README.md updated

README.md

@@ -12,20 +12,57 @@ pinned: false
 
 AI-powered predictive maintenance for solar PV inverters using ML models and LLM-based diagnosis.
 
-## API Endpoints
+## Features
+
+- **Anomaly Detection**: Isolation Forest + LSTM Autoencoder
+- **Failure Forecasting**: XGBoost models for time-to-failure and failure probability
+- **AI Diagnosis**: Gemini LLM provides maintenance recommendations
+- **REST API**: FastAPI-based service with automatic documentation
+
+## Quick Start
+
+### Installation
+
+```bash
+git clone <repository-url>
+cd pdm-agent
+pip install -r requirements.txt
+```
+
+### Environment Setup
+
+Set your Google API key for LLM features:
+```bash
+export GOOGLE_API_KEY=your_api_key_here
+```
+
+### Run
+
+```bash
+uvicorn app:app --host 0.0.0.0 --port 7860 --reload
+```
+
+## API Usage
 
 ### POST /analyze
-Accepts voltage and current sensor data, returns ML predictions and agent diagnosis.
+
+Analyzes sensor data and returns ML predictions with optional AI diagnosis.
 
 **Request:**
 ```json
 {
   "vdc1": [600.0, 601.0, 602.0],
   "idc1": [10.0, 10.1, 10.2],
-  "api_key": "your_google_api_key_here"  // Optional: enables AI diagnosis
+  "api_key": "your_google_api_key_here",
+  "asset_id": "PV_INVERTER_001"
 }
 ```
 
+**Parameters:**
+- `vdc1`, `idc1`: Voltage and current sensor readings
+- `api_key`: Optional Google API key for AI diagnosis
+- `asset_id`: Optional asset identifier (auto-generated if not provided)
+
 **Response:**
 ```json
 {
@@ -37,15 +74,76 @@ Accepts voltage and current sensor data, returns ML predictions and agent diagno
     "confidence": 0.85
   },
   "agent_output": {
-    "diagnosis": "...",
+    "diagnosis": "Minor voltage fluctuations detected...",
     "urgency": "Low",
-    "recommended_action": "...",
-    "justification": ["..."]
+    "recommended_action": "Continue monitoring...",
+    "justification": ["Voltage within range", "Current stable"]
   }
 }
 ```
 
-## ML Pipeline
-- **Anomaly Detection**: Isolation Forest + LSTM Autoencoder
-- **Failure Forecasting**: XGBoost (Time-to-Failure + Failure Probability)
-- **Agent Reasoning**: Gemini 2.5 Flash Lite via LangChain
+### Data Processing Pipeline
+
+1. **Input Validation**: Ensures voltage/current arrays match and contain sufficient data points
+2. **Data Preparation**: Pads input to 100 data points for consistent processing
+3. **Feature Engineering**: Creates 7 statistical features using rolling window analysis:
+   - Voltage mean/standard deviation
+   - Power mean/standard deviation
+   - Power delta and slope
+   - Normalized efficiency
+4. **ML Inference**: Processes features through anomaly detection and prediction models
+5. **Agent Analysis**: LLM analyzes ML results for human-readable diagnosis (if API key provided)
+
+## Configuration
+
+- `GOOGLE_API_KEY`: Required for AI diagnosis features
+- Models: Gemini 2.5 Flash Lite, XGBoost, LSTM Autoencoder
+- Port: 7860 (configurable)
+
+## Docker
+
+```bash
+docker build -t pdm-agent .
+docker run -p 7860:7860 -e GOOGLE_API_KEY=your_key pdm-agent
+```
+
+## Testing
+
+```bash
+# Test the API
+curl -X POST "http://localhost:7860/analyze" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "vdc1": [600.0, 601.0, 602.0],
+    "idc1": [10.0, 10.1, 10.2],
+    "api_key": "your_api_key",
+    "asset_id": "PV_INVERTER_001"
+  }'
+```
+
+## 📊 ML Models
+
+### Anomaly Detection
+- **Isolation Forest**: Unsupervised outlier detection
+- **LSTM Autoencoder**: Sequence-based anomaly scoring
+
+### Predictive Models
+- **XGBoost Classifier**: Failure probability prediction
+- **XGBoost Regressor**: Time-to-failure estimation
+
+### LLM Agent
+- **Gemini 2.5 Flash Lite**: Diagnostic reasoning and recommendations
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit your changes (`git commit -m 'Add amazing feature'`)
+4. Push to the branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+
+## License
+
+MIT License
+