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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)
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