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🤖 My Siri AI

Full Stack AI-Powered Text Assistant 💬 Built using Modern Web Technologies 🚀


📖 About The Project

My Siri AI is a full-stack AI-based text assistant application.
Users can send text messages through a chat interface and receive intelligent AI-generated responses.

This project is structured into two main parts:

  • 🖥️ Frontend (Client)
  • ⚙️ Backend (Server)

🏗️ Project Structure

My-Siri-AI/
│
├── client/        # Frontend (React Application)
├── server/        # Backend (Node.js + Express API)
└── .gitignore

🖥️ Frontend - Client

📂 Location:

/client

🔹 Technologies Used

  • React.js
  • HTML5
  • CSS3
  • JavaScript (ES6+)
  • Axios
  • React Hooks

🔹 Features

  • 💬 Chat-Based Interface
  • ⚡ Real-Time Message Display
  • 🔗 API Integration with Backend
  • 📱 Responsive Design
  • 🧩 Clean UI Layout

▶️ How To Run Frontend

cd client
npm install
npm start

Runs at:

http://localhost:3000

⚙️ Backend - Server

📂 Location:

/server

🔹 Technologies Used

  • Node.js
  • Express.js
  • MySQL (Railway Database)
  • REST APIs
  • dotenv (Environment Variables)

🔹 Features

  • 🔐 Secure API Handling
  • 🧠 AI Text Response Processing
  • 🗄️ Database Connectivity
  • 🌐 CORS Configuration
  • ⚙️ Environment-Based Configuration

▶️ How To Run Backend

cd server
npm install
node index.js

Runs at:

http://localhost:5000

🔑 Environment Variables Setup

Create a .env file inside the server folder and add:

MYSQL_URL=your_railway_mysql_url
PORT=5000

⚠️ Do NOT upload your .env file to GitHub.


🌍 Deployment

Service Platform
Frontend Vercel / Netlify
Backend Railway
Database Railway MySQL

🚀 Future Improvements

  • 🧠 Improved AI logic
  • 💾 Chat history storage
  • 🔐 User authentication
  • 🌙 Dark / Light mode toggle
  • 📊 Admin monitoring dashboard

👨‍💻 Author

Mahesh Peetla
Full Stack Developer 💻
Passionate about AI & Web Technologies 🚀


⭐ Support

If you like this project, give it a ⭐ on GitHub!

About

AI Chat Assistant is a full-stack intelligent chatbot application built using React and Node.js. It integrates modern Large Language Model (LLM) APIs to provide real-time, structured, and detailed responses to user queries through an interactive and responsive chat interface.

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