app.py

from fastapi import FastAPI, File, Query, UploadFile, HTTPException, Form
from fastapi.responses import FileResponse, PlainTextResponse
import uvicorn
import joblib
import numpy as np
from pydantic import BaseModel



app = FastAPI(
    title="Credit Card Fraud Detection API",
    description="""An API that utilises a Machine Learning model that detects if a credit card transaction is fraudulent or not based on the following features: hours, amount, transaction type etc.""",
    version="1.0.0", debug=True)


model = joblib.load('credit_fraud.pkl')

@app.get("/", response_class=PlainTextResponse)
async def running():
  note = """
Credit Card Fraud Detection API 🙌🏻

Note: add "/docs" to the URL to get the Swagger UI Docs or "/redoc"
  """
  return note

favicon_path = 'favicon.png'
@app.get('/favicon.png', include_in_schema=False)
async def favicon():
    return FileResponse(favicon_path)
																	
class fraudDetection(BaseModel):
    step:int
    types:int
    amount:float	
    oldbalanceorig:float	
    newbalanceorig:float	
    oldbalancedest:float	
    newbalancedest:float	
    isflaggedfraud:float


@app.post('/predict')
def predict(data : fraudDetection):
                                                                                                                                                                                                                                
    features = np.array([[data.step, data.types, data.amount, data.oldbalanceorig, data.newbalanceorig, data.oldbalancedest, data.newbalancedest, data.isflaggedfraud]])
    model = joblib.load('credit_fraud.pkl')

    predictions = model.predict(features)
    if predictions == 1:
        return {"fraudulent"}
    elif predictions == 0:
        return {"not fraudulent"}