---
title: Creditcard-Fraud-Detection
app_file: gradio_app.py
sdk: gradio
sdk_version: 4.37.1
---
Credit Card Fraud Detection Application
This application leverages machine learning to detect fraudulent credit card transactions.
This project contains a Fraud Detection application that includes a FastAPI server for the backend and a Gradio interface for the frontend. The application can predict if a transaction is fraudulent using either XGBoost or RandomForest models.
[Dataset](https://www.kaggle.com/datasets/kartik2112/fraud-detection)
## Overview
1. **FastAPI Backend**: Handles prediction requests using machine learning models.
2. **Gradio Frontend**: Provides a user-friendly web interface for users to input transaction details and get predictions.
## Project Structure
```
/Creditcard-Fraud-Detection
│
├──/model
│ ├── xgboost.pkl
│ └── randomforest.pkl
├──/Encoder
│ └── WOEEncoder.pkl
│
├──/static
│ └──/images
│ ├── github.svg
│ └── api.png
│
├── app.py
├── gradio_app.py
├── docker_app.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── features.py
````
- `app.py`: Defines the FastAPI application.
- `gradio_app.py`: Defines the Gradio interface.
- `docker_app.py`: Gradio interface for docker
- `Dockerfile`: Dockerfile for building the Docker image.
- `docker-compose.yml`: Docker Compose file for orchestrating the services.
- `requirements.txt`: List of dependencies.
- `features.py`: List of features.
- `model/`: Directory containing pre-trained machine learning models.
- `Encoder/`: Directory containing encoders used for data preprocessing.
- `static/`: Directory containing static files such as images used in the interface.
## Getting Started
### Prerequisites
- Docker
- Docker Compose
### Installation
**Clone the repository:**
```bash
git clone https://github.com/Sibikrish3000/Creditcard-Fraud-Detection.git
cd Creditcard-Fraud-Detection
```
```
git install lfs
git lfs ls-files
```
```
git lfs pull
```
## Running Locally
### Using Docker Compose
1. Build and start the containers:
```sh
docker network create AIservice
```
```sh
docker-compose up --build
```
2. Access the Gradio interface at [http://localhost:7860](http://localhost:7860).
### Using Docker image
```sh
docker network create AIservice
```
```sh
docker pull sibikrish/creditcard-fraud-detection:latest
docker run sibikrish/creditcard-fraud-detection:latest #or
docker run -d -p 7860:7860 sibikrish/creditcard-fraud-detection:latest
```
### Manually
To run the application locally without Docker, ensure you have Python installed and follow these steps:
1. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
2. **Run the FastAPI server:**
```bash
uvicorn app:app --host 0.0.0.0 --port 8000
```
3. **Run the Gradio interface:**
```bash
python gradio_app.py
```
## Development
### Running in a Gitpod Cloud Environment
**Click the button below to start a new development environment:**
[](https://gitpod.io/#https://github.com/Sibikrish3000/Creditcard-Fraud-Detection)
## Deployment
### Using Vercel
1. Create a `vercel.json` file in the project root:
```json
{
"version": 2,
"builds": [
{ "src": "app.py", "use": "@vercel/python" },
{ "src": "gradio_app.py", "use": "@vercel/python" }
],
"routes": [
{ "src": "/api/(.*)", "dest": "app.py" },
{ "src": "/(.*)", "dest": "gradio_app.py" }
]
}
```
2. Deploy using the Vercel CLI:
```sh
vercel
```
## Usage
1. **Access the Gradio Interface:**
Open your web browser and navigate to `http://localhost:7860` to access the Gradio interface.
- **Inputs**: Users can input transaction details such as credit card frequency, job, age, gender, category, distance, hour, hours difference between transactions, amount, and choose a model.
- **Output**: The application returns a prediction indicating whether the transaction is legitimate or fraudulent.
- **Flag Option**: Users can enable a flag option to provide feedback on incorrect or suspicious predictions.
2. **Access the FastAPI Documentation:**
Open your web browser and navigate to `http://localhost:8000/docs` to access the FastAPI documentation.
### API Endpoints
- **POST /predict**
Predict if a transaction is fraudulent.
**Request:**
```json
{
"cc_freq": int,
"cc_freq_class": int,
"job": str,
"age": int,
"gender_M": int,
"category": str,
"distance_km": float,
"hour": str,
"hours_diff_bet_trans": float,
"amt": float
}
```
**Response:**
```json
{
"prediction": 0 for legitimate, 1 for fraudulent
}
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.