| import streamlit as st |
| import pandas as pd |
| import os |
| from app.utils.data_loader import load_artifacts |
| from app.models.escalation import escalation_decision |
| from app.models.interpretation import ( |
| interpret_lr, plot_feature_importance_sns, |
| plot_marginal_effects_sns, plot_shap_analysis, |
| get_feature_display_name |
| ) |
| from app.utils.credit_preprocessor import check_business_rules |
|
|
| |
| PROJECT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) |
| MODELS_PATH = os.path.join(PROJECT_PATH, 'models/best/train_150/') |
| PREPROCESSOR_PATH = os.path.join(PROJECT_PATH, 'preprocessors/') |
|
|
|
|
| def main(): |
| st.title("🏦 Кредитный скоринг - Анкета") |
|
|
| |
| preprocessor, scaler, models = load_artifacts(MODELS_PATH, PREPROCESSOR_PATH) |
|
|
| |
| if 'stats' not in st.session_state: |
| st.session_state.stats = { |
| 'total': 0, |
| 'manual': 0, |
| 'lr_confident': 0, |
| 'second_used': 0, |
| 'second_confident': 0, |
| 'approved': 0, |
| 'declined': 0 |
| } |
|
|
| if 'step' not in st.session_state: |
| st.session_state.step = 'input' |
|
|
| |
|
|
| if st.session_state.step == 'input': |
| st.header("📋 Анкета заемщика") |
|
|
| with st.form("credit_form"): |
| st.subheader("👤 Личная информация") |
| col1, col2 = st.columns(2) |
| with col1: |
| age = st.number_input("Возраст", 0, 150, 35) |
| with col2: |
| dependents = st.number_input("Иждивенцы", 0, 20, 0) |
|
|
| st.subheader("💰 Ежемесячный доход") |
| income_method = st.radio("Способ указания дохода", ["Слайдер (до 20,000$)", "Точное значение"], |
| horizontal=True) |
|
|
| st.subheader("💳 Ежемесячные платежи") |
| debt_method = st.radio("Способ указания платежей", ["Слайдер (до 10,000$)", "Точное значение"], |
| horizontal=True) |
|
|
| st.subheader("📊 Кредитная история") |
| credit_lines = st.number_input("Открытых кредитов и карт", 0, 100, 5) |
| real_estate = st.number_input("Кредитов под залог недвижимости", 0, 100, 1) |
|
|
| st.subheader("📈 Использование лимитов") |
| util_method = st.radio("Уровень использования", |
| ["Норма (0-100%)", "Овердрафт (100-200%)", "Экстремальный (>200%)"], horizontal=True) |
|
|
| st.subheader("⏱️ Просрочки за последние 2 года") |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| late_30_59 = st.number_input("30-59 дней", 0, 100, 0) |
| with col2: |
| late_60_89 = st.number_input("60-89 дней", 0, 100, 0) |
| with col3: |
| late_90 = st.number_input("90+ дней", 0, 100, 0) |
|
|
| submitted = st.form_submit_button("➡️ Далее: указать точные значения") |
|
|
| if submitted: |
| st.session_state.update({ |
| 'age': age, 'dependents': dependents, 'income_method': income_method, |
| 'debt_method': debt_method, 'credit_lines': credit_lines, |
| 'real_estate': real_estate, 'util_method': util_method, |
| 'late_30_59': late_30_59, 'late_60_89': late_60_89, 'late_90': late_90 |
| }) |
| st.session_state.step = 'values' |
| st.rerun() |
|
|
|
|
| |
|
|
| elif st.session_state.step == 'values': |
| st.header("💰 Укажите точные значения") |
|
|
| with st.form("values_form"): |
| col1, col2 = st.columns(2) |
| with col1: |
| st.subheader("Доход") |
| if st.session_state.income_method == "Слайдер (до 20,000$)": |
| monthly_income = st.slider("Ежемесячный доход ($)", 0, 20000, 5000) |
| else: |
| monthly_income = st.number_input("Ежемесячный доход ($)", 0, 1000000, 5000) |
|
|
| with col2: |
| st.subheader("Платежи") |
| if st.session_state.debt_method == "Слайдер (до 10,000$)": |
| monthly_debt = st.slider("Ежемесячные платежи ($)", 0, 10000, 1500) |
| else: |
| monthly_debt = st.number_input("Ежемесячные платежи ($)", 0, 1000000, 1500) |
|
|
| st.subheader("📈 Использование лимитов") |
| if st.session_state.util_method == "Норма (0-100%)": |
| util_value = st.slider("Процент использования", 0, 100, 20) |
