FILE_STRUCTURE.md

📁 Hugging Face Deployment - Complete File Structure

Overview

This folder contains everything needed to deploy the Crystallization Component Predictor to Hugging Face Spaces.

Total Size: ~46 MB
Status: ✅ Ready for deployment

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📂 Directory Structure

huggingface_app/
│
├── 📄 Core Application Files
│   ├── app.py                          # Main Streamlit application (standalone)
│   ├── requirements.txt                # Python dependencies for Hugging Face
│   └── README.md                       # Hugging Face Space documentation
│
├── ⚙️ Configuration Files
│   ├── .gitattributes                  # Git LFS configuration for large files
│   └── .gitignore                      # Files to exclude from Git
│
├── 📚 Documentation
│   ├── DEPLOYMENT_GUIDE.md             # Step-by-step deployment instructions
│   ├── QUICKSTART.txt                  # Quick reference guide
│   └── FILE_STRUCTURE.md               # This file
│
├── 🔧 Utility Scripts
│   ├── verify_files.py                 # Verification script (check all files present)
│   ├── RUN_LOCAL.bat                   # Windows: Run app locally
│   └── run_local.sh                    # Linux/Mac: Run app locally
│
├── 🤖 models/
│   │
│   ├── simple_baseline/                # Simple Baseline models
│   │   ├── model_component_name.pkl    # Random Forest classifier (name)
│   │   ├── model_component_ph.pkl      # XGBoost regressor (pH)
│   │   ├── label_encoder_name.pkl      # Label encoder for component names
│   │   ├── scaler.pkl                  # StandardScaler for features
│   │   ├── tfidf.pkl                   # TF-IDF vectorizer for methods
│   │   └── training_results.json       # Training metrics
│   │
│   └── advanced_baseline/              # Advanced Baseline models
│       ├── model_component_name.pkl    # Ensemble classifier (name)
│       ├── model_component_conc.pkl    # Ensemble regressor (concentration)
│       ├── model_component_ph.pkl      # Ensemble regressor (pH)
│       ├── label_encoder_name.pkl      # Label encoder for component names
│       ├── scaler.pkl                  # StandardScaler for features
│       ├── tfidf.pkl                   # TF-IDF vectorizer for methods
│       └── training_results.json       # Training metrics
│
└── 📊 visualizations/                  # Performance comparison charts
    ├── 01_component_name_comparison.png
    ├── 02_component_conc_comparison.png
    ├── 03_component_ph_comparison.png
    ├── 04_all_approaches_heatmap.png
    ├── 05_complete_comparison.png
    ├── eda_01_missing_values_matrix.png
    ├── eda_02_missing_values_heatmap.png
    ├── eda_03_target_distributions.png
    ├── eda_04_feature_distributions.png
    └── eda_05_correlation_matrix.png

---

📋 File Descriptions

Core Application Files

app.py (Main Application)

• Purpose: Streamlit web application
• Key Features:
  • Model selection (Simple vs Advanced Baseline)
  • Interactive parameter input
  • Real-time predictions
  • Top-5 component predictions with probabilities
  • Visual pH scale
  • Downloadable results (CSV)
  • Performance visualizations
  • Model comparison charts
• Dependencies: All specified in requirements.txt
• Entry Point: Yes - Hugging Face will run this automatically

requirements.txt

• Purpose: Python package dependencies
• Key Packages:
  • streamlit==1.29.0
  • pandas==2.1.4
  • numpy==1.26.2
  • scikit-learn==1.3.2
  • xgboost==2.0.3
  • lightgbm==4.1.0
  • catboost==1.2.2
  • joblib==1.3.2
• Note: Versions pinned for reproducibility

README.md

• Purpose: Documentation displayed on Hugging Face Space page
• Contains:
  • App description and features
  • Model performance metrics
  • Usage instructions
  • Technical details
  • Background information
  • Acknowledgments
• Special: YAML header configures Space appearance

---

Configuration Files

.gitattributes

• Purpose: Git LFS (Large File Storage) configuration
• Tracks:
  • *.pkl (model files)
  • *.pth (PyTorch models)
  • *.json (results)
  • *.png (images)
• Why: Files >10MB need LFS on Hugging Face

.gitignore

• Purpose: Exclude unnecessary files from Git
• Excludes:
  • Python cache (__pycache__/)
  • Virtual environments
  • IDE files
  • OS files
  • Logs

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Documentation Files

DEPLOYMENT_GUIDE.md

• Purpose: Complete deployment instructions
• Sections:
  • Prerequisites
  • Step-by-step deployment (Web UI & Git CLI)
  • Troubleshooting
  • Customization
  • Monitoring
  • Security & privacy

QUICKSTART.txt

• Purpose: Quick reference for common tasks
• Format: Plain text for easy viewing
• Content: Essential info at a glance

