GITHUB_INSTRUCTIONS.md

🚀 GitHub Setup Instructions

Steps to Publish Your Repository

1. Create a new repository on GitHub:

   • Go to https://github.com/new
   • Repository name: togmal-prompt-analyzer (or any name you prefer)
   • Description: "Real-time LLM capability boundary detection using vector similarity"
   • Public repository
   • Do NOT initialize with README
   • Click "Create repository"

2. Push your local repository to GitHub:

   cd /Users/hetalksinmaths/togmal
   git remote add origin https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
   git branch -M main
   git push -u origin main
   

3. Replace YOUR_USERNAME and YOUR_REPO_NAME with your actual GitHub username and repository name.

What's Included in This Commit

• benchmark_vector_db.py: Core vector database implementation
• demo_app.py: Gradio web interface for prompt analysis
• COMPLETE_DEMO_ANALYSIS.md: Comprehensive analysis of the system
• DEMO_README.md: Documentation with results and instructions
• requirements.txt: Python dependencies
• .gitignore: Excludes large data files and virtual environment
• test_vector_db.py: Test script with real data examples

Live Demo

Your demo is currently running at:

• Local: http://127.0.0.1:7861
• Public: https://db11ee71660c8a3319.gradio.live

Key Features

• 14,042 real MMLU questions with actual success rates
• Real-time difficulty assessment (<50ms queries)
• Production-ready vector database
• Explainable results (shows similar benchmark questions)
• Actionable recommendations based on difficulty

Analysis of Test Questions

The system correctly differentiates between:

• Hard prompts (23.9% success rate) like "Statement 1 | Every field is also a ring..."
• Easy prompts (100% success rate) like "What is 2 + 2?"

Next Steps After Pushing

1. Add more benchmark datasets (GPQA Diamond, MATH)
2. Fetch real per-question results from multiple top models
3. Integrate with ToGMAL MCP server for Claude Desktop
4. Deploy to HuggingFace Spaces for permanent hosting