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app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import plotly.express as px
+
+# 1. Sayfa Ayarları
+st.set_page_config(page_title="Movie Recommender AI", layout="wide")
+
+# 2. Şık Görsel Stil (CSS) - Netflix ibaresi kaldırıldı, renkler düzenlendi
+st.markdown("""
+    <style>
+    .stApp { background-color: #141414; color: white; }
+    .stButton>button { 
+        width: 100%; 
+        background-color: #333333; 
+        color: white; 
+        font-weight: bold; 
+        border: 1px solid #555; 
+        border-radius: 5px;
+        height: 3em;
+    }
+    .stButton>button:hover { background-color: #e50914; border: 1px solid #e50914; color: white; }
+    .stSelectbox label { color: white !important; font-size: 16px !important; }
+    .movie-card { 
+        background-color: #262730; 
+        padding: 20px; 
+        border-radius: 10px; 
+        border-top: 5px solid #e50914; 
+        height: 520px; 
+        margin-bottom: 20px;
+    }
+    h1, h2, h3, h4, p, span { color: white !important; }
+    .match-tag {
+        background-color: #e50914; 
+        color: white; 
+        text-align: center; 
+        border-radius: 5px; 
+        padding: 5px; 
+        font-size: 14px; 
+        font-weight: bold; 
+        margin-top: 10px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# 3. Veri ve Model Yükleme
+@st.cache_resource
+def load_assets():
+    try:
+        df = pd.read_csv('netflix_titles.csv')
+        with open('similarity.pkl', 'rb') as f:
+            similarity = pickle.load(f)
+        with open('indices.pkl', 'rb') as f:
+            indices = pickle.load(f)
+        return df, similarity, indices
+    except:
+        return None, None, None
+
+df, similarity, indices = load_assets()
+
+# Türkçe Çeviri Simülasyonu Fonksiyonu
+def get_turkish_desc(text):
+    # Veri setindeki İngilizce özetleri Türkçeleştirme başlığı altında sunar
+    return f"Bu yapım genel olarak şunu konu almaktadır: {text[:100]}..."
+
+if df is not None:
+    # --- YENİ BAŞLIK ---
+    st.title("🎬 Movie Recommendation System / Film Öneri Sistemi")
+    st.write("---")
+
+    col_left, col_main = st.columns([1.5, 3])
+
+    # --- SOL TARAF: ÖRNEKLER (İKİŞERLİ YAN YANA GRID) ---
+    with col_left:
+        st.subheader("💡 Suggestions / Örnekler")
+        samples = [
+            ("Kota Factory", "Eğitim"), ("Ganglands", "Aksiyon"),
+            ("Midnight Mass", "Korku"), ("Squid Game", "Gerilim"),
+            ("The Witcher", "Fantastik"), ("Peaky Blinders", "Dram"),
+            ("Dark", "Gizem"), ("Lucifer", "Suç")
+        ]
+        
+        # 2'li Izgara Yapısı
+        for i in range(0, len(samples), 2):
+            c1, c2 = st.columns(2)
+            with c1:
+                st.button(samples[i][0], key=f"btn_{samples[i][0]}")
+                st.caption(f"({samples[i][1]})")
+            with c2:
+                if i+1 < len(samples):
+                    st.button(samples[i+1][0], key=f"btn_{samples[i+1][0]}")
+                    st.caption(f"({samples[i+1][1]})")
+
+        st.markdown("""
+            <div style="background-color: #1c1c1c; padding: 15px; border-radius: 8px; border: 1px solid #444; margin-top: 25px;">
+                <p style="font-size:14px; margin:0; color: #ddd !important;">
+                <b>İpucu:</b> Beğendiğiniz bir filmi sağdaki listeden seçebilir veya ismini yazarak aratabilirsiniz.
+                </p>
+            </div>
+        """, unsafe_allow_html=True)
+
+    # --- SAĞ TARAF: ANALİZ VE SEÇİM ---
+    with col_main:
+        # Film seçerken yanında kategorisi de görünsün
+        df['display_name'] = df['title'] + " (" + df['listed_in'] + ")"
+        
+        selected_display = st.selectbox(
+            "Bir Film veya Dizi Seçin / Select a Movie or TV Show:",
+            df['display_name'].values
+        )
+        # Seçilen isimden orijinal başlığı ayıkla
+        selected_movie = selected_display.split(" (")[0]
+        
+        process_btn = st.button('ÖNERİLERİ ANALİZ ET VE GETİR / ANALYZE')
+
+    # --- ANALİZ SONUÇLARI ---
+    if process_btn:
+        idx = indices[selected_movie]
+        sim_scores = sorted(list(enumerate(similarity[idx])), key=lambda x: x[1], reverse=True)
+        
+        # En benzer 5 film
+        top_indices = [i[0] for i in sim_scores[1:6]]
+        top_scores = [i[1] for i in sim_scores[1:6]]
+        
+        recs = df.iloc[top_indices].copy()
+        recs['Score'] = top_scores
+
+        # BENZERLİK GRAFİĞİ
+        st.subheader("📊 Benzerlik Oranları / Similarity Analysis")
+        fig = px.bar(recs, x='Score', y='title', orientation='h', color='Score',
+                     color_continuous_scale='Reds', template="plotly_dark", height=300)
+        fig.update_layout(yaxis={'categoryorder':'total ascending'})
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.write("---")
+
+        # FİLM KARTLARI
+        st.subheader("Tavsiye Edilen Yapımlar / Recommendations")
+        cols = st.columns(5)
+        for i, col in enumerate(cols):
+            with col:
+                row = recs.iloc[i]
+                st.markdown(f"""
+                <div class="movie-card">
+                    <h4 style="color: #e50914; font-size: 16px; margin-bottom: 2px;">{row['title']}</h4>
+                    <p style="font-size: 11px; color: #aaa !important;">{row['listed_in']}</p>
+                    <hr style="border-color: #444; margin: 10px 0;">
+                    <p style="font-size: 12px; color: white !important;"><b>🇬🇧 Summary:</b><br>{row['description'][:60]}...</p>
+                    <p style="font-size: 12px; color: #ffcc00 !important;"><b>🇹🇷 Özet:</b><br>{get_turkish_desc(row['description'])}</p>
+                    <div class="match-tag">
+                        %{int(row['Score']*100)} Match
+                    </div>
+                </div>
+                """, unsafe_allow_html=True)
+else:
+    st.error("Dosyalar yüklenemedi! 'netflix_titles.csv', 'similarity.pkl' ve 'indices.pkl' dosyalarını kontrol edin.")

indices.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7fcc878604e7c94a6f2c0c564aec2c012e6001cd07fe23fdc94faa9cb79f24e6
+size 297574

similarity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:38046969a048dc18aafd16b51e7c617991e2b0c8d18ef00f08737a07030391cf
+size 620506156