Update app.py

app.py

@@ -1,124 +1,116 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import pickle
-from tensorflow.keras.models import load_model
-
-# Sayfa Yapılandırması
-st.set_page_config(page_title="Hotel AI Decision Support", layout="wide")
-
-@st.cache_resource
-def load_assets():
-    model = load_model("hotel_model.keras")
-    with open("scaler.pkl", "rb") as f:
-        sc = pickle.load(f)
-    return model, sc
-
-try:
-    model, sc = load_assets()
-except Exception as e:
-    st.error(f"Error: Assets not found! / Hata: Dosyalar bulunamadı! {e}")
-    st.stop()
-
-# ==========================================
-# SIDEBAR / SOL PANEL (REHBER GERİ GELDİ)
-# ==========================================
-with st.sidebar:
-    st.markdown("### 📖 Guide & Info / Rehber")
-    st.info("""
-    **How to Upload? / Nasıl Yüklenmeli?**
-    CSV Format:
-    | bookings |
-    | :--- |
-    | 150 |
-    | 210 |
-    """)
-    st.markdown("---")
-    st.markdown("### ❓ What is 'Bookings'? / 'Bookings' Nedir?")
-    st.write("""
-    **EN:** Total daily reservations. If 2023-05-10 is 150, it means 150 rooms were booked.
-    
-    **TR:** Günlük toplam rezervasyon. Eğer 2023-05-10 değeri 150 ise, o gün 150 oda satılmış demektir.
-    """)
-    st.markdown("---")
-    st.markdown("### 📊 Thresholds / Yoğunluk")
-    st.warning("High (Yoğun): > Avg + 20%")
-    st.success("Stable (Stabil): Normal range")
-    st.markdown("---")
-    file = st.sidebar.file_uploader("Upload CSV / CSV Yükle", type=["csv"])
-
-# ==========================================
-# MAIN CONTENT / ANA SAYFA
-# ==========================================
-st.markdown("<h1 style='text-align: center;'>🏨 Hotel Demand Forecasting / Otel Talep Tahmini</h1>", unsafe_allow_html=True)
-
-if file is not None:
-    df = pd.read_csv(file)
-    
-    # Hesaplamalar
-    raw_data = df[["bookings"]].values
-    data_scaled = sc.transform(raw_data)
-    predictions_scaled = model.predict(data_scaled)
-    predictions = sc.inverse_transform(predictions_scaled).flatten().astype(int)
-
-    avg_val = int(predictions.mean())
-    busy_limit = int(avg_val * 1.2)
-    current_max = int(predictions.max())
-
-    # 1. METRİKLER
-    st.markdown("---")
-    c1, c2, c3 = st.columns(3)
-    with c1:
-        st.markdown(f"<div style='text-align: center;'><strong>Avg Forecast / Ort. Tahmin</strong><br><span style='font-size: 45px; color: #2980b9;'>{avg_val}</span></div>", unsafe_allow_html=True)
-    with c2:
-        st.markdown(f"<div style='text-align: center;'><strong>Busy Threshold / Yoğunluk Sınırı</strong><br><span style='font-size: 45px; color: #e74c3c;'>{busy_limit}</span></div>", unsafe_allow_html=True)
-    with c3:
-        status_color = "#e67e22" if current_max >= busy_limit else "#27ae60"
-        status_text = "HIGH / YOĞUN" if current_max >= busy_limit else "STABLE / STABİL"
-        st.markdown(f"<div style='text-align: center;'><strong>Status / Durum</strong><br><span style='font-size: 40px; color: {status_color}; font-weight: bold;'>{status_text}</span></div>", unsafe_allow_html=True)
-
-    # 2. YÖNETİM TAVSİYESİ
-    st.markdown("---")
-    st.markdown("<h3 style='text-align: center;'>👔 Management Advice / Yönetim Tavsiyesi</h3>", unsafe_allow_html=True)
-    advice_col_en, advice_col_tr = st.columns(2)
-    with advice_col_en:
-        if current_max >= busy_limit: st.warning("**High Demand:** Peak days detected. Increase staff.")
