Hugging Face's logo

Spaces:
Vignesh19
/
TB-Guard-XAI-v3
Runtime error

App Files Community
TB-Guard-XAI-v3 / README_GUIDES.md
Vignesh19's picture
Vignesh19
Upload README_GUIDES.md with huggingface_hub
504c48a verified
preview code
|
Raw
History Blame Contribute Delete
11.2 kB

TB-Guard-XAI: Complete Documentation Suite

This folder contains three comprehensive guides to help you replicate, understand, and deploy TB-Guard-XAI from scratch.

📚 Guide Overview

1. QUICK_START_5_MINUTES.md ⚡

For: People who want to run it NOW

  • ⏱️ 5-minute setup - Get running immediately
  • 🚀 Live demo link - No installation required
  • 🔧 Troubleshooting - Common issues and fixes
  • 📋 Next steps - What to do after setup

Start here if: You just want to test the application quickly

Read time: 5 minutes

2. COMPLETE_REPRODUCTION_GUIDE.md 📖

For: People who want to replicate the ENTIRE project

  • 🏗️ Full architecture explanation - How everything fits together
  • 📊 Dataset sources with links - Exactly where to download data
  • 💻 Complete environment setup - Step-by-step configuration
  • 🔧 All implementation code - Key components included
  • 📈 Training instructions - How to train models from scratch
  • 🌐 Deployment options - Local, Docker, Cloud, Hugging Face
  • ✅ Checklist - Verify everything works

Sections included:

  1. Project Overview
  2. Architecture & Technology Stack
  3. Dataset Information & Sources
  4. Environment Setup (7 steps)
  5. Complete Implementation (Configuration, Schemas, Models, etc.)
  6. Model Training
  7. Backend Implementation
  8. Frontend Implementation
  9. Testing & Validation
  10. Deployment Guide
  11. Troubleshooting
  12. Performance Metrics
  13. Compliance & Regulatory

Start here if: You want to understand and replicate the complete project

Read time: 45 minutes

Use it as: Reference guide during implementation

3. IMPLEMENTATION_DETAILS.md 🔍

For: Developers who want LINE-BY-LINE code details

  • 📝 Complete code implementations - Every important function
  • 🏢 Project structure - File organization explained
  • 🔧 Configuration system - How settings work
  • 🧠 Model architecture - Ensemble implementation
  • 📷 Preprocessing pipeline - Image processing steps
  • 🎨 Grad-CAM++ implementation - Visual explanation code
  • ⚙️ FastAPI backend - Route definitions
  • 🗄️ Database/Storage - Audit logging, rate limiting
  • 🧪 Testing framework - Test examples
  • 🚀 Running the app - All execution options

Start here if: You need to understand the exact implementation

Read time: 60 minutes

Use it as: Code reference while developing

🎯 How to Use These Guides

Scenario 1: "I just want to try it"

  1. Read: QUICK_START_5_MINUTES.md (5 min)
  2. Follow 5-step setup
  3. Done! You're running it

Scenario 2: "I want to build it from scratch"

  1. Read: COMPLETE_REPRODUCTION_GUIDE.md Overview section (10 min)
  2. Follow Environment Setup steps (15 min)
  3. Download datasets (30 min - passive download)
  4. Follow Implementation steps (60 min)
  5. Train model or use pre-trained (varies)
  6. Deploy using Deployment Guide (10 min)
  7. Test using Testing section (10 min)

Total time: 2-4 hours (depending on dataset download speed)

Scenario 3: "I need to modify/understand the code"

  1. Read: IMPLEMENTATION_DETAILS.md Overview section (10 min)
  2. Jump to specific component you need
  3. Read code + explanation
  4. Modify as needed

Scenario 4: "I'm deploying to production"

  1. Read: COMPLETE_REPRODUCTION_GUIDE.md → Deployment Guide (10 min)
  2. Follow deployment option (AWS/GCP/Azure/Docker/Hugging Face)
  3. Configure API keys and environment
  4. Run health checks
  5. Monitor using Performance Metrics section

📊 Quick Reference

Question Answer Read
How do I run it? Follow 5-minute setup QUICK_START_5_MINUTES.md
Where are the datasets? See Dataset Sources section COMPLETE_REPRODUCTION_GUIDE.md
How does the model work? See Architecture section COMPLETE_REPRODUCTION_GUIDE.md
What's the exact code? See IMPLEMENTATION_DETAILS.md IMPLEMENTATION_DETAILS.md
How do I train? See Model Training section COMPLETE_REPRODUCTION_GUIDE.md
How do I deploy? See Deployment Guide section COMPLETE_REPRODUCTION_GUIDE.md
What are the metrics? See Performance Metrics section COMPLETE_REPRODUCTION_GUIDE.md
How do I fix errors? See Troubleshooting section COMPLETE_REPRODUCTION_GUIDE.md or QUICK_START_5_MINUTES.md
What's the file structure? See Project Structure section IMPLEMENTATION_DETAILS.md
How do I modify code? See specific component in IMPLEMENTATION_DETAILS IMPLEMENTATION_DETAILS.md

