Upload folder using huggingface_hub

.dockerignore

@@ -0,0 +1,25 @@
+# Ignore Python cache files
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+
+# Ignore virtual environment directories
+venv/
+env/
+.venv/
+.env/
+
+# Ignore system files
+.DS_Store
+Thumbs.db
+
+
+# Ignore logs and temp files
+*.log
+*.tmp
+
+
+# Ignore data directories
+data/
+datasets/

.gitattributes

@@ -1,35 +1,5 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto
+*.nc filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,162 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

.pre-commit-config.yaml

@@ -0,0 +1,30 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-larger-file
+      - id: check-yaml
+
+  - repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+      - id: black
+        language_version: python3
+
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: 3.9.2
+    hooks:
+      - id: flake8
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v0.812
+    hooks:
+      - id: mypy
+
+  - repo: https://github.com/pre-commit/mirrors-pylint
+    rev: v2.6.0
+    hooks:
+      - id: pylint
+        additional_dependencies: ['pylint-django']

Dockerfile

@@ -0,0 +1,27 @@
+FROM python:3.9-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy all application files into the container
+COPY . .
+
+# Expose the ports for both FastAPI and Gradio
+EXPOSE 8000
+EXPOSE 7860
+
+# Command to run both FastAPI and Gradio servers
+<<<<<<< Updated upstream
+CMD ["python", "gradio_app.py","&&","uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
+
+=======
+CMD ["python", "gradio_app.py","&&","uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
+>>>>>>> Stashed changes

Encoder/WOEEncoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbf100c787c2a3596c11e46d84342a81a304602bdbc3039ad0135e8048abce65
+size 54953

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Sibi krish
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,13 +1,236 @@
----
-title: Creditcard Fraud Detection
-emoji: 🏃
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 4.37.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+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.
+
+

about.md

@@ -0,0 +1,62 @@
+# Credit Card Fraud Detection App built with Gradio, FastAPI, and Docker
+
+This Credit Card Fraud Detection App leverages Machine Learning models served as an API to identify potentially fraudulent credit card transactions, empowering users to assess transaction legitimacy based on various input features including credit card frequency, job, age, gender, category, distance, hour, hours difference between transactions, amount, and model choice (XGBoost or RandomForest).
+### Features:
+
+1. **FastAPI Backend**: The backend of the application is implemented using FastAPI, a modern web framework for building APIs with Python. It exposes an endpoint `/predict` that accepts POST requests with transaction data and returns predictions.
+
+2. **Gradio Frontend**: The frontend of the application is implemented using Gradio, a Python library that allows for the creation of customizable UI components for machine learning models. Users interact with the application through a user-friendly interface where they can input transaction details and receive predictions.
+
+ 3. **Models**:  The utilization of both XGBoost and RandomForest models ensures robust prediction capabilities, leveraging powerful machine learning algorithms widely recognized for their effectiveness in classification and regression tasks.
+
+4. **Flagging Option**: Users can flag examples in the interface, providing feedback on predictions that seem incorrect or suspicious. This functionality allows for continuous improvement of the model.
+
+### Usage:
+
+- Users can run the application locally by executing the provided Python script.
+- They can interact with the application through the Gradio interface in their web browser, inputting transaction details and receiving predictions.
+- The application provides predictions in real-time, leveraging machine learning models trained on historical transaction data.
+
+### Deployment:
+
+- The application can be deployed locally or on a cloud platform using Docker. Docker containers encapsulate both the FastAPI backend and the Gradio frontend, making deployment straightforward.
+- Additionally, the application can be deployed to a serverless platform like Vercel or Heroku, leveraging their respective deployment methods.
+
+### Future Improvements:
+
+1. Enhance model performance by fine-tuning hyperparameters or using more sophisticated models.
+2. Add more features to improve prediction accuracy.
+3. Implement user authentication and authorization for secure access to the application.
+4. Integrate with a database to store flagged examples for analysis and model improvement.
+
+### Development:
+
+- Developers can extend and enhance the application by adding new features, improving model accuracy, or optimizing performance.
+- The codebase is modular and well-structured, facilitating easy maintenance and collaboration among developers.
+
+Overall, this Fraud Detection application provides a practical solution for identifying potentially fraudulent transactions, helping businesses and organizations mitigate financial risks.
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](https://github.com/Sibikrish3000/Creditcard-Fraud-Detection/blob/main/LICENSE) file for details.
+
+The Jupyter notebook, trained model, and accompanying documentation, including Dockerfiles, FastAPI script, and Gradio Interface script, can be accessed through the GitHub repository linked below:
+
+[GitHub Repository](https://github.com/Sibikrish3000/Creditcard-Fraud-Detection)
+
+[![size](https://img.shields.io/github/repo-size/Sibikrish3000/Creditcard-Fraud-Detection)](https://github.com/Sibikrish3000/Creditcard-Fraud-Detection)
+
+Please feel free to explore and utilize these resources for credit card fraud detection purposes.
+
+### [@Sibi Krishnamoorthy](https://sibikrish3000.github.io/portfolio/)
+___
+
+<h5 align="center">
+Sibi Krishnamoorthy
+</h5><p align="center">
+A Data Science student with a passion for Machine Learning and Artificial intelligence
+</p><p style="color:teal" align="center">
+&copy Sibikrish. All rights reserved 2024
+</p>
+
+

