| --- |
| title: SAP Finance Dashboard with RPT-1-OSS |
| emoji: 📊 |
| colorFrom: purple |
| colorTo: blue |
| sdk: docker |
| app_port: 7860 |
| app_file: app_gradio.py |
| pinned: false |
| license: apache-2.0 |
| --- |
| |
| # 📊 SAP Finance Dashboard with RPT-1-OSS Model |
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| > **Production-ready AI-powered financial analysis dashboard** with SAP data integration, ML predictions, and interactive visualizations. |
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| **🔗 Live Demo**: https://huggingface.co/spaces/amitgpt/sap-finance-dashboard-RPT-1-OSS |
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| --- |
|
|
| ## 📋 Table of Contents |
|
|
| - [Overview](#overview) |
| - [Architecture](#architecture) |
| - [Key Features](#key-features) |
| - [What You'll Achieve](#what-youll-achieve) |
| - [Prerequisites](#prerequisites) |
| - [Quick Start](#quick-start) |
| - [Local Development](#local-development) |
| - [Deployment](#deployment) |
| - [Project Structure](#project-structure) |
| - [Troubleshooting](#troubleshooting) |
| - [License](#license) |
|
|
| --- |
|
|
| ## 🎯 Overview |
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| The **SAP Finance Dashboard** is an enterprise-grade web application that brings AI-powered financial intelligence to SAP systems. It combines: |
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| - **Real-time SAP data** through OData connectors |
| - **Advanced ML predictions** using the SAP-RPT-1-OSS model (Retrieval-Pretrained Transformer) |
| - **Interactive analytics** with Plotly visualizations |
| - **No-code ML training** via the Playground tab |
| - **Multi-user support** with secure authentication |
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|
| **Perfect for**: |
| - SAP finance teams needing predictive insights |
| - Data analysts building custom financial models |
| - Organizations requiring automated SAP reporting |
| - Learning AI/ML in enterprise contexts |
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| --- |
|
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| ## 🏗️ Architecture |
| ┌─────────────────────────────────────────────────────────┐ |
| │ Gradio Web Interface │ |
| │ (Dashboard • Data Explorer • Predictions • Playground) │ |
| └────────────────┬────────────────────────────────────────┘ |
| │ |
| ┌────────┴────────┬────────────┬─────────────────┐ |
| │ │ │ │ |
| ┌────▼────┐ ┌──────▼──────┐ ┌─▼────────────┐ ┌──▼──────────┐ |
| │ SAP │ │ SAP-RPT-1- │ │ Plotly │ │ Hugging │ |
| │ OData │ │ OSS Model │ │ Visualizer │ │ Face Hub │ |
| │Connector│ │ (Classifier/ │ │ (Charts) │ │ (Models) │ |
| │ │ │ Regressor) │ │ │ │ │ |
| └─────────┘ └──────────────┘ └─────────────┘ └─────────────┘ |
| │ │ |
| ┌────▼─────────────────▼─────────────────┐ |
| │ Python + Pandas + NumPy + PyTorch │ |
| └────────────────────────────────────────┘ |
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| --- |
|
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| ## ✨ Key Features |
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| ### 1. **Dashboard Tab** 📈 |
| - Key financial metrics (Revenue, Expenses, Net Income) |
| - Revenue vs. Expense breakdown |
| - Balance sheet analysis |
| - Real-time metric cards with trend indicators |
| - Fully interactive Plotly charts |
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| ### 2. **Data Explorer Tab** 🔍 |
| - Browse synthetic SAP datasets: |
| - **GL Accounts**: Chart of Accounts with balances |
| - **Financial Statements**: Multi-period P&L and Balance Sheet |
| - **Sales Orders**: Order details with line items |
| - Filter, search, and export capabilities |
| - Data validation and profiling |
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| ### 3. **Upload Tab** 📤 |
| - Upload custom CSV datasets |
| - Automatic data validation |
| - Preview before processing |
| - Support for various SAP data formats |
|
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| ### 4. **Predictions Tab** 🤖 |
| - AI-powered financial forecasting using SAP-RPT-1-OSS |
| - Classification tasks (e.g., account categorization) |
| - Regression tasks (e.g., amount prediction) |
| - Confidence scores and explainability |
| - Batch prediction support |
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| ### 5. **Playground Tab** 🛠️ |
| - **No-code ML training** interface |
| - Upload training datasets |
| - Configure model parameters: |
| - Context size (2048 for CPU, 8192 for GPU) |
| - Bagging factor (1-8) |
| - Model type (Classifier or Regressor) |
| - Train custom models |
| - Download predictions and model outputs |
| - Performance metrics display |
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| ### 6. **OData Connector Tab** 🔌 |
| - Direct connection to SAP systems |
| - Real-time data retrieval |
| - Secure credential handling |
| - Support for OData v2 and v4 |
| - Query builder interface |
|
|
| --- |
|
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| ## 🎓 What You'll Achieve |
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| After forking and deploying this repository, you'll have: |
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| ### ✅ **Enterprise Web Application** |
| - Production-ready Gradio interface |
| - Docker containerization for any cloud platform |
| - Multi-user authentication support |
| - Responsive design for desktop/mobile |
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| ### ✅ **AI Integration** |
| - Hands-on experience with the SAP-RPT-1-OSS model |
| - Understanding of Transformer-based financial predictions |
| - Custom model training workflows |
| - Real-time inference optimization |
