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| # Importing data | |
| import pandas as pd | |
| from sklearn.linear_model import LogisticRegression | |
| import numpy as np | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import VotingClassifier | |
| import joblib,os | |
| import gradio as gr | |
| script_dir=os.path.dirname(os.path.abspath(__file__)) | |
| model_path=os.path.join(script_dir,'model','EnsembleModel.joblib') | |
| Ensemble_Model=joblib.load(model_path) | |
| # Function | |
| def Fraud(payments, min_pay, oneoff_p, purch, balance): | |
| df=pd.DataFrame({'payments':[payments],'minimum_payments':[min_pay], | |
| 'oneoff_purchases':[oneoff_p],'purchases':[purch], | |
| 'balance':[balance]}) | |
| # prob={"Probability of Fraud": round(float(Ensemble_Model.predict_proba(df)[0][1]),4)} | |
| prob= "The probability of fraud is: "+str(round(float(Ensemble_Model.predict_proba(df)[0][1]),4)) | |
| return prob | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Label('Fraud Detector 💰🏧💳', label='Alpha Bank™') | |
| with gr.Row(): | |
| # gr.Image("model/alpha.jpg", scale=2) | |
| gr.Markdown('Share the following monthly information for detecting fraud activity') | |
| with gr.Row(): | |
| interface=[ | |
| gr.Slider(0,60000,label='Payments per month'), | |
| gr.Slider(0,80000,label='Minimum payments per month'), | |
| gr.Slider(0,50000, label='One-time purchases average amount'), | |
| gr.Slider(0,50000, label='Subscription services regular payments '), | |
| gr.Slider(0,25000, label='Balance amount') | |
| ] | |
| with gr.Row(): | |
| predict_but=gr.Button('Analyse activity') | |
| output= gr.Textbox(label='Result') | |
| predict_but.click(fn=Fraud, inputs=interface, outputs=output) | |
| if __name__ == "__main__": | |
| # print("here") | |
| # block1.launch() | |
| demo.launch() | |