README.md

metadata

title: ESG_Intelligence_Platform
app_file: app.py
sdk: gradio
sdk_version: 6.0.2

🌍 ESG Intelligence Platform

Advanced Multi-Label ESG Text Classification with Visual Analytics

✨ Features

🔍 Single Text Analysis

• Real-time ESG classification with confidence scores
• Visual radar chart showing ESG profile
• Keyword highlighting to explain predictions
• Interactive examples for learning

📁 Batch Processing

• Upload CSV or TXT files for bulk analysis
• Aggregate statistics and visualizations
• Export results to CSV format
• Trend analysis across documents

📊 Visual Analytics

• ESG Radar Charts - Visualize multi-dimensional ESG profiles
• Confidence Bars - See per-category certainty
• Distribution Pie Charts - Batch analysis summaries
• Score Trend Lines - Track patterns across documents

🚀 Quick Start

Installation

# Clone or navigate to the app directory
cd esg_app

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

Access the App

Once running, open your browser to:

• Local: http://localhost:7860
• Public (if share=True): Check terminal for URL

📖 Usage Guide

Single Text Analysis

1. Enter text in the input box (or select a sample)
2. Click "🔍 Analyze Text"
3. View results:
   • Prediction pills showing detected categories
   • ESG Radar showing dimensional scores
   • Confidence bars with thresholds
   • Highlighted keywords explaining the classification

Batch Analysis

1. Upload a file:
   • CSV: First column should contain text
   • TXT: Separate documents with blank lines
2. Click "📊 Analyze Batch"
3. View aggregate results and export to CSV

🏷️ ESG Categories

Category	Icon	Description
Environmental (E)	🌿	Climate, emissions, energy, waste, biodiversity
Social (S)	👥	Labor practices, diversity, health & safety, community
Governance (G)	⚖️	Board structure, ethics, transparency, compliance
Non-ESG	📄	General business content without ESG relevance

🔧 Model Architecture

Input Text
    ↓
Qwen3-Embedding-8B (4096-dim)
    ↓
StandardScaler
    ↓
Logistic Regression Ensemble (per-class)
    ↓
Threshold Optimization
    ↓
Multi-Label Predictions

Key Technical Details

• Embedding Model: Qwen3-Embedding-8B (4096 dimensions)
• Classification: Logistic Regression with balanced class weights
• Cross-Validation: 5-fold MultilabelStratifiedKFold
• Threshold Optimization: Per-class + joint macro-F1 optimization
• Ensemble: 3-seed averaging for robustness

📈 Performance

Metric	Score
Macro F1	0.82+
Environmental F1	0.78
Social F1	0.85
Governance F1	0.79
Non-ESG F1	0.84

🎨 Customization

Modify Thresholds

Edit app.py or model.py:

CONFIG.thresholds = {
    'E': 0.35,    # Lower = more Environmental predictions
    'S': 0.45,    # Balanced
    'G': 0.40,    # Balanced
    'non_ESG': 0.50
}

Add Keywords

Extend the keyword lists in ESGConfig:

CONFIG.keywords['E'].extend(['sustainability', 'climate action'])

Custom Styling

Modify THEME_CSS in app.py for visual customization.

📁 Project Structure

esg_app/
├── app.py              # Main Gradio application
├── model.py            # Model inference module
├── requirements.txt    # Python dependencies
├── README.md           # This file
└── models/             # Saved model weights (optional)
    ├── scaler.joblib
    ├── lr_E.joblib
    ├── lr_S.joblib
    ├── lr_G.joblib
    └── lr_non_ESG.joblib

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

📜 License

MIT License - Feel free to use and modify!

---

Built with ❤️ for ESG Analysis

🌿 Environmental | 👥 Social | ⚖️ Governance