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| title: Vehicle Diagnostics Agent | |
| emoji: 🚗 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.44.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Multi-Agent AI System for Predictive Vehicle Diagnostics | |
| tags: | |
| - anomaly-detection | |
| - lstm | |
| - pytorch | |
| - langchain | |
| - langgraph | |
| - multi-agent | |
| - vehicle-diagnostics | |
| - time-series | |
| # 🚗 Vehicle Diagnostics Agent | |
| ## Multi-Agent AI System for Predictive Vehicle Diagnostics | |
| This is a production-ready multi-agent AI system that analyzes vehicle sensor data to detect anomalies, identify root causes, and provide actionable maintenance recommendations. | |
| ### 🎯 Key Features | |
| - **🔍 Anomaly Detection**: LSTM-based neural network with 99.53% validation accuracy | |
| - **🔬 Root Cause Analysis**: Identifies underlying issues with OBD-II fault code mapping | |
| - **🔧 Maintenance Recommendations**: Provides cost estimates and prioritized action plans | |
| - **📊 Interactive Visualizations**: Real-time anomaly detection charts | |
| - **📋 Natural Language Reports**: Easy-to-understand summaries for vehicle owners | |
| ### 🏗️ System Architecture | |
| The system employs a **multi-agent architecture** orchestrated by LangGraph: | |
| 1. **Data Ingestion Agent** - Loads and prepares vehicle sensor data | |
| 2. **Anomaly Detection Agent** - LSTM neural network for pattern detection | |
| 3. **Root Cause Analysis Agent** - Fault pattern matching and correlation analysis | |
| 4. **Maintenance Recommendation Agent** - Cost estimation and action planning | |
| 5. **Report Generation Agent** - Comprehensive diagnostic reports | |
| ### 🚀 Technology Stack | |
| - **ML Framework**: PyTorch (LSTM-based time-series anomaly detection) | |
| - **Orchestration**: LangGraph for multi-agent coordination | |
| - **Frontend**: Gradio for interactive UI | |
| - **Data Processing**: Pandas, NumPy, Scikit-learn | |
| - **Visualization**: Plotly | |
| ### 📊 Model Performance | |
| - **Validation Accuracy**: 99.53% | |
| - **Training Loss**: 0.0003 (final epoch) | |
| - **Validation Loss**: 0.0409 (best) | |
| - **Dataset**: 50,000 records from 100 vehicles | |
| - **Features**: 60+ engineered features from 14 sensor measurements | |
| ### 🎮 How to Use | |
| 1. **Select a Vehicle**: Choose from available vehicle IDs | |
| 2. **Set Reading Count**: Specify how many recent readings to analyze (default: 200) | |
| 3. **Run Diagnostic**: Click the diagnostic button to analyze | |
| 4. **Review Results**: View anomaly detection, root cause analysis, and maintenance recommendations | |
| ### 📈 Dataset | |
| The system analyzes synthetic vehicle sensor data including: | |
| - Engine temperature, RPM, speed | |
| - Battery voltage and health | |
| - Oil and fuel pressure | |
| - Tire pressure (all four wheels) | |
| - Vibration levels | |
| - Coolant temperature | |
| - And more... | |
| ### 🔬 Technical Details | |
| **Anomaly Detection Model:** | |
| - Architecture: 2-layer LSTM with 64 hidden units | |
| - Input: Sequences of 10 timesteps with 60 features | |
| - Output: Binary classification (normal/anomaly) | |
| - Training: 31,570 sequences on GPU | |
| **Root Cause Analysis:** | |
| - 8 fault pattern definitions | |
| - Sensor correlation analysis | |
| - Confidence scoring | |
| - OBD-II fault code mapping (P-codes, C-codes) | |
| ### 📝 License | |
| MIT License - See LICENSE file for details | |
| ### 🔗 Links | |
| - **GitHub**: [VehicleDiagnosticsAgent](https://github.com/saadmann18/VehicleDiagnosticsAgent) | |
| - **Documentation**: Full project documentation available in the repository | |
| ### 👨💻 Author | |
| Built with ❤️ by Saad Mann | |
| --- | |
| **Note**: This is a demonstration system using synthetic data. For production use with real vehicles, integration with actual OBD-II devices would be required. | |