README.md

---
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

> **Production-ready AI-powered financial analysis dashboard** with SAP data integration, ML predictions, and interactive visualizations.

**🔗 Live Demo**: https://huggingface.co/spaces/amitgpt/sap-finance-dashboard-RPT-1-OSS

---

## 📋 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

The **SAP Finance Dashboard** is an enterprise-grade web application that brings AI-powered financial intelligence to SAP systems. It combines:

- **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

**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

---

## 🏗️ 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 │
└────────────────────────────────────────┘


---

## ✨ Key Features

### 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

### 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

### 3. **Upload Tab** 📤
- Upload custom CSV datasets
- Automatic data validation
- Preview before processing
- Support for various SAP data formats

### 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

### 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

### 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

---

## 🎓 What You'll Achieve

After forking and deploying this repository, you'll have:

### ✅ **Enterprise Web Application**
- Production-ready Gradio interface
- Docker containerization for any cloud platform
- Multi-user authentication support
- Responsive design for desktop/mobile

### ✅ **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

### ✅ **SAP Integration**
- OData connector patterns for SAP systems
- Secure credential management
- Real-time data pipeline examples
- Chart of Accounts and transaction handling

### ✅ **Cloud Deployment Skills**
- Docker multi-stage builds for ML apps
- HuggingFace Spaces deployment
- Azure Container Apps integration (optional)
- Environment management and secrets handling

### ✅ **Data Science Pipeline**
- Data preprocessing and validation
- Feature engineering examples
- Model training and evaluation
- Prediction batch processing

---

## 📦 Prerequisites

### Local Development
- **Python 3.11+** (tested on 3.11)
- **Git** (for version control)
- **pip** (Python package manager)
- **Virtual environment** (recommended: venv or conda)

### For Cloud Deployment
- **Docker** (for containerization)
- **Hugging Face account** (free, for SAP-RPT-1-OSS access)
- **HF authentication token** (for gated models)

### For SAP Integration
- **SAP OData endpoint** URL
- **SAP credentials** (username/password or OAuth token)
- **Network access** to SAP system

### For GPU Support (Optional)
- **NVIDIA GPU** (CUDA 11.8+)
- **8GB+ VRAM** (recommended for model training)

---

## 🚀 Quick Start

### Option 1: Run on HuggingFace Spaces (Easiest, 5 minutes)

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


 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