app.py

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import gradio as gr
# Load data
nexus_bank = pd.read_csv('nexus_bank_dataa.csv')
# Preprocessing
X = nexus_bank[['salary', 'dependents']]
y = nexus_bank['defaulter']
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=90)
# Model training
knn_classifier = KNeighborsClassifier()
knn_classifier.fit(X_train, y_train)
# Prediction function
def predict_defaulter(salary, dependents):
    input_data = [[salary, dependents]]
    knn_predict = knn_classifier.predict(input_data)
    return "Yes! It's a Defaulter" if knn_predict[0] == 1 else "No! It's not a Defaulter"
# Interface
interface = gr.Interface(
    fn=predict_defaulter,
    inputs=["number", "number"],
    outputs="text",
    title="Defaulter Prediction",
    descripation="Predicting Defaulters An intuitive app leveraging machine learning to forecast potential defaulters based on financial attributes. Simply input salary and number of dependents to receive instant predictions. Streamlining risk assessment and decision-making processes in financial domains with just a few clicks."
)
# Launch the interface
interface.launch()