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+# URL Malicious Detection API
+
+This FastAPI application provides an API for detecting malicious URLs using two different machine learning models.
+
+## Features
+
+- Two independent ML models for URL analysis
+- RESTful API for easy integration
+- High performance with `uv` package management
+
+## Installation
+
+1. Clone the repository
+2. Create and activate a virtual environment using `uv`:
+
+```bash
+cd backend
+uv venv
+source .venv/bin/activate
+```
+
+3. Install dependencies using `uv pip`:
+
+```bash
+uv pip install -r requirements.txt
+```
+
+## Running the Application
+
+Start the FastAPI server:
+
+```bash
+uvicorn app.main:app --reload
+```
+
+The API will be available at http://localhost:8000
+
+## API Endpoints
+
+### GET /
+
+Health check endpoint that confirms the API is running.
+
+**Response**:
+```json
+{
+  "message": "URL 악성 판별기 FastAPI 서버 정상 작동 중!"
+}
+```
+
+### POST /predict
+
+Analyzes a URL to determine if it's malicious.
+
+**Request Body**:
+```json
+{
+  "url": "http://example.com"
+}
+```
+
+**Response**:
+```json
+{
+  "url": "http://example.com",
+  "model1": 0.2048,  // Lower value means less likely to be malicious
+  "model2": {
+    "url": "http://example.com",
+    "malicious_probability": 0.1076,  // Probability of being malicious
+    "is_malicious": false,  // Boolean classification result
+    "threshold": 0.4034  // Classification threshold
+  }
+}
+```
+
+## Models
+
+The application uses two different models for URL analysis:
+
+1. **XGBoost Ensemble** (model1): Ensemble of 4 XGBoost models
+2. **Neural Network** (model2): TensorFlow/Keras model
+
+## Dependencies
+
+Main dependencies:
+- FastAPI
+- TensorFlow
+- XGBoost
+- scikit-learn
+- pandas
+- numpy
+- tldextract
+- uvicorn
+
+## License
+
+This project is licensed under the MIT License.