Professional XRF (X-ray Fluorescence) analysis reporting system with advanced AI capabilities powered by both OpenAI and EleutherAI (community-driven AI) models through OpenRouter.
- Multi-format XRF Data Support: Delta and Vanta CSV formats
- Professional PDF Reports: Customizable headers, element analysis, and statistics
- Customer Information Management: Complete project tracking
- Element Analysis: Automatic detection and statistical analysis of most prevalent elements
- Dual AI Provider Support: OpenAI and OpenRouter (EleutherAI)
- EleutherAI Models: Llama 3.1, Mixtral 8x7B, Claude Haiku, and more
- Intelligent Analysis: Material identification, quality assessment, and technical recommendations
- LangGraph Workflows: Multi-step AI analysis pipelines
- Smart Fallback: Automatic fallback to standard reports when AI is unavailable
- Command Line Interface (CLI): Batch processing and automation
- Graphical User Interface (GUI): User-friendly point-and-click operation
- Conversational Interface: Natural language report requests
- Python 3.11+
- Virtual environment (recommended)
# Clone or download the project
cd ReportGenerator
# Create virtual environment
python -m venv venv
venv\Scripts\activate # Windows
# source venv/bin/activate # macOS/Linux
# Install dependencies
pip install -r requirements.txtCreate a .env file:
OPENAI_API_KEY=your-openai-key-here
OPENROUTER_API_KEY=your-openrouter-key-here
# Standard report
python simple_cli.py "ExampleData/DeltaData.csv" --customer-name "ABC Corp" --product-number "XRF-001"
# AI-enhanced with OpenAI
python simple_cli.py "data.csv" --ai-enhanced --ai-provider openai
# AI-enhanced with EleutherAI (via OpenRouter)
python simple_cli.py "data.csv" --ai-enhanced --ai-provider openrouter --ai-model llama-3.1-8bpython simple_main.py- OpenRouter Models:
auto,llama-3.1-8b,llama-3.1-70b,mixtral-8x7b,claude-haiku,hermes-3-8b - OpenAI Models: GPT-4, GPT-3.5-turbo (configured via OpenAI settings)
ReportGenerator/
├── simple_cli.py # Command line interface
├── simple_main.py # GUI application
├── main.py # Legacy main application
├── requirements.txt # Python dependencies
├── .env # API keys (create this)
│
├── src/ # Core modules
│ ├── data_parsers.py # XRF data parsing
│ ├── simple_report_generator.py # PDF generation
│ └── ai_enhanced_report_generator.py # AI-enhanced reports
│
├── ai_modules/ # AI integration modules
│ ├── ai_enhanced_generator.py # OpenAI integration
│ ├── simple_openrouter_ai_generator.py # EleutherAI integration
│ ├── conversational_interface.py # Natural language interface
│ └── langgraph_example.py # LangGraph workflows
│
├── tests/ # Test files and demos
│ ├── test_live_eleutherai.py # Live EleutherAI test
│ ├── test_fallback_ai.py # Fallback system test
│ └── eleutherai_complete_demo.py # Complete demo
│
├── docs/ # Documentation
│ ├── README.md # This file
│ ├── API_KEY_SETUP.md # API key configuration guide
│ ├── LANGGRAPH_INTEGRATION.md # LangGraph integration details
│ └── PROJECT_STRUCTURE.md # Detailed project structure
│
├── ExampleData/ # Sample XRF data files
├── output/ # Generated reports
└── venv/ # Virtual environment
- Models: Meta Llama 3.1 (8B/70B), Mixtral 8x7B, and more
- Provider: OpenRouter (cost-effective, open-source focused)
- Analysis: Professional material identification and quality assessment
- Cost: Typically lower cost than OpenAI
- Models: GPT-4, GPT-3.5-turbo
- Analysis: Advanced material insights and recommendations
- Reliability: Industry-standard AI analysis
- Quality Assessment: Data consistency and measurement reliability
- Material Identification: Alloy classification and composition analysis
- Technical Recommendations: Industry-standard suggestions for further testing
- Professional Reporting: AI insights integrated into PDF reports
QUALITY ASSESSMENT:
Overall data quality rating: 8/10
The XRF data shows good consistency with narrow concentration ranges
for primary elements (Fe: 97.27-97.96%, Cr: 1.10-1.39%).
MATERIAL ANALYSIS:
Most likely material: Carbon Steel with high Cr content
- Fe (97.73%): Primary matrix element
- Cr (1.20%): Corrosion resistance contributor
- Mn (0.51%): Deoxidizer/strengthening element
RECOMMENDATIONS:
1. Optimize Cu and Ni detection limits
2. Verify sample preparation procedures
3. Consider SEM-EDS for microstructure confirmation
- Install the system following the installation guide above
- Try the GUI: Run
python simple_main.pyfor point-and-click operation - Test with sample data: Use files in
ExampleData/folder - Add AI features: Set up API keys for intelligent analysis
- Explore demos: Run files in
tests/folder to see AI capabilities
--ai-enhanced: Enable AI analysis--ai-provider: Chooseopenaioropenrouter--ai-model: Select specific model (e.g.,llama-3.1-8b)--customer-name: Customer information--product-number: Product tracking--elements N: Number of elements to analyze (5-15)
- AI Provider selection (radio buttons)
- Model selection dropdown
- API key management
- Customer information forms
- Output directory selection
- Style errors: PDF generation issues (being resolved)
- API quota: Add credits to OpenRouter/OpenAI accounts
- Data format: Ensure CSV files are properly formatted
- Check the
docs/folder for detailed guides - Run test files in
tests/folder to verify functionality - Review example data in
ExampleData/folder
This system successfully integrates cutting-edge AI analysis with professional XRF reporting:
- ✅ Live EleutherAI Integration: Confirmed working with real API calls
- ✅ Professional Analysis: Industry-standard material identification
- ✅ Multiple Interfaces: CLI, GUI, and conversational options
- ✅ Cost-Effective: Community-driven AI reduces analysis costs
- ✅ Fallback Systems: Reliable operation even when AI is unavailable
Your XRF analysis system is now powered by both commercial and open-source AI! 🚀