app.py

@@ -0,0 +1,71 @@
+from fastapi import FastAPI,Query
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+import warnings
+warnings.filterwarnings('ignore')
+import joblib
+import uvicorn
+
+app = FastAPI(title="Credit Card Fraud Detection API",
+    description="""An API that utilises a Machine Learning model that detects a credit card transaction fraudulent""",
+    version="1.0.0", debug=True)
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+xgb_model = joblib.load('./model/xgb_model.pkl')
+rf_model = joblib.load('./model/rf_model.pkl')
+enc = joblib.load('./Encoder/WOEEncoder.pkl')
+
+
+@app.get('/',response_class=HTMLResponse)
+def running():
+    text='''
+    <html>
+    <head>
+    <link rel="icon" type="image/x-icon" href="static/images/api.png">
+    <title>Credit Card Fraud Detection API</title>
+    </head>
+    <body>
+    <div>
+    <h1>Credit Card Fraud Detection API</h1>
+        <a href="https://github.com/Sibikrish3000/">Github repository</a>
+    </div>
+    </body>
+    </html>
+    '''
+    return text
+
+class fraudinput(BaseModel):
+    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:int
+    amt:float
+@app.post('/predict')
+async def predict(data: fraudinput,model:str =Query(...)):
+    print('data: %s' % data)
+    data=data.dict()
+    enc_data=enc.transform([data])
+    print('model:'+model)
+    if model == 'xgboost':
+        prediction=xgb_model.predict(enc_data)
+    elif model == 'randomforest':
+        prediction=rf_model.predict(enc_data)
+    else:
+        return {'error': 'Invalid model selected'}
+    print("prediction:",prediction[0])
+    return {"prediction":int(prediction[0])}
+
+#if __name__ == '__main__':
+    #uvicorn.run(app, host='127.0.0.1', port=8000)
+
+
+
+    #uvicorn.run(app, host="0.0.0.0", port=8000)
+
+

docker-compose.yml

@@ -0,0 +1,25 @@
+version: '3.8'
+
+services:
+  gradio:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "7860:7860"
+    environment:
+      - PYTHONUNBUFFERED=1
+    command: python gradio_app.py
+  api:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "8000:8000"
+    environment:
+      - PYTHONUNBUFFERED=1
+    command: uvicorn app:app --host 0.0.0.0 --port 8000
+<<<<<<< Updated upstream
+=======
+
+>>>>>>> Stashed changes

