- Players receive a word from OpenAI API (with Anthropic API as fallback) and must describe it using a target language.
- AI evaluates the correctness of the description.
- SMS-based interaction using Twilio.
- Scoring system provides feedback on accuracy.
- Low latency response times (~1 second per request).
- Scalable to support multiple concurrent players.
- Secure user authentication for player sessions.
- Python (Backend logic)
- Twilio SMS API (Text-based interactions)
- OpenAI GPT API (Primary API for word generation and NLP scoring)
- Anthropic Claude API (Fallback API for word generation and NLP scoring)
- Django or Flask (API framework)
- PostgreSQL (Storing game data and user scores)
- Set up a Flask/Django backend to handle SMS messages.
- Integrate Twilio SMS API to send and receive game prompts.
- Integrate OpenAI API for word generation and response scoring.
- Store session data and scores in PostgreSQL.
- Deploy to AWS or a cloud provider with scalability considerations.
- Players can speak their descriptions instead of typing.
- Twilio transcribes the response before passing it to the AI evaluator.
- AI provides spoken feedback on correctness.
- Score system adjusts for fluency and pronunciation.
- Real-time speech processing with low delay (~2 seconds per request).
- Support multiple languages with adaptive AI processing.
- Ensure audio data privacy and security.
- Twilio Voice API (Speech-to-text processing)
- Whisper AI (More accurate transcription for AI evaluation)
- FastAPI (Backend service)
- PostgreSQL (Extended schema to store voice logs)
- Extend the backend to support Twilio Voice API.
- Implement Whisper AI for accurate transcriptions.
- Modify AI scoring to handle spoken input with fluency evaluation.
- Deploy real-time voice processing pipeline with AWS Lambda.
- Ensure proper error handling for transcription failures.
- AI assigns scores based on pronunciation accuracy, fluency, and grammar.
- Players receive detailed feedback with improvement tips.
- Leaderboards introduced for competitive play.
- AI must provide feedback in under 3 seconds.
- Scoring model should be adaptive to different accents.
- Data-driven improvement: Store game data for AI model refinements.
- DeepSpeech or Google Speech-to-Text API (Alternative speech recognition)
- TensorFlow/Keras-based AI model (Pronunciation analysis)
- Django Channels or WebSockets (Real-time feedback system)
- Redis (Fast leaderboard management)
- Develop an AI-based scoring engine with fluency analysis.
- Integrate DeepSpeech for improved voice recognition.
- Introduce Redis caching for real-time leaderboard updates.
- Implement WebSockets for instant feedback.
- Continuously train and refine the AI model based on player performance data.
- Players compete in teams to describe and guess words.
- AI moderates the game and ensures fair play.
- Real-time voice chat between team members via Twilio.
- Points are awarded based on time taken and accuracy.
- Synchronization of multiple players in real-time.
- Low-latency voice communication for a seamless experience.
- Scalable architecture to support thousands of concurrent matches.
- WebRTC with Twilio Video API (Real-time communication)
- Celery Task Queue (Handling game synchronization)
- Django Channels (Managing multiplayer game sessions)
- Firebase or DynamoDB (Fast and scalable game state storage)
- Implement WebRTC-based voice communication with Twilio.
- Modify game logic to support multiplayer sessions.
- Develop a queue system for matchmaking.
- Store real-time game states in Firebase for fast access.
- Introduce AI-driven moderation for fair play.
This 4-phase approach ensures that each phase delivers a complete, functional product while allowing incremental improvements. Let me know if you want any refinements!
Here is a breakdown of detailed tasks for each step of the high-level implementation plan for AI Language Charades across all four phases.
- [-] Initialize a new Flask/Django project with a structured directory.
- [-] Configure environment variables for API keys (Twilio, OpenAI GPT, Anthropic Claude, database credentials).
- [-] Implement a stub webhook endpoint for incoming SMS messages.
- [-] Expose the webhook endpoint to Twilio.
- [-] Implement webhook handler for all SMS game interactions:
- [-] Add "LangGang" command processing for player registration
- [-] Add "STOP" command processing for player opt-out
- [-] Add language code processing to start new game sessions
- [-] Add description processing for active game sessions
- [-] Implement game session management using existing models:
- [-] Add player opt-in/opt-out state management using Player model
- [-] Add game session creation and tracking using GameSession model
- [-] Add word assignment and scoring using OpenAI API (with Anthropic API as fallback)
- [-] Establish session management system for tracking player interactions:
- [-] Design database schema for player opt-in/opt-out status
- [-] Design database schema for active game sessions
- [-] Implement player description and AI evaluation storage
- Deploy a basic server instance on AWS/GCP for initial testing.
- [-] Define a scoring rubric based on accuracy, vocabulary, and clarity.
- [-] Implement an OpenAI GPT integration for evaluating player responses.
