atropos/environments/community/ufc_prediction_env

UFC Fight Prediction Environment

This environment provides a framework for training and evaluating AI models on UFC fight prediction tasks, with a unique twist: instead of traditional analytical predictions, it generates entertaining fight commentary that can be directly used with Text-to-Speech (TTS) models like DIA. The environment includes two main components: a text-based predictor and an image-based predictor, both designed to create engaging, broadcast-style fight commentary.

Environment Design

Core Components

1. UFC Server (ufc_server.py)

   • Text-based fight prediction environment
   • Generates dynamic, entertaining fight commentary
   • Uses fighter statistics and historical data
   • Outputs TTS-ready commentary with dramatic flair
   • Implements a scoring system for model evaluation

2. UFC Image Environment (ufc_image_env.py)

   • Visual-based fight prediction environment
   • Creates commentary based on fighter appearances
   • Implements multimodal prediction capabilities
   • Generates broadcast-style commentary from visual analysis
   • Includes image processing and base64 encoding utilities

Data Structure

• fighter_stats.csv: Contains detailed statistics for each fighter including:

  • Win/Loss records
  • Physical attributes (height, weight, reach)
  • Performance metrics (strikes per minute, takedown accuracy, etc.)

• large_dataset.csv: Historical fight data including:

  • Fighter matchups
  • Fight outcomes
  • Event information

• fighter_images/: Directory containing fighter profile images

  • Images are stored in JPG format
  • Filenames follow slug format (e.g., "john-smith.jpg")

Motivation

This environment was designed to transform traditional fight prediction into an engaging entertainment experience:

1. Entertainment-First Approach

   • Generates dynamic, broadcast-style fight commentary
   • Creates TTS-ready output for voice synthesis
   • Incorporates dramatic elements and commentator personalities
   • Makes fight prediction more engaging and accessible

2. Statistical Analysis with Style

   • Wraps technical analysis in entertaining commentary
   • Uses fight statistics to inform dramatic storytelling
   • Maintains prediction accuracy while being entertaining
   • Creates a more engaging way to present fight analysis

3. Visual Storytelling

   • Transforms visual analysis into engaging commentary
   • Creates dramatic narratives from fighter appearances
   • Makes technical observations more accessible
   • Generates TTS-compatible descriptions of visual elements

4. Multimodal Entertainment

   • Combines statistical and visual data for rich commentary
   • Creates cohesive narratives from multiple data sources
   • Generates engaging stories that work well with TTS
   • Makes technical analysis more accessible and fun

Usage

1. Install dependencies:

pip install -r requirements.txt

2. Prepare data:

   • Ensure fighter_stats.csv and large_dataset.csv are in the environment directory
   • Place fighter images in the fighter_images/ directory

3. Run the environment:

   • For text-based commentary: Use UFCEnv
   • For image-based commentary: Use UFCImageEnv

4. TTS Integration:

   • The generated commentary is formatted for direct use with TTS models
   • Includes dramatic pauses and emphasis markers
   • Contains natural speech patterns and commentator personalities
   • Ready for voice synthesis with models like DIA

Example Runs

Here are some example runs demonstrating the environment in action:

• Video Demo - Watch the environment in action with real-time commentary generation
• Text-based Prediction Run - Shows the environment generating commentary based on fighter statistics and historical data
• Image-based Prediction Run - Demonstrates the environment creating commentary from visual analysis of fighter appearances

The key difference between these runs is their input modality:

• The text-based run focuses on statistical analysis and historical data to generate commentary
• The image-based run analyzes fighter appearances and visual characteristics to create engaging narratives

Configuration

The environment can be configured through the following parameters:

• fighter_stats_path: Path to fighter statistics CSV
• fight_data_path: Path to fight dataset CSV
• image_folder: Path to fighter images directory
• max_steps: Number of steps per prediction
• temperature: Generation diversity parameter (affects commentary style)
• top_p: Nucleus sampling parameter (affects commentary creativity)

Scoring System

The environment implements a scoring system that evaluates predictions based on:

• Accuracy of winner prediction
• Entertainment value of the commentary
• TTS compatibility and natural flow
• Integration of statistical/visual data in an engaging way
• Proper formatting for voice synthesis

Contributing

Contributions are welcome! Please feel free to submit pull requests for:

• New commentary styles and personalities
• Enhanced TTS compatibility features
• Additional dramatic elements
• Improved entertainment value
• Better integration with voice synthesis models