Delete app.py

app.py

@@ -1,82 +0,0 @@
-# -*- coding: utf-8 -*-
-"""app.py
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1HA1-9-ihiCRkIXjb9NYEMkTROJNa_715
-"""
-
-import gradio as gr
-import joblib
-# Load the trained model
-model = joblib.load("loan_classifier.joblib")
-#scaler = joblib.load("std_scaler.bin")
-
-
-def predict_loan_status(
-    int_rate,
-    installment,
-    log_annual_inc,
-    dti,
-    fico,
-    revol_bal,
-    revol_util,
-    inq_last_6mths,
-    delinq_2yrs,
-    pub_rec,
-    installment_to_income_ratio,
-    credit_history,
-):
-    input_dict = {
-        "int.rate": int_rate,
-        "installment": installment,
-        "log.annual.inc": log_annual_inc,
-        "dti": dti,
-        "fico": fico,
-        "revol.bal": revol_bal,
-        "revol.util": revol_util,
-        "inq.last.6mths": inq_last_6mths,
-        "delinq.2yrs": delinq_2yrs,
-        "pub.rec": pub_rec,
-        "installment_to_income_ratio": installment_to_income_ratio,
-        "credit_history": credit_history,
-    }
-    # Convert the dictionary to a 2D array
-    input_array = [list(input_dict.values())]
-    scaled_array = scalar.transform(input_array)
-    prediction = model.predict(scaled_array)[0]
-
-    if prediction == 0:
-        return "Loan fully paid"
-    else:
-        return "Loan not fully paid"
-
-
-inputs = [
-    gr.Slider(0.06, 0.23, step=0.01, label="Interest Rate"),
-    gr.Slider(100, 950, step=10, label="Installment"),
-    gr.Slider(7, 15, step=0.1, label="Log Annual Income"),
-    gr.Slider(0, 40, step=1, label="DTI Ratio"),
-    gr.Slider(600, 850, step=1, label="FICO Score"),
-    gr.Slider(0, 120000, step=1000, label="Revolving Balance"),
-    gr.Slider(0, 120, step=1, label="Revolving Utilization"),
-    gr.Slider(0, 10, step=1, label="Inquiries in Last 6 Months"),
-    gr.Slider(0, 20, step=1, label="Delinquencies in Last 2 Years"),
-    gr.Slider(0, 10, step=1, label="Public Records"),
-    gr.Slider(0, 5, step=0.1, label="Installment to Income Ratio"),
-    gr.Slider(0, 1, step=0.01, label="Credit History"),
-]
-outputs = [gr.Label(num_top_classes=2)]
-
-title = "Loan Approval Classifier"
-description = (
-    "Enter the details of the loan applicant to check if the loan is approved or not."
-)
-gr.Interface(
-    fn=predict_loan_status,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    description=description,
-).launch()