🗂️ File Organization 

TB-Guard-XAI/
├── QUICK_START_5_MINUTES.md          ← START HERE
├── COMPLETE_REPRODUCTION_GUIDE.md    ← Main reference
├── IMPLEMENTATION_DETAILS.md         ← Code details
├── README_GUIDES.md                  ← This file
├── TB-Guard-XAI/                     ← Main project folder
│   ├── backend.py
│   ├── app.py
│   ├── config.py
│   ├── requirements.txt
│   ├── .env (create this)
│   └── ... (all source files)
└── gradcam.py, mistral_explainer.py (top-level)

🚀 Quick Setup Checklist

  • Read QUICK_START_5_MINUTES.md
  • Clone repository
  • Create virtual environment
  • Install requirements
  • Create .env file with API keys
  • Run python backend.py
  • Open http://localhost:7860
  • Upload sample X-ray
  • View results

📖 Document Features

QUICK_START_5_MINUTES.md

  • ✅ Live demo link
  • ✅ 5-step installation
  • ✅ Quick troubleshooting
  • ✅ Next steps
  • ✅ API examples

COMPLETE_REPRODUCTION_GUIDE.md

  • ✅ Full overview
  • ✅ Complete architecture
  • ✅ Dataset sources + links
  • ✅ Step-by-step setup
  • ✅ Code snippets
  • ✅ Training instructions
  • ✅ Deployment options
  • ✅ Compliance info
  • ✅ References + citations

IMPLEMENTATION_DETAILS.md

  • ✅ Project structure
  • ✅ Configuration code
  • ✅ Model architecture
  • ✅ Preprocessing pipeline
  • ✅ Grad-CAM++ code
  • ✅ FastAPI routes
  • ✅ Monitoring code
  • ✅ Error handling
  • ✅ Training script
  • ✅ Testing framework

🎓 Learning Path

Beginner

  1. QUICK_START_5_MINUTES.md → Get running
  2. Try the web interface
  3. Upload sample X-rays

Intermediate

  1. COMPLETE_REPRODUCTION_GUIDE.md → Understand architecture
  2. Review datasets section
  3. Set up local environment
  4. Download sample datasets
  5. Run basic tests

Advanced

  1. IMPLEMENTATION_DETAILS.md → Deep dive into code
  2. Modify preprocessing pipeline
  3. Retrain model with custom data
  4. Deploy to production
  5. Monitor and optimize

✨ Key Topics Covered

Setup & Configuration

  • Virtual environments
  • Dependency installation
  • API key configuration
  • Environment variables
  • Directory structure

Understanding the Project

  • Architecture overview
  • System flow
  • Technology stack
  • Component interactions
  • Data pipeline

Datasets

  • Where to download
  • How to prepare
  • Data splits
  • Preprocessing
  • Validation sets

Implementation

  • Model architecture
  • Image preprocessing
  • Visual explanation (Grad-CAM++)
  • LLM integration
  • API endpoints
  • Frontend components

Training & Evaluation

  • Training procedure
  • Hyperparameters
  • Validation metrics
  • Performance benchmarks
  • External validation

Deployment

  • Local deployment
  • Docker containerization
  • Cloud deployment (AWS/GCP/Azure)
  • Hugging Face Spaces
  • Monitoring & logging

Compliance

  • HIPAA audit logging
  • GDPR data handling
  • Privacy considerations
  • Regulatory pathways

🔗 Important Links

Project

Datasets

API Services

Technologies

🐛 Getting Help

Common Issues

Issue: ModuleNotFoundError: No module named 'torch' → See QUICK_START_5_MINUTES.md Troubleshooting

Issue: MISTRAL_API_KEY not found → See QUICK_START_5_MINUTES.md - Create .env file

Issue: Port already in use → See QUICK_START_5_MINUTES.md Troubleshooting

Issue: Can't find datasets → See COMPLETE_REPRODUCTION_GUIDE.md - Dataset Information

Issue: Model inference slow → See COMPLETE_REPRODUCTION_GUIDE.md - Performance Metrics

More Help

  • Check README.md for project overview
  • Review GitHub Issues
  • Check documentation in code comments

📝 Version Info

  • TB-Guard-XAI Version: 3.0
  • Python Version: 3.10+
  • Last Updated: June 14, 2026
  • Status: Production Ready

🎯 What You'll Be Able to Do After Reading These Guides

✅ Run TB-Guard-XAI locally or in the cloud
✅ Understand the complete architecture
✅ Replicate the project from scratch
✅ Modify components for your needs
✅ Train models with custom data
✅ Deploy to production
✅ Monitor and maintain the system
✅ Contribute improvements
✅ Build healthcare AI applications

💡 Pro Tips

  1. Start Simple: Run live demo first, then local install
  2. Read Order: QUICK_START → COMPLETE_GUIDE → IMPLEMENTATION
  3. Keep Terminal Ready: You'll need to run many commands
  4. Internet Speed: Slow? Download datasets overnight
  5. GPU Helpful: Not required but makes training faster
  6. API Keys First: Get these before starting setup
  7. Test Early: Run health checks after each step
  8. Version Control: Keep .env out of git (it's in .gitignore)
  9. Monitor Progress: Check logs during training/deployment
  10. Save Output: Keep results and metrics for reference

🙏 Acknowledgments

  • WHO for TB screening guidelines
  • Mistral AI for LLM access
  • Google for Gemini API
  • Research Community for TB datasets
  • Healthcare Professionals who guided requirements

Ready to get started? → Open QUICK_START_5_MINUTES.md

Made with ❤️ for healthcare in resource-limited settings