| utilization = util_value / 100 |
| elif st.session_state.util_method == "Овердрафт (100-200%)": |
| util_value = st.slider("Процент использования", 100, 200, 120) |
| utilization = util_value / 100 |
| else: |
| st.warning("Экстремальное использование (>200%) - автоматический ручной разбор") |
| utilization = st.number_input("Процент использования", 200, 1000, 200) / 100 |
|
|
| submitted = st.form_submit_button("✅ Получить решение") |
|
|
| |
| with st.sidebar: |
| st.markdown("---") |
| st.subheader("⚙️ Настройки") |
|
|
| with st.expander("🎯 Пороги уверенности", expanded=False): |
| threshold = st.slider("Порог одобрения", 0.3, 0.7, 0.5, 0.05) |
| lr_margin = st.slider("Отступ LR", 0.2, 0.5, 0.35, 0.05) |
| second_margin = st.slider("Отступ второй модели", 0.2, 0.5, 0.4, 0.05) |
|
|
| with st.expander("🤖 Выбор модели", expanded=False): |
| available_models = [name for name in models.keys() if name != 'Logistic Regression'] |
| second_model_name = st.selectbox("Модель для эскалации", available_models) |
|
|
| with st.expander("📊 Статистика", expanded=False): |
| stats = st.session_state.stats |
| if stats['total'] > 0: |
| st.metric("Всего заявок", stats['total']) |
| st.metric("Ручной разбор", f"{stats['manual'] / stats['total']:.1%}") |
| st.metric("LR уверена", f"{stats['lr_confident'] / stats['total']:.1%}") |
| if stats['second_used'] > 0: |
| st.metric("Вторая модель уверена", |
| f"{stats['second_confident'] / stats['second_used']:.1%}") |
|
|
| if st.button("🔄 Сброс"): |
| st.session_state.stats = {'total': 0, 'manual': 0, 'lr_confident': 0, |
| 'second_used': 0, 'second_confident': 0, |
| 'approved': 0, 'declined': 0} |
| st.rerun() |
| else: |
| st.info("Нет данных") |
|
|
| with st.expander("ℹ️ О проекте", expanded=False): |
| st.markdown(f""" |
| **Модели:** |
| - Logistic Regression |
| - {', '.join(available_models)} |
| |
| **AUC:** 0.8578 (LR), ~0.87 (остальные) |
| """) |
|
|
| st.session_state.threshold = threshold |
| st.session_state.lr_margin = lr_margin |
| st.session_state.second_margin = second_margin |
| st.session_state.second_model_name = second_model_name |
|
|
| if submitted: |
| debt_ratio = monthly_debt / monthly_income if monthly_income > 0 else monthly_debt |
|
|
| |
| input_data = pd.DataFrame([{ |
| 'RevolvingUtilizationOfUnsecuredLines': utilization, |
| 'age': st.session_state.age, |
| 'NumberOfTime30-59DaysPastDueNotWorse': st.session_state.late_30_59, |
| 'DebtRatio': debt_ratio, |
| 'MonthlyIncome': monthly_income, |
| 'NumberOfOpenCreditLinesAndLoans': st.session_state.credit_lines, |
| 'NumberOfTimes90DaysLate': st.session_state.late_90, |
| 'NumberRealEstateLoansOrLines': st.session_state.real_estate, |
| 'NumberOfTime60-89DaysPastDueNotWorse': st.session_state.late_60_89, |
| 'NumberOfDependents': st.session_state.dependents |
| }]) |
|
|
| st.markdown("---") |
|
|
| with st.spinner("🔄 Анализ заявки..."): |
| lr_model = models['Logistic Regression'] |
| second_model = models[second_model_name] |
|
|
| |
| decisions, manual_mask, task = escalation_decision( |
| input_data, |
| lr_model, |
| second_model, |
| second_model_name, |
| threshold=st.session_state.threshold, |
| lr_margins=[st.session_state.lr_margin], |
| second_margins=[st.session_state.second_margin], |
| preprocessor=preprocessor, |
| scaler=scaler |
| ) |
| decision = decisions[0] |
|
|
| |
| processed = preprocessor.transform(input_data) |
| processed_scaled = scaler.transform(processed) |
|
|
| |
| st.session_state.stats['total'] += 1 |
| if decision['needs_review']: |
| st.session_state.stats['manual'] += 1 |
| else: |
| if decision['final_decision'] == 0: |
| st.session_state.stats['approved'] += 1 |
| else: |
| st.session_state.stats['declined'] += 1 |
|
|
| if decision.get('lr_confident', False): |
