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app.py

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+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")

bankruptcy_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5f65fe38e117a28e021d1a4ef10abf70b27bce344af466ac9cace0c1f7aba404
+size 532862