-        else: st.success("**Stable Demand:** Demand is normal. Focus on maintenance.")
-    with advice_col_tr:
-        if current_max >= busy_limit: st.warning("**Yoğun Talep:** Zirve günler tespit edildi. Personel artırın.")
-        else: st.success("**Stabil Talep:** Talep normal. Bakım işlerine odaklanılabilir.")
-
-    # 3. SONUÇ TABLOSU (YUKARIYA TAŞINDI)
-    st.markdown("---")
-    st.subheader("📋 Results Table / Sonuç Tablosu")
-    result_df = pd.DataFrame({
-        "Actual / Gerçek": raw_data.flatten(),
-        "Predicted / Tahmin": predictions
-    })
-    st.dataframe(result_df, use_container_width=True, height=250)
-    
-    st.download_button(
-        label="📥 Download Results / Sonuçları İndir",
-        data=result_df.to_csv(index=False).encode('utf-8'),
-        file_name="hotel_forecast_results.csv",
-        mime="text/csv"
-    )
-
-    # 4. GRAFİK
-    st.markdown("---")
-    st.subheader("📈 Prediction Graph / Tahmin Grafiği")
-    fig, ax = plt.subplots(figsize=(10, 3.5)) 
-    ax.plot(raw_data, label="Actual / Gerçek", color="#bdc3c7", alpha=0.6, linestyle='--')
-    ax.plot(predictions, label="AI Forecast / YZ Tahmini", color="#2980b9", linewidth=2)
-    ax.axhline(y=busy_limit, color='#e74c3c', linestyle=':', label="Limit")
-    ax.legend(prop={'size': 8})
-    st.pyplot(fig)
-
-    # 5. VERİ ÖN İZLEME (EN ALTTA)
-    st.markdown("---")
-    st.subheader("📊 Data Preview / Veri Ön İzleme (Raw Data)")
-    st.dataframe(df, use_container_width=True, height=150)
-
-else:
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pickle
+import os
+from tensorflow.keras.models import load_model
+
+# Sayfa Yapılandırması
+st.set_page_config(page_title="Hotel AI Decision Support", layout="wide")
+
+@st.cache_resource
+def load_assets():
+    # Dosya isimlerini burada tanımlıyoruz
+    model_file = "hotel_model.keras"
+    scaler_file = "scaler.pkl"
+    
+    # Dosya varlık kontrolü
+    if not os.path.exists(model_file):
+        st.error(f"❌ Model dosyası bulunamadı: {model_file}")
+        st.stop()
+    if not os.path.exists(scaler_file):
+        st.error(f"❌ Scaler dosyası bulunamadı: {scaler_file}")
+        st.stop()
+        
+    model = load_model(model_file)
+    with open(scaler_file, "rb") as f:
+        sc = pickle.load(f)
+    return model, sc
+
+# Varlıkları yükle
+try:
+    model, sc = load_assets()
+except Exception as e:
+    st.error(f"⚠️ Yükleme Hatası: {e}")
+    st.stop()
+
+# ==========================================
+# SIDEBAR / SOL PANEL
+# ==========================================
+with st.sidebar:
+    st.markdown("### 📖 Guide & Info / Rehber")
+    st.info("**How to Upload? / Nasıl Yüklenmeli?**\n\nCSV Format:\n| bookings |\n| :--- |\n| 150 |")
+    st.markdown("---")
+    st.markdown("### ❓ What is 'Bookings'? / 'Bookings' Nedir?")
+    st.write("EN: Total daily reservations.\n\nTR: Günlük toplam rezervasyon.")
