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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hotel_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,124 @@
+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:
+    st.warning("👈 Please upload a CSV file from the left panel. / Lütfen sol panelden bir CSV dosyası yükleyin.")

hotel_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56df47a06afe17a3240f4822dd5956c4081017bbfb51b8e8226f833dda3be532
+size 229885

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23349bc970be5cea6e46fbc6c31d7e725addfa048bc5266b131efefbf90cd9a8
+size 521

test_data.csv

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+bookings
+247
+101
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+174
+133
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+232
+272
+238
+251
+169
+115
+259
+192
+255
+236
+226
+152
+184
+130
+204
+224
+255
+166
+193
+254
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+260