   │   │   ├── reviewService.js   # Review processing logic
│   │   ├── 📂 utils/              # Utility functions
│   │   │   ├── jwtHelper.js       # JWT token helpers
│   │   │   ├── emailHelper.js     # Email notifications
│   │   │   ├── responseHandler.js # Standardized API responses
│   │   ├── app.js                 # Express app configuration
│   │   ├── server.js              # Main server entry point
│   ├── 📂 tests/                  # Backend tests
│   │   ├── authTests.js           # Tests for authentication
│   │   ├── reviewTests.js         # Tests for review management
│   ├── .env                        # Environment variables
│   ├── .gitignore                  # Ignore unnecessary files
│   ├── Dockerfile                  # Dockerization support
│   ├── package.json                 # Dependencies & scripts
│   ├── package-lock.json            # Lock file for package versions
│   ├── postman_collection.json      # API testing collection
│
│── 📂 ai_model/                  # AI Model (Python & Machine Learning)
│   ├── 📂 data/                    # Dataset storage
│   │   ├── reviews.csv             # Raw dataset
│   │   ├── preprocessed.pkl        # Preprocessed data
│   │   ├── model.pkl               # Trained ML model
│   │   ├── vectorizer.pkl          # TF-IDF vectorizer
│   ├── 📂 training/                 # Training scripts
│   │   ├── preprocess.py           # Data preprocessing
│   │   ├── train_model.py          # Model training & saving
│   │   ├── evaluate.py             # Model evaluation
│   ├── 📂 api/                     # Flask API to expose model
│   │   ├── app.py                  # Flask API for predictions
│   │   ├── model_loader.py         # Model loading utility
│   │   ├── requirements.txt        # Python dependencies
│   ├── 📂 notebooks/               # Jupyter notebooks for experiments
│   │   ├── DataExploration.ipynb   # Dataset exploration
│   │   ├── ModelTesting.ipynb      # Model evaluation
│   ├── 📂 tests/                   # Model testing scripts
│   │   ├── test_predictions.py     # API testing for model predictions
│   │   ├── test_preprocessing.py   # Test data preprocessing steps
│   ├── ai_service.py               # Python client to call Flask API
│
│── 📂 frontend/                  # (Optional) React/Angular Frontend
│   ├── src/                       # Source code
│   │   ├── 📂 components/          # React components
│   │   │   ├── ReviewForm.js       # Review submission form
│   │   │   ├── ReviewList.js       # Review display
│   │   │   ├── Navbar.js           # Navbar UI
│   │   ├── 📂 pages/               # Application pages
│   │   │   ├── HomePage.js         # Home page UI
│   │   │   ├── Dashboard.js        # User dashboard
│   │   ├── 📂 services/            # API call handlers
│   │   │   ├── api.js              # Backend API calls
│   │   ├── App.js                  # Main React component
│   │   ├── index.js                # React app entry point
│   ├── public/                     # Static files (HTML, CSS)
│   ├── package.json                 # Dependencies
│
│── 📂 docs/                      # Documentation
│   ├── API_Documentation.md       # API usage and endpoints
│   ├── AI_Model_Documentation.md  # AI model details
│
└── README.md                      # Project documentation

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🛠️ Tech Stack

Backend (API & Server)

• Node.js, Express.js, MongoDB, Socket.io
• Nodemailer (Email notifications)

AI Model

• Python, Flask, Scikit-Learn, TF-IDF
• Jupyter Notebooks (Experiments)

Frontend (Optional)

• React.js, TailwindCSS

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📡 API Endpoints

1️⃣ AI Model API (Flask)

Method	Endpoint	Description
POST	/predict	Classifies review

2️⃣ Backend API (Node.js)

Method	Endpoint	Description
POST	/api/ai/classify	Classifies user review
GET	/api/reviews	Fetches all classified reviews

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⚙️ Installation & Setup

1️⃣ Clone Repository

git clone https://github.com/yourusername/AI-Based-Review-Classifier.git
cd AI-Based-Review-Classifier

2️⃣ Install Dependencies

Backend (Node.js)

cd backend
npm install

AI Model (Python)

cd ai_model/api
pip install -r requirements.txt

3️⃣ Setup Environment Variables

Create a .env file in the backend/ folder:

MONGO_URI=your_mongo_db_connection_string
PORT=5000

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🚀 Running the Application

1️⃣ Start Backend (Node.js)

cd backend
npm start

2️⃣ Start AI Model API (Flask)

cd ai_model/api
python app.py

3️⃣ Test in Postman

POST http://localhost:5000/api/ai/classify
Body: { "review": "This product is amazing!" }

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🛠️ Deployment

Backend Deployment

• Deploy on Heroku, AWS, or Vercel
• Use PM2 for process management

AI Model Deployment

• Deploy Flask API on Render or Google Cloud
• Use Docker for containerization

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🔐 Security Enhancements

• JWT Authentication
• CORS Handling
• Input Validation

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📊 Monitoring & Visualization

• Grafana & Prometheus for API monitoring
• MLflow for AI model tracking

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🤝 Contributing

We welcome contributions! Follow these steps:

1. Fork the repo
2. Create a new branch (feature-branch)
3. Commit your changes
4. Create a Pull Request

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📜 License

This project is licensed under the MIT License. Feel free to modify and use it!

🚀 Happy Coding! 🎉