+    st.markdown("---")
+    st.markdown("### 📊 Thresholds / Yoğunluk")
+    st.warning("High (Yoğun): > Avg + 20%")
+    st.markdown("---")
+    file = st.file_uploader("Upload CSV / CSV Yükle", type=["csv"])
+
+# ==========================================
+# MAIN CONTENT / ANA SAYFA
+# ==========================================
+st.markdown("<h1 style='text-align: center;'>🏨 Hotel Demand Forecasting / Otel Talep Tahmini</h1>", unsafe_allow_html=True)
+
+if file is not None:
+    df = pd.read_csv(file)
+    
+    # Tahmin İşlemleri
+    raw_data = df[["bookings"]].values
+    data_scaled = sc.transform(raw_data)
+    predictions_scaled = model.predict(data_scaled)
+    predictions = sc.inverse_transform(predictions_scaled).flatten().astype(int)
+
+    avg_val = int(predictions.mean())
+    busy_limit = int(avg_val * 1.2)
+    current_max = int(predictions.max())
+
+    # 1. METRİKLER
+    st.markdown("---")
+    c1, c2, c3 = st.columns(3)
+    with c1:
+        st.markdown(f"<div style='text-align: center;'><strong>Avg Forecast / Ort. Tahmin</strong><br><span style='font-size: 45px; color: #2980b9;'>{avg_val}</span></div>", unsafe_allow_html=True)
+    with c2:
+        st.markdown(f"<div style='text-align: center;'><strong>Busy Threshold / Yoğunluk Sınırı</strong><br><span style='font-size: 45px; color: #e74c3c;'>{busy_limit}</span></div>", unsafe_allow_html=True)
+    with c3:
+        status_color = "#e67e22" if current_max >= busy_limit else "#27ae60"
+        status_text = "HIGH / YOĞUN" if current_max >= busy_limit else "STABLE / STABİL"
+        st.markdown(f"<div style='text-align: center;'><strong>Status / Durum</strong><br><span style='font-size: 40px; color: {status_color}; font-weight: bold;'>{status_text}</span></div>", unsafe_allow_html=True)
+
+    # 2. YÖNETİM TAVSİYESİ
+    st.markdown("---")
+    st.markdown("<h3 style='text-align: center;'>👔 Management Advice / Yönetim Tavsiyesi</h3>", unsafe_allow_html=True)
+    ce, ct = st.columns(2)
+    with ce:
+        if current_max >= busy_limit: st.warning("**High Demand Advice:** Peak days detected. Increase staff.")
+        else: st.success("**Stable Demand Advice:** Demand is normal. Focus on maintenance.")
+    with ct:
+        if current_max >= busy_limit: st.warning("**Yoğun Talep Tavsiyesi:** Zirve günler tespit edildi. Personel artırın.")
+        else: st.success("**Stabil Talep Tavsiyesi:** Talep normal. Bakım işlerine odaklanılabilir.")
+
+    # 3. SONUÇ TABLOSU
+    st.markdown("---")
+    st.subheader("📋 Results Table / Sonuç Tablosu")
+    res_df = pd.DataFrame({"Actual / Gerçek": raw_data.flatten(), "Predicted / Tahmin": predictions})
+    st.dataframe(res_df, use_container_width=True, height=200)
+
+    # 4. GRAFİK
+    st.markdown("---")
+    st.subheader("📈 Prediction Graph / Tahmin Grafiği")
+    fig, ax = plt.subplots(figsize=(10, 3.5)) 
+    ax.plot(raw_data, label="Actual / Gerçek", color="#bdc3c7", alpha=0.6, linestyle='--')
+    ax.plot(predictions, label="AI Forecast / YZ Tahmini", color="#2980b9", linewidth=2)
+    ax.axhline(y=busy_limit, color='#e74c3c', linestyle=':', label="Limit")
+    ax.legend(prop={'size': 8})
+    st.pyplot(fig)
+
+    # 5. VERİ ÖN İZLEME
+    st.markdown("---")
+    st.subheader("📊 Data Preview / Veri Ön İzleme")
+    st.dataframe(df, use_container_width=True, height=150)
+
+else:
     st.warning("👈 Please upload a CSV file from the left panel. / Lütfen sol panelden bir CSV dosyası yükleyin.")