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