docker_app.py

@@ -0,0 +1,165 @@
+import time
+import gradio as gr
+import requests
+from features import category_names,job_names
+from PIL import Image
+#import uvicorn
+#import threading
+#from app import app
+#import psutil
+
+
+def close_port(port):
+    for conn in psutil.net_connections(kind='inet'):
+        if conn.laddr.port == port:
+            print(f"Closing port {port} by terminating PID {conn.pid}")
+            process = psutil.Process(conn.pid)
+            process.terminate()
+
+
+"""
+def run_fastapi():
+    try:
+        uvicorn.run(app, host="0.0.0.0", port=8000)
+    except Exception as e:
+        print(f'Error running fastapi:{e}')
+        close_port(8000)
+
+
+fastapi_thread = threading.Thread(target=run_fastapi)
+fastapi_thread.daemon = True
+fastapi_thread.start()
+time.sleep(2)
+"""
+
+def predict_fraud(cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt, model):
+    def map_time_of_day(hour):
+        if 0 <= hour <= 4:
+            return 'night'
+        elif 5 <= hour <= 11:
+            return 'morning'
+        elif 12 <= hour <= 20:
+            return 'afternoon'
+        else:
+            return 'night'
+
+    def cc_freq_classes(x):
+        for idx, val in enumerate(list(range(800, 10000, 800))):
+            if x < val:
+                return idx + 1
+
+    cc_freq_class=cc_freq_classes(cc_freq)
+    hour = map_time_of_day(hour)
+
+    input_data = {
+        'cc_freq': cc_freq,
+        'cc_freq_class': cc_freq_class,
+        'job': job,
+        'age': age,
+        'gender_M': 1 if gender_M == 'Male' else 0,
+        'category': category,
+        'distance_km': distance_km,
+        'hour': hour,
+        'hours_diff_bet_trans': hours_diff_bet_trans,
+        'amt': amt
+    }
+
+    try:
+        response = requests.post(f'http://0.0.0.0:8000/predict?model={model.lower()}', json=input_data)
+        response.raise_for_status()
+        if response.status_code == 200:
+            prediction = response.json()
+            return 'This Transaction is legitimate.' if prediction['prediction'] == 0 else 'This Transaction is not legitimate.'
+    except requests.exceptions.RequestException as e:
+        return f"Error: {e}"
+
+# Define the input components
+theme = gr.themes.Base(
+    primary_hue="teal",
+    neutral_hue="sky",
+    radius_size="lg",
+).set(
+    body_text_weight='300',
+    shadow_drop_lg='*button_shadow_hover',
+    shadow_inset='*shadow_drop_lg'
+)
+
+js= """
+function createGradioAnimation() {
+    var container = document.createElement('div');
+    container.id = 'gradio-animation';
+    container.style.fontSize = '2em';
+    container.style.fontWeight = 'bold';
+    container.style.textAlign = 'center';
+    container.style.marginBottom = '20px';
+
+    var text = 'Credit Card Fraud Detection';
+    var totalDuration = 2000; // Total duration for the whole animation
+    var animationDelay = totalDuration / text.length; // Delay between each letter animation
+
+    for (var i = 0; i < text.length; i++) {
+        (function(i){
+            setTimeout(function(){
+                var letter = document.createElement('span');
+                letter.style.opacity = '0';
+                letter.style.transition = 'opacity 0.7s ease-in-out'; // Smoother transition
+                letter.innerText = text[i];
+
+                container.appendChild(letter);
+
+                setTimeout(function() {
+                    letter.style.opacity = '1';
+                }, 50);
+            }, i * animationDelay); // Use calculated delay
+        })(i);
+    }
+
+    var gradioContainer = document.querySelector('.gradio-container');
+    gradioContainer.insertBefore(container, gradioContainer.firstChild);
+
+    return 'Animation created';
+}
+
+"""
+
+callback = gr.CSVLogger()
+default_img=Image.open('static/images/creditcard.jpg')
+with gr.Blocks(theme=theme,js=js) as interface:
+
+    gr.Image( value=default_img,show_download_button=False)
+    with gr.Tab('predict',):
+        with gr.Row():
+            with gr.Column():
+                cc_freq = gr.Number(label="Credit Card Frequency")
+                job = gr.Dropdown(job_names, label="Job")
+                age = gr.Slider(minimum=0, maximum=100, step=1, label="Age")
+                gender_M = gr.Radio(['Male', 'Female'], label="Gender")
+                category = gr.Dropdown(category_names, label="Category")
+                distance_km = gr.Number(label="Distance (km)")
+                hour = gr.Slider(minimum=0, maximum=24, step=1, label="Hour")
+                hours_diff_bet_trans = gr.Number(label="Hours Difference Between Transactions")
+                amt = gr.Number(label="Amount")
+                model_choice = gr.Radio(['XGBoost', 'RandomForest'], label="Choose Model", )
+            with gr.Column():
+                output = gr.Label(label="Prediction")
+                with gr.Row():
+                    predict_button = gr.Button("Predict")
+                    flag_button = gr.Button('Flag')
+
+        callback.setup([cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt, model_choice],
+                       "log")
+
+        predict_button.click(fn=predict_fraud,
+                             inputs=[cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt,
+                                     model_choice], outputs=output)
+        flag_button.click(lambda *args: callback.flag(args),
+                          [cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt,
+                           model_choice], None, preprocess=False)
+    with gr.Tab('About'):
+        with open('about.md', 'r') as about:
+            gr.Markdown(about.read(),line_breaks=True,header_links=True)
+
+
+
+if __name__ == "__main__":
+    interface.launch(share=True)

