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Prepare project for Hugging Face Space deployment - Add app.py with Gradio interface - Update requirements.txt with torch dependencies - Configure LFS for large files (models, data) - Update README with comprehensive documentation
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| Step-by-step explanation of how to accomplish the **Vehicle Diagnostics Agent** project end-to-end: | |
| ## Vehicle Diagnostics Agent Project: Detailed Implementation Plan | |
| ### Phase 1: Project Setup and Planning | |
| 1. **Define Project Goals and Scope** | |
| - Build a multi-agent AI system for predictive vehicle diagnostics. | |
| - Agents will collaboratively analyze sensor data to detect anomalies, identify causes, recommend maintenance, and generate reports. | |
| - Use realistic automotive sensor data (real/simulated). | |
| - Demonstrate production-readiness with FastAPI backend and Gradio interface. | |
| 2. **Select Tools and Frameworks** | |
| - LangChain and LangGraph for multi-agent orchestration. | |
| - Python for logic implementation. | |
| - PyTorch/TensorFlow for any ML model development. | |
| - FastAPI for service endpoints. | |
| - Gradio for user-friendly interface. | |
| - Docker for containerization. | |
| 3. **Gather Data** | |
| - Use open datasets like NASA Prognostics repository, Udacity self-driving car datasets, OR simulate vehicle telemetry in CARLA and inject anomalies. | |
| ### Phase 2: Data Collection and Preprocessing | |
| 1. **Acquire Vehicle Sensor Data** | |
| - Collect time-series data such as engine temperature, speed, RPM, battery voltage, brake status, etc. | |
| - For supervised learning, acquire or generate corresponding anomaly/fault labels. | |
| 2. **Clean and Process Data** | |
| - Implement filtering to reduce noise (e.g., moving average, Kalman filtering). | |
| - Normalize and synchronize sensor streams. | |
| - Extract meaningful statistical and domain-specific features. | |
| 3. **Split Data** | |
| - Partition into training, validation, and testing datasets. | |
| ### Phase 3: Build Individual Agents | |
| 1. **Data Ingestion Agent** | |
| - Load or stream sensor data into the system. | |
| - Prepare data for downstream agents. | |
| 2. **Anomaly Detection Agent** | |
| - Train and deploy ML models (e.g., LSTM, CNN) to detect unusual sensor patterns. | |
| - Use thresholding or probabilistic models for anomaly scoring. | |
| 3. **Root Cause Analysis Agent** | |
| - Implement rule-based or ML models to infer possible causes of anomalies by correlating sensor data patterns. | |
| - Integrate domain knowledge (e.g., engine fault codes mapping). | |
| 4. **Maintenance Recommendation Agent** | |
| - Map root causes to actionable maintenance steps or alerts. | |
| - Prioritize actions based on severity and impact. | |
| 5. **Report Generation Agent** | |
| - Compile diagnostic summaries into clear reports for users/operators. | |
| - Generate natural-language summaries. | |
| ### Phase 4: Agent Orchestration and Workflow | |
| 1. **Design Communication Protocol** | |
| - Define how agents exchange information (inputs/outputs). | |
| - Implement context/memory sharing to maintain state across steps. | |
| 2. **Implement Multi-Agent Orchestration** | |
| - Use LangChain to manage sequential and parallel task execution among agents. | |
| - Define orchestration logic to call agents in order (Data Ingestion → Anomaly Detection → Root Cause → Recommendation → Report). | |
| 3. **Add Error Handling and Recovery** | |
| - Establish retry/fallback rules in case of agent failures or inconsistent data. | |
| ### Phase 5: Backend and Frontend Development | |
| 1. **FastAPI Service** | |
| - Develop API endpoints for triggering diagnostics, retrieving reports, and health checks. | |
| - Handle concurrent user requests. | |
| 2. **Gradio-based UI** | |
| - Build an interactive dashboard for users to input vehicle IDs and view diagnostic reports. | |
| - Visualize detected anomalies and recommended actions. | |
| ### Phase 6: Deployment and Monitoring | |
| 1. **Containerization** | |
| - Create Docker images for backend and frontend. | |
| - Use Docker Compose for service orchestration. | |
| 2. **Deployment** | |
| - Deploy locally or on cloud (AWS, Azure). | |
| - Configure environment variables and API keys securely. | |
| 3. **Observability** | |
| - Add logging and monitoring for system performance and errors. | |
| - Use LangSmith or other tracing tools to instrument agent workflows. | |
| ### Phase 7: Testing and Validation | |
| 1. **Unit Testing** | |
| - Write tests for each agent’s logic. | |
| - Validate correct anomaly detection and recommendations. | |
| 2. **Integration Testing** | |
| - Verify multi-agent orchestration flows end-to-end. | |
| - Simulate vehicle scenarios including anomalies. | |
| 3. **User Acceptance Testing** | |
| - Gather feedback on Gradio interface usability and report clarity. | |
| ### Phase 8: Documentation and Presentation | |
| 1. **Write Comprehensive README** | |
| - Explain project goals, architecture, how to run and extend. | |
| - Include example data and system diagram. | |
| 2. **Prepare Demo and Presentation** | |
| - Showcase live diagnostics on sample data. | |
| - Highlight modular design and agent collaboration. | |
| ## Tasks to accomplish | |
| | 1 | Data collection, preprocessing, build Data Ingestion & Anomaly Agents | | |
| | 2 | Build Root Cause, Recommendation, Report Agents; implement LangChain orchestration | | |
| | 3 | Backend (FastAPI), Frontend (Gradio), Deployment, Testing, Documentation | | |
| - Multi-agent AI system design and orchestration | |
| - Production-grade ML pipeline development | |
| - Cross-functional, safety-critical domain knowledge | |
| - Full-stack deployment and user interface | |
| - Strong data engineering and AI validation skills | |
| This project will serve as a flagship portfolio piece so one can apply AI to automotive challenges with agentic AI thinking. | |