HUGGINGFACE_DEPLOYMENT.md

🚀 HuggingFace Space Deployment Guide

Status: Ready to Push

Your ToGMAL Prompt Difficulty Analyzer is set up and ready to deploy to HuggingFace Spaces!

What's Been Done

✅ Repository Cloned: Togmal-demo from HuggingFace Spaces ✅ Files Copied:

• app.py - Main Gradio demo application
• benchmark_vector_db.py - Vector database implementation
• data/ - Complete vector database with 14,042 benchmark questions
• requirements.txt - All necessary dependencies

✅ README Updated: Professional description with features and usage ✅ Changes Committed: All files staged and committed

📝 Next Step: Push to HuggingFace

The code is committed and ready. To push, run:

cd /Users/hetalksinmaths/togmal/Togmal-demo
git push -u origin main

You'll be prompted for credentials:

• Username: JustTheStatsHuman
• Password: Use your HuggingFace Access Token (not your account password!)

Generate Access Token

If you don't have a token yet:

1. Go to: https://huggingface.co/settings/tokens
2. Click "New token"
3. Give it write permissions
4. Copy the token
5. Paste it when git asks for password

🎯 What Will Happen After Push

1. HuggingFace will automatically detect requirements.txt
2. Install all dependencies (gradio, sentence-transformers, chromadb, etc.)
3. Start the Gradio app from app.py
4. Your space will be live at: https://huggingface.co/spaces/JustTheStatsHuman/Togmal-demo

📦 Files Included

Togmal-demo/
├── app.py                          # Main Gradio interface
├── benchmark_vector_db.py          # Vector database class
├── requirements.txt                # Python dependencies
├── README.md                       # HuggingFace Space description
└── data/
    ├── benchmark_vector_db/        # ChromaDB persistent storage (14,042 questions)
    └── benchmark_results/          # Real benchmark success rates

🔧 Features in Your Space

• Real-time Analysis: Users can enter any prompt
• Vector Similarity Search: Finds 5 most similar benchmark questions
• Success Rate Prediction: Shows how well LLMs perform on similar questions
• Risk Assessment: LOW/MODERATE/HIGH/CRITICAL difficulty levels
• Smart Recommendations: Actionable suggestions based on difficulty
• Example Prompts: Pre-loaded examples to try

🎨 Space Configuration

From README.md frontmatter:

• SDK: Gradio 5.42.0
• Emoji: 🧠
• Color: Yellow to Purple gradient
• License: Apache 2.0
• Description: Prompt difficulty predictor using vector similarity

🐛 Troubleshooting

If the space fails to build:

1. Check Build Logs: HuggingFace will show detailed error logs

2. Common Issues:

   • Large file size: The vector DB is ~10MB, should be fine
   • Missing dependencies: All listed in requirements.txt
   • Python version: HuggingFace uses Python 3.10+ by default

3. Test Locally First:

   cd /Users/hetalksinmaths/togmal/Togmal-demo
   source ../.venv/bin/activate
   python app.py
   

📊 Database Stats

Your space includes:

• Total Questions: 14,042 benchmark questions
• Sources: MMLU (13,900), MMLU-Pro (100), GPQA (36), MATH (6)
• Domains: 57 different domains (mathematics, physics, medicine, law, etc.)
• Success Rates: Real performance data from Claude, GPT-4, Gemini

🔗 Related Links

• Your Space: https://huggingface.co/spaces/JustTheStatsHuman/Togmal-demo
• GitHub Repo: https://github.com/HeTalksInMaths/togmal-mcp
• Token Settings: https://huggingface.co/settings/tokens

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Ready to deploy! Just run the push command and enter your access token when prompted. 🚀