https://github.com/datachefhq/ai-roaster
AI Roaster is an AI-powered roasting platform that generates witty and hilarious 'roasts' for teammates based on their unique characteristics
https://github.com/datachefhq/ai-roaster
Last synced: 4 months ago
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AI Roaster is an AI-powered roasting platform that generates witty and hilarious 'roasts' for teammates based on their unique characteristics
- Host: GitHub
- URL: https://github.com/datachefhq/ai-roaster
- Owner: DataChefHQ
- Created: 2024-12-17T08:55:41.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-12-17T12:00:05.000Z (over 1 year ago)
- Last Synced: 2025-10-06T01:37:18.860Z (9 months ago)
- Language: Python
- Homepage: https://chefroaster.ai/
- Size: 10.3 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
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README
# AI Roaster 😈
AI Roaster is an AI-powered roasting platform that generates witty and hilarious "roasts" for team members based on
their unique characteristics. This project combines a serverless backend with dynamic AI processing, voice-based
interactions, and customizable roast outputs.
## How We Roasted Our Teammates 😂
[](https://links.datachef.co/happy-2025-video)
## Features
- 🌶 **Funny and Personalized Roasts**: Create tailored and hilarious roasts for team members using their characteristics
and data.
- 🔥 **Dynamic Roast Image Generation**: Generate roast-themed images unique to each team member.
- 🌐 **Web Application**: A polished web interface for users to interact with the roasting experience.
- 🔗 **Unique URLs for Roasts**: Share roasts easily using one-click URLs.
- 🎙 **Voice Output Support**: Listen to your roast via Text-to-Speech (TTS) audio.
**And it’s totally SERVERLESS! 😎**
## Tech Stack
- **Backend**: Python (Flask), AWS Lambda
- **Frontend**: HTML, CSS, JavaScript
- **Serverless Framework**: AWS infrastructure setup and deployment
- **Dependencies**:
- Flask, Werkzeug, OpenAI API
- Boto3 for AWS interactions
## Getting Started
### Prerequisites
1. You will need the following tools:
- Python 3.10 or later
- Node.js for Serverless Framework integration
- AWS CLI configured with the required permissions
2. **Create a Bucket to store assets**:
- Create an S3 bucket in your AWS account to store the roast images and other assets.
- Update the `ASSET_BUCKET` value in the `.env` file with the bucket name.
3. **Create a Deployment Role**:
The GitHub workflow uses [GitHub AWS OIDC](https://aws.amazon.com/blogs/security/use-iam-roles-to-connect-github-actions-to-actions-in-aws/).
- Create an IAM role in your AWS account for GitHub Actions.
- Update the `GITHUB_CI_ROLE` value in the `.env` file (GitHub Actions will use this role to deploy the project).
4. **Set Up the OpenAI API Key**:
- Generate an OpenAI API Key.
- Store the key in AWS Secrets Manager.
- Update the `OPENAI_KEY_SECRET_NAME` in the `.env` file with the key's secret name.
5. **Set Other Environment Variables**:
- Update `ASSET_BUCKET`, `AWS_ACCOUNT` and `REGION` in the `.env` file.
### Training data
To bring the roasting experience to life, you need to provide descriptions and images for your teammates. Follow the
steps below to populate the database:
1. **Create Roast Descriptions**:
- Provide a detailed description of the person, including quirky traits, unique experiences, and anything that can
serve as roast material—bonus points if you include pre-written roasts!
- Save each roast in a separate `.txt` file within the `src/roasts` directory.
- Use clear, descriptive file names (e.g., `john_doe.txt`).
2. **Add Corresponding Images**:
- For each teammate, provide their image.
- Save the images in the `src/static/images/team` directory.
- Use the same naming convention as the `.txt` files (e.g., `john_doe.png` or `john_doe.jpg`).
### Running Locally
1. **Set Up the Environment**:
```bash
pip install poetry
poetry install
```
2. **Configure environment variables**: Ensure your `.env` file is set up correctly and has the correct values.
3. **Start the Server**:
```bash
python -m src.main
```
4. **Open the application in your browser at**:
```
http://127.0.0.1:5000/
```
## Deployment
The project uses the Serverless Framework for deployment on AWS Lambda, with GitHub Actions for deploying to AWS.
To deploy:
- Commit your changes to the `master` branch.
- The GitHub Actions pipeline will deploy to AWS and host your website.
## Project Structure
```
Ai-Roaster/
├── src/
│ ├── main.py # Flask app and API handlers
│ ├── bedrock.py # Core AI logic for finding team members and roasting
│ ├── roasts/ # Roast content files
│ ├── static/ # Frontend assets (CSS, JS, images)
│ ├── templates/ # HTML templates for the web app
├── serverless.yml # Serverless configuration
├── pyproject.toml # Python dependencies (Poetry)
├── package.json # Node.js dependencies
```
## Contributing
1. Fork the repository and create a branch for your feature.
2. Submit a pull request with detailed changes and test cases.
## License
This project is licensed under the MIT License.