arindam200--awesome-ai-apps/.github/README_TEMPLATE.md

Project Name

A brief, one-sentence description of what this project does and who it's for.

An advanced AI-powered agent that does [X, Y, and Z]. Built with [mention key technologies like CrewAI, Langchain, etc.].

🚀 Features

• Feature 1: Description of the feature.
• Feature 2: Description of the feature.
• Feature 3: Description of the feature.
• User-Friendly Dashboard: Built with Streamlit for easy interaction.

🛠️ Tech Stack

• Python: Core programming language
• [Framework e.g., Streamlit, FastAPI]: For the web interface/API
• [AI Library e.g., ScrapeGraph AI, CrewAI]: For AI-powered workflows
• [LLM Provider e.g., Nebius AI, OpenAI]: For language model access
• [Other tools e.g., Twilio, APScheduler]: For notifications, scheduling, etc.
• [Database e.g., JSON, Vector DB]: For data storage

Workflow

A brief explanation of how the project works, from input to output.

📦 Getting Started

Prerequisites

• Python 3.9+
• uv or pip for package management
• API keys for the following services:
  • Service 1 (e.g., Nebius AI)
  • Service 2 (e.g., Bright Data)

Environment Variables

Create a .env or api.env file in the project root and add the following variables. Refer to the specific project's documentation for the exact file name and variables required.

SERVICE1_API_KEY="your_service1_api_key"
SERVICE2_API_KEY="your_service2_api_key"

Note: Ensure that any phone numbers or client IDs are correctly formatted and registered with their respective services.

Installation

1. Clone the repository:

   git clone https://github.com/Arindam200/awesome-llm-apps.git
   cd awesome-llm-apps/[project_directory]
   

2. Create and activate a virtual environment:

   • Using venv:

     python -m venv .venv
     source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
     

3. Install dependencies:

   • Using uv (recommended):

     uv sync
     

   • Using pip:

     pip install -r requirements.txt
     

⚙️ Usage

1. Run the application:

   streamlit run app.py
   

   or for background services:

   python main.py
   

2. Open your browser to http://localhost:8501 (or as indicated by the application).

3. Follow the on-screen instructions, such as providing an API key or input URL.

📂 Project Structure

A standardized project structure is recommended for clarity and maintainability.

project_name/
├── agents/               # AI agent definitions
├── assets/               # Static assets (images, GIFs)
├── tools/                # Custom tools for agents
├── .venv/                # Virtual environment
├── .env                  # Environment variables
├── app.py                # Main application file (e.g., Streamlit UI)
├── main.py               # Core logic or service entry point
├── requirements.txt      # Python dependencies
└── README.md             # Project-specific README

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. See the CONTRIBUTING.md for more details.

📄 License

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

🙏 Acknowledgments

• Shoutout to Library/Framework for their amazing work.
• Inspired by Project/Article.