Add Hugging Face Spaces configuration and API documentation

README.md

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+---
+title: Amide Models
+emoji: 🔬
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_file: app.py
+pinned: false
+---
+
+# Amide Models - Breach Prediction API
+
+A Flask-based API for network breach prediction using multiple machine learning models.
+
+## Overview
+
+This project provides a REST API for predicting network security breaches using three different machine learning models:
+
+- **LightGBM**: Gradient boosting model for breach classification
+- **Autoencoder**: Deep learning model for anomaly detection
+- **XGB-LSTM**: Hybrid XGBoost and LSTM model for sequence prediction
+
+## Features
+
+- REST API endpoints for model inference
+- Support for multiple ML models
+- Input validation and error handling
+- Health check endpoint
+- Model information endpoint
+- Docker containerized deployment
+
+## API Endpoints
+
+### GET `/health`
+Health check endpoint to verify the service is running.
+
+**Response:**
+```json
+{
+  "status": "healthy"
+}
+```
+
+### GET `/models`
+Returns available models and their configuration.
+
+**Response:**
+```json
+{
+  "available_models": {
+    "lightGBM": {
+      "file": "lightGBM.py",
+      "available": true,
+      "interface": "hardcoded"
+    },
+    "autoencoder": {
+      "file": "autoencoder.py",
+      "available": true,
+      "interface": "hardcoded"
+    },
+    "XGB_lstm": {
+      "file": "XGB_lstm.py",
+      "available": true,
+      "interface": "argparse"
+    }
+  },
+  "required_columns": ["timestamp", "src_ip", "dst_ip", "src_port", "dst_port"]
+}
+```
+
+### POST `/compute`
+Run breach prediction on network logs.
+
+**Request:**
+```json
+{
+  "model_type": "lightGBM",
+  "file": [
+    {
+      "timestamp": "2024-01-01T10:00:00",
+      "src_ip": "192.168.1.100",
+      "dst_ip": "10.0.0.1",
+      "src_port": 12345,
+      "dst_port": 80,
+      "packet_size": 1500,
+      "seq": 1000,
+      "ack": 2000
+    }
+  ]
+}
+```
+
+**Response:**
+```json
+{
+  "success": true,
+  "output": "Model execution output",
+  "predictions": [
+    {
+      "timestamp": "2024-01-01T10:00:00",
+      "src_ip": "192.168.1.100",
+      "breach_probability": 0.95,
+      "breach_predicted": 1
+    }
+  ],
+  "error": null
+}
+```
+
+## Required Input Columns
+
+- `timestamp`: Timestamp of the network flow
+- `src_ip`: Source IP address
+- `dst_ip`: Destination IP address
+- `src_port`: Source port
+- `dst_port`: Destination port
+
+Additional columns like `packet_size`, `seq`, `ack` are recommended for better predictions.
+
+## Installation
+
+### Local Setup
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+The API will be available at `http://localhost:5000`
+
+### Docker Setup
+
+```bash
+docker build -t amide-models .
+docker run -p 5000:5000 amide-models
+```
+
+## Requirements
+
+- Python 3.8+
+- Flask
+- Flask-CORS
+- pandas
+- numpy
+- scikit-learn
+- tensorflow
+- lightgbm
+- xgboost
+
+See `requirements.txt` for all dependencies.
+
+## Models
+
+### LightGBM
+Gradient boosting classifier optimized for network breach detection with 60% threshold for breach classification.
+
+### Autoencoder
+Deep learning model using autoencoders to detect anomalous network patterns.
+
+### XGB-LSTM
+Hybrid model combining XGBoost with LSTM for temporal sequence analysis of network flows.
+
+## Output
+
+All models generate predictions with:
+- Breach probability score (0-1)
+- Binary breach prediction (0 or 1)
+- Confidence metrics
+
+## Development
+
+To modify or add new models:
+
+1. Create a new Python file with your model
+2. Update `MODEL_CONFIGS` in `app.py` with the new model configuration
+3. Ensure your model can accept input via:
+   - Hardcoded filename: reads from `network_logs.csv`
+   - Argparse: accepts `--logfile` command-line argument
+
+## License
+
+MIT
+
+## Contact
+
+For issues and questions, please check the repository documentation.