- [-] Implement an Anthropic Claude integration as fallback for evaluating player responses.
- [-] Develop a function to format and send game queries to the AI.
- [-] Implement logic for interpreting AI responses and assigning scores.
- [-] Store AI feedback and scores in the database.
- [-] Design a PostgreSQL schema for storing player sessions, scores, and game history.
- [-] Implement Django ORM or SQLAlchemy models for database interaction.
- Develop CRUD operations for player and game session management.
- Set up database connection pooling for efficiency.
- Implement automated daily backups for database integrity.
- Choose a cloud provider (AWS, GCP, or DigitalOcean) for deployment.
- Set up an EC2 or equivalent server instance.
- Implement a WSGI server (Gunicorn/Uvicorn) for serving the API.
- Configure load balancing and auto-scaling strategies.
- Implement CI/CD pipeline for automated deployments.
- Enable Twilio Voice in the Twilio console.
- Implement an API endpoint to handle incoming voice calls.
- Develop Twilio XML (TwiML) responses for game prompts.
- Implement logic to detect if a player wants to play via voice or SMS.
- Store voice session metadata in the database.
- Set up Whisper AI API integration.
- Develop a function to send recorded speech data to Whisper.
- Parse Whisper transcription results and format them for AI evaluation.
- Implement error handling for failed or unclear transcriptions.
- Compare Whisper AI performance with Twilio’s built-in transcription.
- Expand the scoring rubric to include fluency and pronunciation.
- Train the AI to recognize common errors in spoken language.
- Implement a phonetic similarity scoring system.
- Store transcription confidence levels in the database.
- Develop a feature for users to receive detailed feedback on pronunciation.
- Develop an AWS Lambda function for processing voice data.
- Optimize data flow between Twilio, Whisper AI, and the backend.
- Implement real-time feedback for spoken responses.
- Test latency and optimize response times.
- Implement a monitoring system to track voice processing performance.
- Implement fallback mechanisms for failed transcriptions.
- Develop logic to prompt players to retry unclear responses.
- Set up logging and alerts for voice processing failures.
- Test various network conditions to handle edge cases.
- Provide user-friendly error messages for better experience.
- Implement an AI model that scores pronunciation, fluency, and grammar.
- Train the model using labeled datasets from diverse speakers.
- Define weightage for accuracy vs. fluency in the scoring algorithm.
- Test the AI model with real user data for optimization.
- Integrate the scoring engine into the backend.
- Set up DeepSpeech API and pre-trained models.
- Develop a pipeline for processing voice data with DeepSpeech.
- Compare DeepSpeech results with Whisper AI for accuracy.
- Implement user-specific speech adaptation for better recognition.
- Store processed voice data securely for further training.
- Design a Redis-based caching layer for score storage.
- Implement API endpoints for fetching real-time leaderboard data.
- Optimize queries for low-latency leaderboard updates.
- Develop a scheduled task for updating long-term rankings.
- Test Redis performance under high traffic conditions.
- Set up Django Channels for WebSockets support.
- Implement real-time AI feedback via WebSocket connections.
- Develop a frontend client to display instant feedback.
- Optimize WebSocket traffic handling for scalability.
- Ensure security and authentication for WebSocket connections.
- Implement logging for AI decision-making.
- Develop a system to flag incorrect AI evaluations for manual review.
- Allow players to report incorrect feedback for model improvement.
- Retrain AI model periodically using collected data.
- Deploy updated models seamlessly with minimal downtime.
- Enable Twilio Video API for real-time communication.
- Develop UI/UX for in-game voice chat.
- Implement WebRTC-based real-time audio streaming.
- Optimize audio quality for different bandwidth conditions.
- Store session logs for moderation and review.
- Develop game session structures for multiple players.
- Implement team-based gameplay logic.
- Design a fair scoring system for team-based rounds.
- Store team-based match data in the database.
- Implement APIs for inviting and joining team games.
- Implement a matchmaking queue with Redis or Firebase.
- Develop an API to allow players to join existing matches.
- Implement ranking-based matchmaking for fair competition.
- Ensure low-latency updates for matchmaking status.
- Develop fallback mechanisms for failed matches.
- Choose Firebase or DynamoDB for real-time state management.
- Implement data structures for tracking game progress.
- Ensure state persistence across network disruptions.
- Develop APIs for retrieving live game state updates.
- Optimize Firebase queries for high-speed access.
- Implement AI-powered speech monitoring for cheating detection.
- Develop rules for detecting inappropriate language usage.
- Implement automatic penalties for rule violations.
- Allow human moderation override for AI decisions.
- Provide transparency reports for flagged actions.
This structured plan ensures that each step is actionable, leading to a seamless execution of AI Language Charades from text-based gameplay to a full multiplayer experience. 🚀