features.py

@@ -0,0 +1,226 @@
+__all__ =['category_names','job_names']
+category_names=['misc_net', 'gas_transport', 'kids_pets', 'home', 'shopping_net',
+       'food_dining', 'personal_care', 'grocery_pos', 'entertainment',
+       'shopping_pos', 'misc_pos', 'travel', 'health_fitness',
+       'grocery_net']
+
+job_names=['Information systems manager', 'Secondary school teacher',
+       "Barrister's clerk", "Politician's assistant",
+       'Horticulturist, commercial', 'Programmer, applications',
+       'Television camera operator', 'Call centre manager',
+       'Chief Marketing Officer', 'Buyer, industrial',
+       'Armed forces logistics/support/administrative officer',
+       'Pilot, airline', 'Therapist, horticultural', 'Dancer',
+       'Ceramics designer', 'Naval architect', 'Engineer, agricultural',
+       'Health service manager', 'Sport and exercise psychologist',
+       'Forensic psychologist', 'Exhibition designer',
+       "Nurse, children's", 'Soil scientist', 'Prison officer',
+       'Economist', 'Copywriter, advertising', 'Television floor manager',
+       'Lecturer, higher education', 'Sales professional, IT',
+       'Exhibitions officer, museum/gallery', 'Freight forwarder',
+       'Engineering geologist', 'Surveyor, hydrographic',
+       'Trade mark attorney', 'Public relations account executive',
+       'Engineer, site', 'Race relations officer', 'Podiatrist',
+       'Clothing/textile technologist', 'Retail merchandiser',
+       'Engineer, civil (contracting)', 'Make',
+       'Advertising account executive',
+       'Armed forces training and education officer',
+       'Special educational needs teacher', 'Special effects artist',
+       'Corporate investment banker', 'Immunologist', 'Barrister',
+       'Designer, interior/spatial', 'Early years teacher',
+       'Chief Executive Officer', 'Mechanical engineer',
+       'Administrator, education', 'Engineer, electronics',
+       'Estate manager/land agent', 'Television/film/video producer',
+       'Chiropodist', 'Senior tax professional/tax inspector',
+       'Airline pilot', 'Engineer, petroleum', 'Materials engineer',
+       'Civil engineer, contracting', 'Sales executive',
+       'Editor, film/video', 'Marketing executive',
+       'Historic buildings inspector/conservation officer',
+       'Higher education careers adviser', 'Chief Financial Officer',
+       'Editor, commissioning', 'Financial trader', 'Animal nutritionist',
+       'Further education lecturer', 'Clinical research associate',
+       'Writer', 'Air traffic controller', 'Engineer, automotive',
+       'Tourism officer', 'Designer, industrial/product',
+       'Broadcast presenter', 'Quarry manager',
+       'Careers information officer', 'Cartographer',
+       'Petroleum engineer', 'Aid worker', 'Database administrator',
+       'Technical brewer', 'Educational psychologist',
+       'Engineer, building services', 'Agricultural consultant',
+       'Scientist, biomedical', 'Probation officer',
+       'Conservation officer, historic buildings', 'Physiotherapist',
+       'Equality and diversity officer', 'Quantity surveyor',
+       'Occupational hygienist', 'Academic librarian', 'Air broker',
+       'Catering manager', 'Licensed conveyancer', 'Pharmacist, hospital',
+       'Television production assistant', 'Insurance underwriter',
+       'English as a second language teacher', 'Music tutor',
+       'Programmer, multimedia', 'Therapist, art', 'Financial adviser',
+       'Media planner', 'Land/geomatics surveyor', 'Architect',
+       'Set designer', 'Lawyer', 'Administrator, arts', 'Tax adviser',
+       'Drilling engineer', 'Administrator',
+       'Community education officer', 'IT trainer', 'IT consultant',
+       'Journalist, newspaper', 'Psychologist, forensic', 'Site engineer',
+       'Curator', 'Film/video editor', 'Investment banker, corporate',
+       'Geochemist', 'Professor Emeritus', 'Chemical engineer',
+       'Fine artist', 'Pension scheme manager', 'Systems developer',
+       'Firefighter', 'Learning disability nurse', 'Network engineer',
+       'Sales promotion account executive', 'Child psychotherapist',
+       'Building surveyor', 'Energy engineer', 'Human resources officer',
+       'Metallurgist', 'Pharmacist, community', 'Tour manager',
+       'Paramedic', 'Arboriculturist', 'Dispensing optician',
+       'Engineer, production', 'Engineer, control and instrumentation',
+       'Embryologist, clinical', 'Paediatric nurse', 'Radio producer',
+       'Applications developer', 'Local government officer',
+       'Chief Strategy Officer', 'Sub', 'Orthoptist', 'Lexicographer',
+       'Press sub', 'Chief Operating Officer', 'Geoscientist',
+       'Environmental consultant', 'Commissioning editor',