FILE_STRUCTURE.md

• Purpose: This document - complete file inventory

---

Utility Scripts

verify_files.py

• Purpose: Pre-deployment verification
• Checks:
  • All required files present
  • Model files exist
  • Folder structure correct
  • Total size calculation
• Usage: python verify_files.py

RUN_LOCAL.bat (Windows)

• Purpose: Launch app locally for testing
• Usage: Double-click or run RUN_LOCAL.bat
• Opens: http://localhost:8501

run_local.sh (Linux/Mac)

• Purpose: Launch app locally for testing
• Usage: bash run_local.sh
• Opens: http://localhost:8501

---

Model Files

Simple Baseline Models (6 files)

Performance:

• Name Accuracy: 61.12%
• pH R²: 95.58%
• Concentration: N/A

Files:

1. model_component_name.pkl - Random Forest classifier
2. model_component_ph.pkl - XGBoost regressor
3. label_encoder_name.pkl - Encode component names
4. scaler.pkl - Feature normalization
5. tfidf.pkl - Text vectorization
6. training_results.json - Performance metrics

Advanced Baseline Models (7 files)

Performance:

• Name Accuracy: 64.18% ⭐
• Concentration R²: 47.33%
• pH R²: 99.34% ⭐

Files:

1. model_component_name.pkl - Ensemble (RF + XGB + LGB + Cat)
2. model_component_conc.pkl - Ensemble concentration regressor
3. model_component_ph.pkl - Ensemble pH regressor
4. label_encoder_name.pkl - Encode component names
5. scaler.pkl - Feature normalization
6. tfidf.pkl - Text vectorization
7. training_results.json - Performance metrics

---

Visualization Files (10 images)

Model Comparison Charts

• 01_component_name_comparison.png - Name accuracy comparison
• 02_component_conc_comparison.png - Concentration R² comparison
• 03_component_ph_comparison.png - pH R² comparison
• 04_all_approaches_heatmap.png - Performance heatmap
• 05_complete_comparison.png - Comprehensive comparison

EDA Visualizations

• eda_01_missing_values_matrix.png - Missing data patterns
• eda_02_missing_values_heatmap.png - Missing data heatmap
• eda_03_target_distributions.png - Target variable distributions
• eda_04_feature_distributions.png - Feature distributions
• eda_05_correlation_matrix.png - Feature correlations

---

🚀 Deployment Checklist

Before deploying to Hugging Face:

• ✅ All core files present (app.py, requirements.txt, README.md)
• ✅ Configuration files (.gitattributes, .gitignore)
• ✅ Simple Baseline models (6 files)
• ✅ Advanced Baseline models (7 files)
• ✅ Visualizations (10 images)
• ✅ Documentation complete
• ✅ Verification script passes
• ✅ Total size: 46.47 MB (within limits)
• ⏳ Test locally (run streamlit run app.py)
• ⏳ Deploy to Hugging Face
• ⏳ Test live deployment

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💡 Key Features

What Makes This Deployment Special

1. Self-Contained: No external dependencies or file paths
2. Production-Ready: All error handling included
3. User-Friendly: Beautiful UI with helpful tooltips
4. Well-Documented: Comprehensive README and guides
5. Verified: Includes verification script
6. Git LFS Ready: Configured for large model files
7. Cross-Platform: Works on Windows, Linux, Mac

App Capabilities

• ✅ Two model options (Simple & Advanced)
• ✅ Interactive parameter input
• ✅ Real-time predictions
• ✅ Top-5 component suggestions
• ✅ Confidence scores
• ✅ Visual pH scale
• ✅ Downloadable CSV results
• ✅ Performance visualizations
• ✅ Model comparison tables
• ✅ Responsive design

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📊 Statistics

Metric	Value
Total Files	30
Python Scripts	2
Model Files	13
Images	10
Documentation	5
Total Size	46.47 MB
Largest File	model_component_name.pkl (~8 MB each)

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🔗 Next Steps

1. Test Locally:

   streamlit run app.py
   

2. Verify Files:

   python verify_files.py
   

3. Deploy to Hugging Face:

   • Follow DEPLOYMENT_GUIDE.md
   • Or see QUICKSTART.txt for quick steps

4. Share Your Space:

   • URL: https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME

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⚠️ Important Notes

• All paths in app.py are relative to the script location
• Models load on first prediction (not at startup)
• Git LFS is required for files >10MB
• Free tier on Hugging Face is sufficient
• No API keys or secrets required

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📞 Support

• Deployment Issues: See DEPLOYMENT_GUIDE.md
• File Issues: Run verify_files.py
• App Issues: Check app.py comments
• Hugging Face Help: https://huggingface.co/docs/hub/spaces

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Status: ✅ READY FOR DEPLOYMENT

This folder is complete and ready to be uploaded to Hugging Face Spaces!