gemini_warrior
Team led by a NYU-trained Data Engineer (The Warehouse Group) skilled in PySpark, Databricks, Kafka, Airflow, LangChain, vector DBs, and GPU‑deployed agentic RAG AI.
YouTube Video
Project Description
ASLGEMINI is an AI-powered sign language translation system that bridges communication gaps between Deaf and hearing communities. Using Google’s Gemini CLI, it converts English text → authentic American Sign Language (ASL) with cultural accuracy, hand positions, movements, and timing.
Unlike most translation systems, ASLGEMINI prioritizes cultural authenticity, offline-first processing, and educational impact, making it both practical and socially meaningful.
🎯 Goals
Accessibility: Democratize access to ASL communication and learning.
Cultural Accuracy: Ensure translations reflect real ASL grammar, timing, and cultural context.
Local-First Processing: Function offline with fallback systems (important for hackathon reproducibility).
Education: Help hearing individuals learn ASL via AI-generated, real-time sign demonstrations.
🏗️ Architecture
- Gemini CLI Integration (src/gemini_integration.py)
Text pre-processing → enhance English input for ASL translation.
Calls Gemini CLI for translation + quality assessment.
Returns structured outputs (JSON/Markdown).
- Real Sign Language Engine (src/real_sign_language.py)
Lexicon of ASL gestures: handshapes, palm orientation, movements, timing.
Fallback system if Gemini unavailable (ensures hackathon demo won’t break).
Adds cultural notes per sign (context matters in ASL).
- Web Interface (src/clean_web_app.py)
Built with Streamlit → clean and interactive.
Real-time ASL demonstration (animations or gesture sequences).
Displays translation quality score + cultural notes.
- File Processing System (process_files.py)
Batch text → ASL JSON/Markdown outputs.
Input: ASL_input/ → Output: ASL_output/ with logs.
Perfect for hackathon reproducibility and demo videos.
🔄 Core User Flows
Interactive Web Demo
User enters text: “Hello, how are you?”
System → Gemini CLI → ASL engine → cultural context.
Streamlit UI:
ASL hand movements & timing
Quality & cultural notes
File Processing Demo
User drops text files in ASL_input/.
Run: python process_files.py.
Output → structured ASL JSON + processing logs in ASL_output/.
🚀 Hackathon Execution Roadmap (Step-by-Step)
Step 1: Setup Environment
git clone https://github.com/ritzzi23/gemini_warrior
cd gemini_warrior
pip install -r requirements.txt
Configure .env with Gemini API key.
Step 2: Run Interactive Web Demo
streamlit run src/clean_web_app.py –server.port 8502
Open: http://localhost:8502
Test with sample phrases.
Step 3: File Processing Demo
python process_files.py
Check outputs in ASL_output/.
Verify logs & JSON structure.
Step 4: Hackathon Demo Plan
Show Web Demo (Flow 1): Live input → ASL translation → sign demo.
Show File Processing (Flow 2): Prove reproducibility with batch input.
Highlight Innovation: Local-first, fallback engine, cultural accuracy.
End with Impact: Accessibility + education.
📊 Judging Criteria Alignment
–Technical Excellence (20%) – Robust fallback, reproducible logs, offline-capable.
–Architecture & Docs (20%) – Clean repo, sample outputs, structured README.
–Gemini Integration (30%) – Multi-stage pipeline, deep CLI usage.
–Impact & Innovation (30%) – Accessibility, cultural authenticity, educational value.
🎬 Demo Links
Video : https://youtu.be/2lChYACoa4Y?si=B8NnzlCft-xD5p1p
Web Interface: http://localhost:8502
GitHub Repo: https://github.com/ritzzi23/gemini_warrior