first commit

app.py

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+import pandas as pd
+import numpy as np
+import joblib
+import gradio as gr
+
+gender_mapping = {'Male': 1, 'Female': 0}
+married_mapping = {'Yes': 1, 'No': 0}
+education_mapping = {'Graduate': 1, 'Not Graduate': 0}
+self_employed_mapping = {'Yes': 1, 'No': 0}
+property_area_mapping = {'Urban': 2, 'Semiurban': 1, 'Rural': 0}
+credit_mapping = {'Yes': 1, 'No': 0}
+dependents_mapping = {'0': 0, '1': 1, '2': 2, '3+': 3}
+
+def predict_the_loan(gender, married, dependents, education, self_employed,
+                     applicant_income, coapplicant_income, loan_amount,
+                     loan_amount_term, credit_history, property_area):
+
+    gender = gender_mapping[gender]
+    married = married_mapping[married]
+    education = education_mapping[education]
+    self_employed = self_employed_mapping[self_employed]
+    property_area = property_area_mapping[property_area]
+    dependents = dependents_mapping[dependents]
+    credit_history = credit_mapping[credit_history]
+
+    input_data = pd.DataFrame({
+        'Gender': [gender],
+        'Married': [married],
+        'Dependents': [dependents],
+        'Education': [education],
+        'Self_Employed': [self_employed],
+        'ApplicantIncome': [applicant_income],
+        'CoapplicantIncome': [coapplicant_income],
+        'LoanAmount': [loan_amount],
+        'Loan_Amount_Term': [loan_amount_term],
+        'Credit_History': [credit_history],
+        'Property_Area': [property_area]
+    })
+
+    model = joblib.load("random_forest_model.pkl")
+    prediction = model.predict(input_data)[0]
+
+    return "Approved" if prediction == 1 else "Rejected"
+
+app = gr.Interface(
+    fn=predict_the_loan,
+    inputs=[
+        gr.Dropdown(["Male", "Female"], label="Gender"),
+        gr.Radio(["Yes", "No"], label="Married"),
+        gr.Dropdown(["0", "1", "2", "3+"], label="Dependents"),
+        gr.Dropdown(["Graduate", "Not Graduate"], label="Education"),
+        gr.Radio(["Yes", "No"], label="Self Employed"),
+        gr.Number(label="Applicant Income"),
+        gr.Number(label="Coapplicant Income"),
+        gr.Number(label="Loan Amount"),
+        gr.Number(label="Loan Amount Term"),
+        gr.Radio(["Yes", "No"], label="Credit History"),
+        gr.Dropdown(["Urban", "Semiurban", "Rural"], label="Property Area")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="AI-Powered Loan Approval Prediction System",
+    description="Enter the details and get the prediction"
+)
+
+app.launch()