Update README.md

README.md

@@ -1,236 +1,234 @@
----
-title: Creditcard-Fraud-Detection
-app_file: gradio_app.py
-sdk: gradio
-sdk_version: 4.36.1
----
-
-<!-- markdownlint-disable -->
-<p align="center">
-<a href = "https://github.com/Sibikrish3000/Creditcard-Fraud-Detection" > <img src = "https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/blob/main/static/images/creditcard1.jpg?raw=true" alt = "fraud detection image"  width=500 height=280> </a>
-</p>
-<h1 align="center"> Credit Card Fraud Detection Application </h1>
-
-<p align="center">
-This application leverages machine learning to detect fraudulent credit card transactions.
-</p>
-
-<p align="center">
-<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/blob/main/LICENSE"><img src="https://img.shields.io/github/license/Sibikrish3000/Creditcard-Fraud-Detection" alt="GitHub license"></a>
-<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/stargazers"><img src="https://img.shields.io/github/stars/Sibikrish3000/Creditcard-Fraud-Detection?style=social" alt="GitHub stars"></a>
-<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/issues"><img src="https://img.shields.io/github/issues/Sibikrish3000/Creditcard-Fraud-Detection" alt="GitHub issues"></a>
-<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/actions/workflows/quality.yml"><img src="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/actions/workflows/quality.yml/badge.svg" alt="Code Quality"></a>
-</p>
-<p align="center">
-<a href="https://scikit-learn.org/"><img src=https://img.shields.io/badge/sklearn-darkorange.svg?style=flat&logo=scikit-learn&logoColor=white alt="sklearn"></a>
-<a href="https://www.python.org"><img src="https://img.shields.io/badge/Python-darkblue.svg?style=flat&logo=python&logoColor=white" alt="language"></a>
-<a href="https://fastapi.tiangolo.com/" ><img src="https://img.shields.io/badge/FastAPI-darkgreen.svg?style=flat&logo=fastapi&logoColor=white " alt="fastapi"></a> <a href="https://hub.docker.com/repository/docker/sibikrish3000/creditcard-fraud-detection/"><img src="https://img.shields.io/badge/Docker-blue?style=flat&logo=docker&logoColor=white" alt= "docker"></a>
-
-
-
-
-
-</p>
-
-
-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:**
-
-[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](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.
-
-
+---
+title: Creditcard-Fraud-Detection
+app_file: gradio_app.py
+sdk: gradio
+sdk_version: 4.37.1
+---
+
+<!-- markdownlint-disable -->
+<p align="center">
+<a href = "https://github.com/Sibikrish3000/Creditcard-Fraud-Detection" > <img src = "https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/blob/main/static/images/creditcard1.jpg?raw=true" alt = "fraud detection image"  width=500 height=280> </a>
+</p>
+<h1 align="center"> Credit Card Fraud Detection Application </h1>
+
+<p align="center">
+This application leverages machine learning to detect fraudulent credit card transactions.
+</p>
+
+<p align="center">
+<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/blob/main/LICENSE"><img src="https://img.shields.io/github/license/Sibikrish3000/Creditcard-Fraud-Detection" alt="GitHub license"></a>
+<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/stargazers"><img src="https://img.shields.io/github/stars/Sibikrish3000/Creditcard-Fraud-Detection?style=social" alt="GitHub stars"></a>
+<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/issues"><img src="https://img.shields.io/github/issues/Sibikrish3000/Creditcard-Fraud-Detection" alt="GitHub issues"></a>
+<a href="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/actions/workflows/quality.yml"><img src="https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/actions/workflows/quality.yml/badge.svg" alt="Code Quality"></a>
+</p>
+<p align="center">
+<a href="https://scikit-learn.org/"><img src=https://img.shields.io/badge/sklearn-darkorange.svg?style=flat&logo=scikit-learn&logoColor=white alt="sklearn"></a>
+<a href="https://www.python.org"><img src="https://img.shields.io/badge/Python-darkblue.svg?style=flat&logo=python&logoColor=white" alt="language"></a>
+<a href="https://fastapi.tiangolo.com/" ><img src="https://img.shields.io/badge/FastAPI-darkgreen.svg?style=flat&logo=fastapi&logoColor=white " alt="fastapi"></a> <a href="https://hub.docker.com/repository/docker/sibikrish3000/creditcard-fraud-detection/"><img src="https://img.shields.io/badge/Docker-blue?style=flat&logo=docker&logoColor=white" alt= "docker"></a>
+
+
+
+
+
+</p>
+
+
+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:**
+
+[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](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.