OnAir.ai Podcast Platform

1. 🤖 Introduction
2. ⚙️ Tech Stack
3. 🔋 Features
4. 📸 Screenshots
5. 🤸 Getting Started
6. 🐳 Docker Instructions
7. 📄 License
8. 📞 Contact

A cutting-edge AI SaaS platform that enables users to create, discover, and enjoy podcasts with advanced features like text-to-audio conversion with multi-voice AI, podcast thumbnail Image generation and seamless playback. The platform is built using Next.js, TypeScript, Convex, OpenAI, Clerk, ShadCN, and Tailwind CSS. It offers a modern home page, podcast discovery page, search functionality, podcast creation page, profile page, podcast details page, and a responsive design for a seamless user experience.

• Next.js
• TypeScript
• Convex
• OpenAI
• Stripe (for payments)
• Clerk
• ShadCN
• Tailwind CSS
• Zod (for validation)

👉 Advanced Authentication: Offers secure and dependable user login and registration functionalities.

👉 Contemporary Home Page: Highlights trending podcasts with a persistent player for uninterrupted listening.

👉 Podcast Discovery Page: A dedicated section for users to explore a wide range of new and popular podcasts.

👉 Comprehensive Search Functionality: Empowers users to effortlessly find podcasts using various filters and criteria.

👉 Podcast Creation Page: Facilitates podcast creation with text-to-speech conversion, AI-generated images, and preview options.

👉 Multi-Voice AI Capability: Provides multiple AI-generated voice options for creating dynamic and engaging podcasts.

👉 User Profile Page: Allows users to view and manage their created podcasts, including options to delete them.

👉 Detailed Podcast Page: Presents in-depth information about each podcast, including creator details, listener statistics, and transcripts.

👉 Enhanced Podcast Player: Includes features like rewind/fast forward controls and mute/unmute functionality for an optimal listening experience.

👉 Responsive Design: Ensures the platform looks great and works seamlessly on all devices and screen sizes.

and many more, including code architecture and reusability

Homepage
Discover Page
Create Podcast Page
Podcast Details Page
Profile Page
Plans Page
Payment / Stripe Checkout Page
Usage Page

Follow these steps to set up the project locally on your machine.

Prerequisites

Make sure you have the following installed on your machine:

• Git
• Node.js
• npm (Node Package Manager)

Cloning the Repository

git clone https://github.com/deepsingh132/aionair.git
cd aionair

Installation

Install the project dependencies using npm:

npm install

Set Up Environment Variables

Create a new file named .env in the root of your project and add the following content to your local env file and on the convex dashboard:

CONVEX_DEPLOYMENT=
NEXT_PUBLIC_CONVEX_URL=
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=
CLERK_SECRET_KEY=
NEXT_PUBLIC_CLERK_SIGN_IN_URL='/sign-in'
NEXT_PUBLIC_CLERK_SIGN_UP_URL='/sign-up'
OPENAI_API_KEY=
NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY=
STRIPE_SECRET_KEY=

Replace the placeholder values with your actual Convex & Clerk credentials. You can obtain these credentials by signing up on the Convex, Clerk, OpenAI and Stripe websites.

Running the Project

npm run dev

Open http://localhost:3000 in your browser to view the project.

Testing Stripe Payments

To test the Stripe payments/subscriptions functionality, you can use the following test card details:

• Card Number: 4242 4242 4242 4242
• Expiry Date: Any future date
• CVC/CVV: Any 3-digit number
• Cardholder Name: Any name
• Address: Any address (e.g., 123 Main Street)
• ZIP Code: Any 5-digit number

You can also use the Stripe Test Cards for more test card details.

You can run this project using Docker with the latest image from Docker Hub. Make sure you have Docker installed and running on your machine. Follow these steps:

1. Pull the latest image from Docker Hub.

   docker pull deepsingh132/aionair:latest

2. Run the Docker image with the following command:

   docker run --env-file .YOURENVFILE -p 3000:3000 deepsingh132/aionair:latest

3. Open http://localhost:3000 in your browser to view the project.

This project is licensed under the MIT License - see the LICENSE file for details.

If you have any queries or feedback, please feel free to reach out to me at my email or connect with me on LinkedIn.