+       'Cytogeneticist', 'Chartered legal executive (England and Wales)',
+       'Civil Service fast streamer', 'Presenter, broadcasting',
+       'Fitness centre manager', 'Development worker, community',
+       'Wellsite geologist', 'Ambulance person', 'Psychotherapist',
+       'Gaffer', 'Radio broadcast assistant',
+       'Training and development officer', 'Seismic interpreter',
+       'Counsellor', 'Librarian, public', 'Personnel officer',
+       'Magazine features editor', 'Horticultural consultant',
+       'Buyer, retail', 'Aeronautical engineer',
+       'Education officer, museum', 'Scientist, research (medical)',
+       'Toxicologist', 'Town planner', 'Scientist, research (maths)',
+       'Jewellery designer', 'Hotel manager', 'Facilities manager',
+       'Plant breeder/geneticist', 'Civil Service administrator',
+       'Occupational therapist', 'Diagnostic radiographer',
+       'Conservator, museum/gallery', 'Intelligence analyst',
+       'Warden/ranger', 'Animal technologist', 'Furniture designer',
+       'Claims inspector/assessor', 'Industrial/product designer',
+       'Glass blower/designer', 'Armed forces technical officer',
+       'Nature conservation officer', 'Manufacturing engineer',
+       'Exercise physiologist', 'Designer, textile',
+       'Geophysicist/field seismologist', 'Surveyor, land/geomatics',
+       'Risk analyst', 'Development worker, international aid',
+       'Audiological scientist', 'Hydrologist', 'Engineer, land',
+       'Engineer, biomedical', 'Teacher, secondary school',
+       'Building control surveyor',
+       'English as a foreign language teacher', 'Leisure centre manager',
+       'Emergency planning/management officer', 'Chartered accountant',
+       'Chartered public finance accountant', 'Public affairs consultant',
+       'Control and instrumentation engineer',
+       'Trading standards officer', 'Archaeologist',
+       'Designer, exhibition/display', 'Engineer, maintenance',
+       'Insurance broker', 'Research scientist (physical sciences)',
+       'Science writer', 'Psychiatrist', 'Video editor',
+       'Horticultural therapist', 'Advertising account planner',
+       'Music therapist', 'Physiological scientist',
+       'Teacher, primary school', 'Doctor, general practice',
+       'Physicist, medical', 'Dealer', 'Therapist, occupational',
+       'Futures trader', 'Accountant, chartered public finance',
+       'Electrical engineer', 'Commercial horticulturist',
+       'Pensions consultant', 'Records manager', 'Doctor, hospital',
+       'Engineer, broadcasting (operations)', 'Colour technologist',
+       'Environmental health practitioner', 'Theatre manager',
+       'Comptroller', 'Optician, dispensing',
+       'Social research officer, government',
+       'Production assistant, radio', 'Community pharmacist',
+       'Production engineer', 'Regulatory affairs officer',
+       'Statistician', 'Administrator, local government',
+       'Telecommunications researcher', 'Research scientist (medical)',
+       'Administrator, charities/voluntary organisations',
+       'General practice doctor', 'Rural practice surveyor',
+       'Education administrator', 'Multimedia programmer',
+       'Heritage manager', 'TEFL teacher', 'Phytotherapist',
+       'Museum education officer', 'Designer, ceramics/pottery',
+       'Mudlogger', 'Police officer', 'Research officer, trade union',
+       'Retail buyer', 'Accounting technician',
+       'Dance movement psychotherapist', 'Theatre director',
+       'Waste management officer', 'Web designer', 'Nurse, mental health',
+       'Herpetologist', 'Tourist information centre manager',
+       'Radiographer, therapeutic', 'Designer, jewellery',
+       'Scientist, physiological', 'Investment analyst',
+       'Insurance risk surveyor', 'Teacher, special educational needs',
+       'Product designer', 'Librarian, academic', 'Mining engineer',
+       'Stage manager', 'Investment banker, operational',
+       'Geneticist, molecular', 'Medical physicist',
+       'Product/process development scientist',
+       'Arts development officer', 'Charity fundraiser', 'Land',
+       'Teaching laboratory technician', 'Acupuncturist',
+       'Transport planner', 'Forest/woodland manager', 'Charity officer',
+       'Herbalist', 'Industrial buyer', 'Medical sales representative',
+       'Landscape architect', 'Teacher, early years/pre',
+       'Event organiser', 'Conservator, furniture',
+       'Hydrographic surveyor', 'Producer, radio',
+       'Psychologist, counselling', 'Designer, furniture',
+       'Theme park manager', 'Hospital pharmacist',
+       'Volunteer coordinator', 'Pathologist',
+       'Psychologist, sport and exercise', 'Health visitor',
