import streamlit as st
import pandas as pd
import pickle
import plotly.express as px
import os

# 1. Sayfa Ayarları
st.set_page_config(page_title="Movie Recommender AI", layout="wide")

# 2. Şık Görsel Stil (CSS) - Senin beğendiğin tam tasarım
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 (Hugging Face / Xet uyumlu)
@st.cache_resource
def load_assets():
    try:
        # Dosya kontrolü
        if not os.path.exists('netflix_titles.csv'):
            return None, None, None
            
        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 Exception as e:
        st.error(f"Yükleme hatası: {e}")
        return None, None, None

df, similarity, indices = load_assets()

# Türkçe Çeviri Simülasyonu
def get_turkish_desc(text):
    return f"Bu yapım genel olarak şunu konu almaktadır: {text[:100]}..."

# Uygulama Başlangıcı
if df is not None:
    st.title("🎬 Movie Recommendation System / Film Öneri Sistemi")
    st.write("---")

    col_left, col_main = st.columns([1.5, 3])

    # --- SOL TARAF: ÖRNEKLER ---
    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")
        ]
        
        for i in range(0, len(samples), 2):
            c1, c2 = st.columns(2)
            with c1:
                if st.button(samples[i][0], key=f"btn_{samples[i][0]}"):
                    st.session_state.selected_movie = samples[i][0]
                st.caption(f"({samples[i][1]})")
            with c2:
                if i+1 < len(samples):
                    if st.button(samples[i+1][0], key=f"btn_{samples[i+1][0]}"):
                        st.session_state.selected_movie = 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:
        df['display_name'] = df['title'] + " (" + df['listed_in'] + ")"
        
        # Seçim kutusunda varsayılan değer kontrolü
        default_index = 0
        if 'selected_movie' in st.session_state:
            try:
                default_index = list(df['title']).index(st.session_state.selected_movie)
            except:
                default_index = 0

        selected_display = st.selectbox(
            "Bir Film veya Dizi Seçin / Select a Movie or TV Show:",
            df['display_name'].values,
            index=default_index
        )
        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)
        
        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

        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("---")

        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.")