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| ### ✅ **SAP Integration** |
| - OData connector patterns for SAP systems |
| - Secure credential management |
| - Real-time data pipeline examples |
| - Chart of Accounts and transaction handling |
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| ### ✅ **Cloud Deployment Skills** |
| - Docker multi-stage builds for ML apps |
| - HuggingFace Spaces deployment |
| - Azure Container Apps integration (optional) |
| - Environment management and secrets handling |
|
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| ### ✅ **Data Science Pipeline** |
| - Data preprocessing and validation |
| - Feature engineering examples |
| - Model training and evaluation |
| - Prediction batch processing |
|
|
| --- |
|
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| ## 📦 Prerequisites |
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| ### Local Development |
| - **Python 3.11+** (tested on 3.11) |
| - **Git** (for version control) |
| - **pip** (Python package manager) |
| - **Virtual environment** (recommended: venv or conda) |
|
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| ### For Cloud Deployment |
| - **Docker** (for containerization) |
| - **Hugging Face account** (free, for SAP-RPT-1-OSS access) |
| - **HF authentication token** (for gated models) |
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| ### For SAP Integration |
| - **SAP OData endpoint** URL |
| - **SAP credentials** (username/password or OAuth token) |
| - **Network access** to SAP system |
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| ### For GPU Support (Optional) |
| - **NVIDIA GPU** (CUDA 11.8+) |
| - **8GB+ VRAM** (recommended for model training) |
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| --- |
|
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| ## 🚀 Quick Start |
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| ### Option 1: Run on HuggingFace Spaces (Easiest, 5 minutes) |
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| 1. **Fork this repo to HF Spaces** |
| ```bash |
| # Visit: https://huggingface.co/spaces/amitgpt/sap-finance-dashboard-RPT-1-OSS |
| # Click "Files" → "Clone repository" |
| Accept SAP-RPT-1-OSS Model Access |
| |
| Go to: https://huggingface.co/SAP/sap-rpt-1-oss |
| Click "Agree" button |
| Create HF Token |
| |
| https://huggingface.co/settings/tokens |
| Click "New token" → Name it → Select "Read" → Create |
| Add Token to Your Space |
| |
| Go to your Space settings → "Repository secrets" |
| Add: HF_TOKEN = [your token from step 3] |
| Wait 2-3 minutes for rebuild |
| Done! Your Space will rebuild and start automatically |
| |
| 👉 See QUICK_START.md for detailed screenshots and troubleshooting |
| Option 2: Local Development (Recommended for customization) |
| Step 1: Clone Repository |
| git clone https://github.com/yourusername/SAP-RPT-1-OSS-App.git |
| cd SAP-RPT-1-OSS-App |
| |
| Step 2: Create Virtual Environment |
| |
| # On Windows |
| python -m venv venv |
| venv\Scripts\activate |
| |
| # On macOS/Linux |
| python3 -m venv venv |
| source venv/bin/activate |
| |
| Step 3: Install Dependencies |
| |
| |
| pip install --upgrade pip |
| pip install -r requirements.txt |
| pip install gradio==4.44.1 |
| pip install huggingface-hub==0.24.7 |
| pip install torch==2.0.0 transformers==4.30.0 |
| pip install git+https://github.com/SAP-samples/sap-rpt-1-oss |
| |
| |
| Step 4: Create Environment File |
| |
| cp .env.example .env |
| # Edit .env and add: |
| # - HUGGINGFACE_TOKEN=hf_xxxxx |
| # - SAP_USERNAME=your_sap_user (optional) |
| # - SAP_PASSWORD=your_sap_pwd (optional) |
| # - SAP_SERVER=sap_system_url (optional) |
| |
| |
| Step 5: Run Application |
| python app_gradio.py |
| The app will start at: http://localhost:7860 |
| |
| |
| 🐳 Docker Deployment |
| Build Docker Image |
| docker build -t sap-finance-dashboard:latest . |
| |
| 📊 Usage Examples |
| Example 1: View Financial Dashboard |
| Open: http://localhost:7860 |
| Click Dashboard tab |
| See metrics and charts instantly |
| Example 2: Make AI Predictions |
| Go to Predictions tab |
| Upload a CSV with financial data |
| Configure model settings |
| Click "Predict" |
| Download results |
| Example 3: Train Custom Model |
| Go to Playground tab |
| Upload training dataset |
| Set model parameters |
| Click "Train Model" |
| Download predictions and metrics |
| Example 4: Connect to SAP System |
| Go to OData tab |
| Enter SAP credentials and OData endpoint |
| Build query |
| Execute and view results |
| |
| 🤝 Contributing |
| We welcome contributions! Please: |
| |
| Fork the repository |
| Create a feature branch (git checkout -b feature/amazing-feature) |
| Commit changes (git commit -m 'Add amazing feature') |
| Push to branch (git push origin feature/amazing-feature) |
| Open Pull Request |
| |
| |
| 📄 License |
| This project is licensed under the Apache 2.0 License - see LICENSE file for details. |
| |
| Attribution: Uses the SAP-RPT-1-OSS model (also Apache 2.0). |
| |
| 🙋 Support |
| Questions? Open an issue on GitHub |
| Deployment help? See QUICK_START.md |
| Authentication issues? See HF_AUTHENTICATION_SETUP.md |
| Status updates? See DEPLOYMENT_STATUS.md |
| |
| 📈 Roadmap |
| Real-time SAP system synchronization |
| Multi-language support |
| Advanced explainability (SHAP, LIME) |
| Time-series forecasting models |
| Automated report generation (PDF/Excel) |
| Mobile app version |
| Integration with SAP Analytics Cloud |
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| |
| Made with ❤️ for SAP developers and data scientists to test SAP Opensource RPT-1 |
| |
| Developed by Amit Lal, Microsoft |
| aka.ms/amitlal |
| |
| |
| Last Updated: December 6, 2025 |