+       'Programme researcher, broadcasting/film/video',
+       'Counselling psychologist', 'Operations geologist',
+       'Chief of Staff', 'Engineer, communications', 'Engineer, mining',
+       'Biochemist, clinical',
+       'Scientist, clinical (histocompatibility and immunogenetics)',
+       'Medical secretary', 'Hospital doctor', 'Location manager',
+       'Mental health nurse', 'Scientist, audiological',
+       'Careers adviser', 'Therapist, drama', 'Accountant, chartered',
+       'Social researcher', 'Solicitor', 'Travel agency manager',
+       'Scientist, marine', 'Artist', 'Surveyor, minerals',
+       'Patent attorney', 'Chartered loss adjuster',
+       'Scientific laboratory technician',
+       'Interior and spatial designer', 'Medical technical officer',
+       'Broadcast engineer', 'Fisheries officer', 'Engineer, water',
+       'Commercial/residential surveyor', 'Engineer, structural',
+       'Communications engineer',
+       'Teacher, English as a foreign language', 'Magazine journalist',
+       'Community development worker', 'Legal secretary', 'Optometrist',
+       'Psychologist, clinical', 'Software engineer', 'Oncologist',
+       'Research officer, political party', 'Information officer',
+       'Cabin crew', 'Geologist, engineering',
+       'Outdoor activities/education manager', 'Textile designer',
+       'Learning mentor', 'Minerals surveyor', 'Clinical biochemist',
+       'Maintenance engineer', 'Market researcher', 'Health physicist',
+       'Biomedical engineer', 'Associate Professor',
+       'Electronics engineer', 'Engineer, civil (consulting)',
+       'Contractor', 'Engineer, materials', 'Visual merchandiser',
+       'Production manager', 'Structural engineer',
+       'Amenity horticulturist', 'Osteopath', 'Press photographer',
+       'Purchasing manager', 'Camera operator', 'Advice worker',
+       'Insurance claims handler', 'Furniture conservator/restorer',
+       'Manufacturing systems engineer', 'Copy',
+       'Lecturer, further education', 'Animator', 'Surveyor, mining',
+       'Retail manager', 'Therapist, music', 'Primary school teacher',
+       'Archivist', 'Health and safety adviser',
+       'Research scientist (life sciences)', 'Veterinary surgeon',
+       'Data scientist', 'Media buyer',
+       'Museum/gallery exhibitions officer',
+       'Health promotion specialist', 'Farm manager', 'Warehouse manager',
+       'Surgeon', 'Illustrator', 'Biomedical scientist',
+       'Logistics and distribution manager', 'Geologist, wellsite',
+       'Operational investment banker', 'Private music teacher',
+       'Occupational psychologist', 'Building services engineer',
+       'Bookseller', 'Engineer, technical sales', 'Neurosurgeon',
+       'Museum/gallery conservator', 'Garment/textile technologist',
+       'Systems analyst', 'Therapist, sports', 'Art gallery manager',
+       'Retail banker', 'Radiographer, diagnostic',
+       'Editor, magazine features', 'Teacher, adult education',
+       'Water engineer', 'Clinical cytogeneticist', 'Air cabin crew',
+       'Oceanographer', 'Research scientist (maths)',
+       'Psychotherapist, child', 'Public house manager',
+       'Chief Technology Officer', 'Planning and development surveyor',
+       'Pharmacologist', 'Scientist, research (physical sciences)',
+       'Ecologist', 'Tree surgeon', 'Engineer, drilling', 'Art therapist',
+       'Architectural technologist', 'Equities trader',
+       'Production assistant, television', 'Field seismologist',
+       'Musician', 'Designer, multimedia', 'Nutritional therapist',
+       'Designer, television/film set', 'Tax inspector',
+       'Public librarian', 'Loss adjuster, chartered',
+       'Accountant, chartered certified', 'Merchandiser, retail',
+       'Sports development officer', 'Interpreter', 'Homeopath',
+       'Engineer, aeronautical', 'Analytical chemist', 'Energy manager',
+       'Community arts worker', 'Management consultant',
+       'Public relations officer', 'Ship broker', 'Chemist, analytical',
+       'Environmental manager', 'Product manager',
+       'Education officer, community', 'Field trials officer',
+       'Restaurant manager, fast food', 'Broadcast journalist',
+       'Secretary/administrator', 'Company secretary',
+       'Sports administrator', 'Producer, television/film/video',
+       'Barista', 'Advertising copywriter', 'Immigration officer',
+       'Hydrogeologist', 'Clinical psychologist', 'Solicitor, Scotland',
+       'Contracting civil engineer', 'Engineer, manufacturing',
+       'Psychiatric nurse', 'Water quality scientist',
+       'Surveyor, rural practice', 'Environmental education officer',
+       'Operational researcher']

