Blog.AI

LLM using API tokens

Blog.AI: AI-Powered Blog Generator 🚀

Overview 📖

Blog.AI is a Streamlit-based AI blog generator that utilizes Google Gemini AI for text generation and Stable Diffusion for AI-generated images. It allows users to create unique, creative, and structured blog posts with AI-generated images based on their inputs.

Features ✨

• 📝 AI-Powered Blog Writing using Google Gemini AI
• 🎨 AI-Generated Images using Stable Diffusion
• 📥 Download Generated Blog as a text file
• 🖥️ Simple & Interactive UI using Streamlit

Folder Structure 📂

.
├── .venv/                  # Virtual environment (ignored in Git)
├── generated_images/       # Folder where generated images are stored (creates when the application script is run and images generated for the blogs are saved here)
├── app.py                  # Main Streamlit application script
├── diffusion_pytorch_model.safetensors  # Stable Diffusion model file
├── README.md               # Project documentation (this file)
└── requirements.txt        # Python dependencies

Installation 🛠️

1️⃣ Clone the Repository

git clone https://github.com/Opikadash/blog-ai.git
cd blog-ai

2️⃣ Create a Virtual Environment

python -m venv .venv
source .venv/bin/activate  # On macOS/Linux
.venv\Scripts\activate     # On Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Set Up API Keys

Before running the application, set the required API keys as environment variables:

export GOOGLE_GEMINI_API_KEY="your_google_gemini_api_key"
export HUGGINGFACE_API_KEY="your_huggingface_api_key"

On Windows (PowerShell):

$env:GOOGLE_GEMINI_API_KEY="your_google_gemini_api_key"
$env:HUGGINGFACE_API_KEY="your_huggingface_api_key"

Usage 🚀

Run the Streamlit app:

streamlit run app.py

After running the command, open http://localhost:8501/ in your browser.

Deploying on Cloud GPU ☁️

If you want to run Blog.AI on a Cloud GPU (Google Colab, RunPod, Paperspace, Lambda Labs):

1. Upload app.py to your cloud instance.
2. Install dependencies using pip install -r requirements.txt
3. Make sure CUDA is available (torch.cuda.is_available() should return True).
4. Run the app using streamlit run app.py --server.port 8501

Contributing 🤝

Feel free to open issues and pull requests to improve this project!

License 📝

This project is licensed under the MIT License.

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