| st.session_state.stats['lr_confident'] += 1 |
|
|
| if decision.get('second_used', False): |
| st.session_state.stats['second_used'] += 1 |
| if decision.get('second_confident', False): |
| st.session_state.stats['second_confident'] += 1 |
|
|
| |
| st.subheader("🔄 Цепочка принятия решения") |
| for step in decision['decision_path']: |
| st.write(step) |
|
|
| col1, col2 = st.columns(2) |
| with col1: |
| st.markdown("**🏦 Logistic Regression**") |
| st.metric("Вероятность", f"{decision['lr_proba']:.1%}") |
| st.write(f"Отступ: {decision['lr_margin']:.1%}") |
| if decision['lr_confident']: |
| st.success("✅ Уверена") |
| else: |
| st.warning("⚠️ Не уверена") |
|
|
| with col2: |
| st.markdown(f"**⚡ {second_model_name}**") |
| if decision['second_used']: |
| st.metric("Вероятность", f"{decision['second_proba']:.1%}") |
| st.write(f"Отступ: {decision['second_margin']:.1%}") |
| if decision['second_confident']: |
| st.success("✅ Уверен") |
| else: |
| st.warning("⚠️ Не уверен") |
| else: |
| st.info("⏳ Не вызывался") |
|
|
| st.markdown("---") |
| if decision['needs_review']: |
| st.warning("👨💼 **РУЧНОЙ РАЗБОР**") |
| st.info("Модели не уверены - требуется проверка специалистом") |
| else: |
| col1, col2 = st.columns(2) |
| with col1: |
| if decision['final_decision'] == 0: |
| st.success("✅ **КРЕДИТ ОДОБРЕН**") |
| else: |
| st.error("❌ **КРЕДИТ НЕ ОДОБРЕН**") |
| with col2: |
| st.metric("Модель", decision['model_used']) |
|
|
| |
| st.markdown("---") |
| st.subheader("🔍 Детальный анализ: Logistic Regression") |
|
|
| feature_names = processed_scaled.columns.tolist() |
| interpretation = interpret_lr(processed_scaled, lr_model, feature_names) |
|
|
| tab1, tab2 = st.tabs(["📊 Вклад в логит", "📈 Влияние на вероятность"]) |
|
|
| with tab1: |
| st.markdown("🔴 Положительный вклад = ↑ риск, 🟢 Отрицательный = ↓ риск") |
| fig1 = plot_feature_importance_sns(interpretation['logit_contributions']) |
| st.pyplot(fig1) |
|
|
| with st.expander("📋 Все вклады"): |
| display_df = interpretation['logit_contributions'][ |
| ['feature', 'value', 'coefficient', 'logit_contribution']].copy() |
| display_df['Описание'] = display_df['feature'].apply(get_feature_display_name) |
| display_df = display_df[['Описание', 'value', 'coefficient', 'logit_contribution']] |
| display_df.columns = ['Признак', 'Значение', 'Коэф', 'Вклад'] |
| display_df = display_df.round(3) |
| st.dataframe(display_df) |
|
|
| with tab2: |
| st.markdown("🔴 Положительное = фактор ↑ риск, 🟢 Отрицательное = ↓ риск") |
| fig2 = plot_marginal_effects_sns(interpretation['marginal_effects']) |
| st.pyplot(fig2) |
|
|
| with st.expander("📋 Все эффекты"): |
| display_df = interpretation['marginal_effects'][['feature', 'marginal_effect']].copy() |
| display_df['Описание'] = display_df['feature'].apply(get_feature_display_name) |
| display_df = display_df[['Описание', 'marginal_effect']] |
| display_df.columns = ['Признак', 'Влияние'] |
| display_df['Влияние'] = display_df['Влияние'].map('{:.1%}'.format) |
| st.dataframe(display_df) |
|
|
| st.info(f"Итоговая вероятность дефолта (LR): {interpretation['probability']:.1%}") |
|
|
| |
| if decision['second_used'] and second_model_name in ['XGBoost', 'LightGBM', 'Random Forest', 'CatBoost']: |
| plot_shap_analysis(second_model, processed_scaled, feature_names, second_model_name) |
|
|
| |
| if st.button("◀️ Вернуться к выбору способов"): |
| st.session_state.step = 'input' |
| st.rerun() |
|
|
| st.markdown("---") |
| col1, col2, col3 = st.columns([1, 2, 1]) |
| with col2: |
| if st.button("🏠 На главную", use_container_width=True): |
| st.switch_page("main.py") |
|
|
| st.markdown("---") |
| st.caption("🏦 GiveMeSomeCredit - Интерпретируемый кредитный скоринг | Модели: Logistic Regression + выбор") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