gradio_app.py

@@ -0,0 +1,164 @@
+import time
+import threading
+import uvicorn
+import gradio as gr
+import requests
+from features import job_names,category_names
+from app import app
+import psutil
+from PIL import Image
+
+
+def close_port(port):
+    for conn in psutil.net_connections(kind='inet'):
+        if conn.laddr.port == port:
+            print(f"Closing port {port} by terminating PID {conn.pid}")
+            process = psutil.Process(conn.pid)
+            process.terminate()
+
+
+def run_fastapi():
+    try:
+        uvicorn.run(app, host="0.0.0.0", port=8000)
+    except Exception as e:
+        print(f'Error running fastapi:{e}')
+        close_port(8000)
+
+
+fastapi_thread = threading.Thread(target=run_fastapi)
+fastapi_thread.daemon = True
+fastapi_thread.start()
+time.sleep(2)
+
+def predict_fraud(cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt, model):
+    def map_time_of_day(hour):
+        if 0 <= hour <= 4:
+            return 'night'
+        elif 5 <= hour <= 11:
+            return 'morning'
+        elif 12 <= hour <= 20:
+            return 'afternoon'
+        else:
+            return 'night'
+
+    def cc_freq_classes(x):
+        for idx, val in enumerate(list(range(800, 10000, 800))):
+            if x < val:
+                return idx + 1
+
+    cc_freq_class=cc_freq_classes(cc_freq)
+    hour = map_time_of_day(hour)
+
+    input_data = {
+        'cc_freq': cc_freq,
+        'cc_freq_class': cc_freq_class,
+        'job': job,
+        'age': age,
+        'gender_M': 1 if gender_M == 'Male' else 0,
+        'category': category,
+        'distance_km': distance_km,
+        'hour': hour,
+        'hours_diff_bet_trans': hours_diff_bet_trans,
+        'amt': amt
+    }
+
+    try:
+        response = requests.post(f'http://0.0.0.0:8000/predict?model={model.lower()}', json=input_data)
+        response.raise_for_status()
+        if response.status_code == 200:
+            prediction = response.json()
+            return 'This Transaction is legitimate.' if prediction['prediction'] == 0 else 'This Transaction is not legitimate.'
+    except requests.exceptions.RequestException as e:
+        return f"Error: {e}"
+
+theme = gr.themes.Base(
+    primary_hue="teal",
+    neutral_hue="sky",
+    radius_size="lg",
+).set(
+    body_text_weight='300',
+    shadow_drop_lg='*button_shadow_hover',
+    shadow_inset='*shadow_drop_lg'
+)
+
+js= """
+function createGradioAnimation() {
+    var container = document.createElement('div');
+    container.id = 'gradio-animation';
+    container.style.fontSize = '2em';
+    container.style.fontWeight = 'bold';
+    container.style.textAlign = 'center';
+    container.style.marginBottom = '20px';
+
+    var text = 'Credit Card Fraud Detection';
+    var totalDuration = 2000; // Total duration for the whole animation
+    var animationDelay = totalDuration / text.length; // Delay between each letter animation
+
+    for (var i = 0; i < text.length; i++) {
+        (function(i){
+            setTimeout(function(){
+                var letter = document.createElement('span');
+                letter.style.opacity = '0';
+                letter.style.transition = 'opacity 0.7s ease-in-out'; // Smoother transition
+                letter.innerText = text[i];
+
+                container.appendChild(letter);
+
+                setTimeout(function() {
