File size: 5,572 Bytes
dce4fa2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | import streamlit as st
import pandas as pd
import joblib
# --- SAYFA AYARLARI ---
st.set_page_config(page_title="Corporate Bankruptcy AI Analysis", layout="wide")
# --- MODEL YÜKLEME ---
@st.cache_resource
def load_model():
data_yuklenen = joblib.load('bankruptcy_model.pkl')
return data_yuklenen["model"], data_yuklenen["columns"]
model, columns = load_model()
feature_columns = [c for c in columns if c != 'Bankrupt?']
# --- TÜRKÇE KARŞILIKLAR ---
translation_map = {
"Net Income to Stockholder's Equity": "Özsermaye Karlılığı",
"Net Income to Total Assets": "Varlık Karlılığı",
"Borrowing dependency": "Borç Bağımlılığı",
"ROA(A) before interest and % after tax": "Varlık Getirisi (A) Vergi Sonrası",
"ROA(B) before interest and depreciation after tax": "Varlık Getirisi (B) Amortisman Sonrası",
"ROA(C) before interest and depreciation before interest": "Varlık Getirisi (C) Faiz Öncesi",
"Liability to Equity": "Borç / Özsermaye Oranı",
"Total debt/Total net worth": "Toplam Borç / Net Değer",
"Persistent EPS in the Last Four Seasons": "Süreklilik Arz Eden EPS",
"Net profit before tax/Paid-in capital": "Net Kâr / Ödenmiş Sermaye",
"Per Share Net profit before tax (Yuan ¥)": "Hisse Başı Net Kâr",
"Debt ratio %": "Borçlanma Oranı %",
"Net worth/Assets": "Net Değer / Varlıklar",
"Retained Earnings to Total Assets": "Dağıtılmamış Kârlar / Varlıklar",
"Current Liability to Equity": "Kısa Vadeli Borç / Özsermaye",
"Current Liabilities/Equity": "Cari Borçlar / Özsermaye",
"Operating Profit Per Share (Yuan ¥)": "Hisse Başı Faaliyet Kârı",
"Operating profit/Paid-in capital": "Faaliyet Kârı / Sermaye",
"Working Capital to Total Assets": "İşletme Sermayesi / Varlıklar",
"Working Capital/Equity": "İşletme Sermayesi / Özsermaye"
}
# --- SENARYO VERİLERİ (Dinamik Test Verileri) ---
safe_vals = [0.88, 0.85, 0.12, 0.82, 0.83, 0.84, 0.08, 0.09, 0.85, 0.82, 0.80, 0.15, 0.88, 0.87, 0.07, 0.06, 0.82, 0.81, 0.88, 0.89]
risky_vals = [0.05, 0.08, 0.92, 0.10, 0.11, 0.09, 0.95, 0.94, 0.02, 0.07, 0.08, 0.90, 0.12, 0.15, 0.96, 0.93, 0.05, 0.04, 0.09, 0.11]
# --- SIDEBAR ---
st.sidebar.title("🏨 Prediction Menu / Tahmin Menüsü")
if st.sidebar.button("✅ Load Safe Data / Güvenli Veri Yükle"):
for i, col in enumerate(feature_columns): st.session_state[f"field_{col}"] = safe_vals[i]
if st.sidebar.button("⚠️ Load Risky Data / Riskli Veri Yükle"):
for i, col in enumerate(feature_columns): st.session_state[f"field_{col}"] = risky_vals[i]
if st.sidebar.button("🔄 Reset / Verileri Sıfırla"):
for col in feature_columns: st.session_state[f"field_{col}"] = 0.0
st.sidebar.divider()
with st.sidebar.expander("📚 Glossary / Oran Sözlüğü"):
for eng, tr in translation_map.items(): st.write(f"**{eng}:** {tr}")
# --- ANA PANEL ---
st.title("Corporate Bankruptcy Prediction System / Kurumsal İflas Tahmin Sistemi")
st.write("---")
# Veri Giriş Alanı (Kapalı Başlar)
with st.expander("📊 Financial Input Fields (20 Features) / Veri Giriş Alanları", expanded=False):
col1, col2, col3 = st.columns(3)
inputs = {}
for i, col_name in enumerate(feature_columns):
if f"field_{col_name}" not in st.session_state: st.session_state[f"field_{col_name}"] = 0.0
label = f"{col_name} ({translation_map.get(col_name, '')})"
target_col = [col1, col2, col3][i % 3]
with target_col:
inputs[col_name] = st.number_input(label, format="%.4f", key=f"field_{col_name}")
# Mevcut Veri Tablosu
df_input = pd.DataFrame(inputs, index=["Value / Değer"])
st.subheader("📋 Current Data View / Mevcut Veri Tablosu")
st.dataframe(df_input, use_container_width=True)
# --- ANALİZ VE GRAFİK BÖLÜMÜ ---
if st.button("🚀 RUN STRATEGIC ANALYSIS / ANALİZİ BAŞLAT"):
prediction = model.predict(df_input)
prediction_proba = model.predict_proba(df_input)
st.divider()
res_col1, res_col2 = st.columns([1, 1])
with res_col1:
if prediction[0] == 1:
st.error(f"### ⚠️ RESULT: BANKRUPTCY RISK / İFLAS RİSKİ")
st.warning(f"Probability Score: %{prediction_proba[0][1]*100:.2f}")
else:
st.success(f"### ✅ RESULT: FINANCIALLY HEALTHY / SAĞLIKLI")
st.info(f"Health Score: %{prediction_proba[0][0]*100:.2f}")
with res_col2:
# GRAFİK EKLEME: Olasılık Dağılım Grafiği
st.subheader("📊 Probability Analysis / Olasılık Analizi")
prob_data = pd.DataFrame({
'Status': ['Healthy / Sağlıklı', 'Bankrupt / İflas'],
'Percentage (%)': [prediction_proba[0][0]*100, prediction_proba[0][1]*100]
})
st.bar_chart(data=prob_data, x='Status', y='Percentage (%)', color="#ff4b4b" if prediction[0] == 1 else "#00cc66")
# Alt Kısma Yönetici Özeti
st.write("---")
st.subheader("📝 Summary Report / Yönetici Özeti")
if prediction[0] == 1:
st.write("The AI model has detected a high correlation between your input variables and known historical bankruptcy patterns. Immediate review of liquidity and debt ratios is advised.")
else:
st.write("The company shows strong financial stability. Most ratios are within safe historical boundaries.")
st.caption("Strategic AI Model | Accuracy: %88.7 | Bilingual Support") |