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| import joblib | |
| import numpy as np | |
| import pandas as pd | |
| # MODEL_PATH = "model/dt_clf.joblib" | |
| model = joblib.load('model/dtc.joblib') | |
| FEATURE_NAMES = [ 'Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', | |
| 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', | |
| 'Loan_Amount_Term', 'Credit_History', 'Property_Area'] | |
| def preprocess_input(user_input): | |
| mapping = { | |
| 'Gender': {'Female': 0, 'Male': 1}, | |
| 'Married': {'Yes': 1, 'No': 0}, | |
| 'Education': {'Graduate': 1, 'Not Graduate': 0}, | |
| 'Self_Employed': {'Yes': 1, 'No': 0}, | |
| 'Property_Area': {'Urban': 2, 'Semiurban': 1, 'Rural': 0}, | |
| 'Dependents': {'0': 0, '1': 1, '2': 2, '3': 3, '3+': 4} | |
| } | |
| processed = [] | |
| for feature in FEATURE_NAMES: | |
| val = user_input[feature] | |
| if feature in mapping: | |
| val = mapping[feature][val] | |
| processed.append(val) | |
| return np.array(processed).reshape(1, -1) | |
| def make_prediction(user_input): | |
| try: | |
| processed_input = preprocess_input(user_input) | |
| pred = model.predict(processed_input)[0] | |
| prob = model.predict_proba(processed_input).max() | |
| return pred, prob | |
| except Exception as e: | |
| return f"Error: {str(e)}", 0.0 | |