🤖 AI Media Generator

A full-stack AI-powered media generation web app built using React (Vite) for the frontend and Flask + Diffusers for the backend. Users can generate images and videos from text and image prompts using state-of-the-art AI models.

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🌐 Live Demo

🔗 https://yashdhanani.github.io/ai-media-project/

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🧠 Features

Tool	Description
🎨 Text-to-Image (T2I)	Create stunning images from text descriptions
🎥 Text-to-Video (T2V)	Generate video clips from a simple text prompt (planned)
🎞️ Image-to-Video (I2V)	Animate a static image to create a short video (planned)
🎵 Image-to-Video + Audio (I2V+A)	Animate an image and add a soundtrack (planned)
✨ Text + Image to Video (T+I2V)	Use a prompt to guide the animation of an image (planned)
🔊 Text + Image to Video + Audio (T+I2V+A)	Guide the animation and add a narrated soundtrack (planned)

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🖼️ Text-to-Image Backend (Python)

The Python backend uses 🤗 Hugging Face's diffusers library with Stable Diffusion v1.5. It supports both CPU and GPU execution.

📂 Backend Directory: python-server/

📦 Dependencies

Install with:

pip install -r requirements.txt
requirements.txt:

nginx
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Flask
Flask-Cors
torch
diffusers
transformers
accelerate
safetensors
🚀 Run the Flask Server
bash
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cd python-server
python app.py
The server will start on http://localhost:5000

🔄 API Endpoint
POST /api/text-to-image

json
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{
  "prompt": "a cyberpunk city at night with glowing lights"
}
Returns: image/png (generated AI image)

⚙️ Frontend (React + Vite)
📂 Frontend Directory: client/
📦 Setup
bash
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cd client
npm install
🚀 Run Locally
bash
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npm run dev
The frontend will start on http://localhost:5173

Make sure the backend is also running at http://localhost:5000 or change the API URL in your React app.

🌐 GitHub Pages Deployment
Vite is configured to deploy to GitHub Pages with:

js
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// vite.config.js
export default defineConfig({
  base: '/ai-media-project/',
});
📦 Deployment via GitHub Actions
.github/workflows/deploy.yml handles automatic CI/CD:

Builds React app

Uploads artifact

Deploys to GitHub Pages

🗂️ Project Structure
csharp
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ai-media-project/
├── client/                   # Frontend (React + Vite)
│   ├── public/
│   ├── src/
│   └── vite.config.js
│
├── python-server/            # Backend (Flask API)
│   ├── app.py
│   └── requirements.txt
│
├── .github/
│   └── workflows/
│       └── deploy.yml
│
└── README.md
🔐 Environment Notes
Backend supports CPU by default, but will use GPU if available (torch.cuda.is_available()).

Uses the Hugging Face model: runwayml/stable-diffusion-v1-5.

Port configuration: Flask runs on 5000, Vite runs on 5173.

🎯 Roadmap
✅ Text-to-Image (T2I) working via Flask + Diffusers

🚧 Add other AI tools (T2V, I2V, etc.)

🚧 Upload image/audio support

🚧 Media download button

🚧 User account and project save

🧑‍💻 Developed By
Yash Dhanani

🔗 GitHub

💼 Founder of AppyGrowth

🧠 AI, Web & Full-Stack Developer

📄 License
© 2025 Yash Dhanani. All Rights Reserved.

This project is intended for learning, experimentation, and demo purposes only.

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