+                    letter.style.opacity = '1';
+                }, 50);
+            }, i * animationDelay); // Use calculated delay
+        })(i);
+    }
+
+    var gradioContainer = document.querySelector('.gradio-container');
+    gradioContainer.insertBefore(container, gradioContainer.firstChild);
+
+    return 'Animation created';
+}
+
+"""
+
+callback = gr.CSVLogger()
+default_img=Image.open('static/images/creditcard.jpg')
+with gr.Blocks(theme=theme,js=js) as interface:
+
+    gr.Image( value=default_img,show_download_button=False)
+    with gr.Tab('predict',):
+        with gr.Row():
+            with gr.Column():
+                cc_freq = gr.Number(label="Credit Card Frequency")
+                job = gr.Dropdown(job_names, label="Job")
+                age = gr.Slider(minimum=0, maximum=100, step=1, label="Age")
+                gender_M = gr.Radio(['Male', 'Female'], label="Gender")
+                category = gr.Dropdown(category_names, label="Category")
+                distance_km = gr.Number(label="Distance (km)")
+                hour = gr.Slider(minimum=0, maximum=24, step=1, label="Hour")
+                hours_diff_bet_trans = gr.Number(label="Hours Difference Between Transactions")
+                amt = gr.Number(label="Amount")
+                model_choice = gr.Radio(['XGBoost', 'RandomForest'], label="Choose Model", )
+            with gr.Column():
+                output = gr.Label(label="Prediction")
+                with gr.Row():
+                    predict_button = gr.Button("Predict")
+                    flag_button = gr.Button('Flag')
+
+        callback.setup([cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt, model_choice],
+                       "log")
+
+        predict_button.click(fn=predict_fraud,
+                             inputs=[cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt,
+                                     model_choice], outputs=output)
+        flag_button.click(lambda *args: callback.flag(args),
+                          [cc_freq, job, age, gender_M, category, distance_km, hour, hours_diff_bet_trans, amt,
+                           model_choice], None, preprocess=False)
+    with gr.Tab('About'):
+        with open('about.md', 'r') as about:
+            gr.Markdown(about.read(),line_breaks=True,header_links=True)
+
+
+
+if __name__ == "__main__":
+    interface.launch(share=True)
+
+    fastapi_thread.join()

log/log.csv

@@ -0,0 +1,15 @@
+Credit Card Frequency,Job,Age,Gender,Category,Distance (km),Hour,Hours Difference Between Transactions,Amount,Choose Model,flag,username,timestamp
+0,,0,,,0,0,0,0,,,,2024-06-25 21:41:43.449715
+0,,0,,,0,0,0,0,,,,2024-06-25 21:41:44.328213
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+75,Secondary school teacher,54,Female,shopping_net,75,9,90,20,RandomForest,,,2024-06-25 21:42:49.917615
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+77,Television camera operator,37,Female,travel,888,24,90,9999,XGBoost,,,2024-06-26 13:59:42.520861
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model/rf_model.pkl

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model/xgb_model.pkl

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requirements.txt

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+pillow
+fastapi
+uvicorn
+gradio
+requests
+psutil
+numpy
+joblib
+xgboost
+category-encoders
+scikit-learn