import streamlit as st
import pickle
from scipy import spatial
import os
import glob

# --- 1. MODELİ YÜKLE ---
@st.cache_resource
def load_data():
    try:
        # Model dosyasını kontrol et
        if os.path.exists('movie_model.pkl'):
            with open('movie_model.pkl', 'rb') as f:
                return pickle.load(f)
        return None
    except:
        return None

model_data = load_data()
movie_dict = model_data['movie_dict'] if model_data else {}

# --- 2. OYUNCU DNA VERİTABANI ---
tum_unlu_verileri = {
    "Sylvester Stallone": {"c": "Male / Erkek", "v": [0.9, 0.2, 0.1, 0.1, 0.1, 0.8]},
    "Robert De Niro": {"c": "Male / Erkek", "v": [0.2, 0.1, 0.1, 0.9, 0.1, 0.8]},
    "Johnny Depp": {"c": "Male / Erkek", "v": [0.1, 0.9, 0.8, 0.4, 0.1, 0.1]},
    "Jason Statham": {"c": "Male / Erkek", "v": [0.95, 0.3, 0.1, 0.1, 0.1, 0.7]},
    "Keanu Reeves": {"c": "Male / Erkek", "v": [0.9, 0.4, 0.1, 0.2, 0.9, 0.6]},
    "Brad Pitt": {"c": "Male / Erkek", "v": [0.7, 0.5, 0.3, 0.8, 0.2, 0.4]},
    "Tom Cruise": {"c": "Male / Erkek", "v": [0.9, 0.8, 0.2, 0.1, 0.4, 0.5]},
    "Anthony Hopkins": {"c": "Male / Erkek", "v": [0.1, 0.1, 0.1, 0.95, 0.1, 0.9]},
    "Arnold Schwarzenegger": {"c": "Male / Erkek", "v": [0.95, 0.6, 0.2, 0.1, 0.8, 0.1]},
    "Bruce Willis": {"c": "Male / Erkek", "v": [0.9, 0.2, 0.1, 0.3, 0.4, 0.7]},
    "Christian Bale": {"c": "Male / Erkek", "v": [0.8, 0.2, 0.1, 0.9, 0.1, 0.7]},
    "Tom Hanks": {"c": "Male / Erkek", "v": [0.2, 0.6, 0.5, 0.9, 0.1, 0.1]},
    "Morgan Freeman": {"c": "Male / Erkek", "v": [0.1, 0.3, 0.1, 0.9, 0.1, 0.6]},
    "Al Pacino": {"c": "Male / Erkek", "v": [0.2, 0.1, 0.1, 0.95, 0.1, 0.8]},
    "Jackie Chan": {"c": "Male / Erkek", "v": [0.9, 0.7, 0.9, 0.1, 0.1, 0.1]},
    "Denzel Washington": {"c": "Male / Erkek", "v": [0.8, 0.2, 0.1, 0.9, 0.1, 0.7]},
    "Pierce Brosnan": {"c": "Male / Erkek", "v": [0.8, 0.7, 0.3, 0.4, 0.1, 0.6]},
    "Leonardo DiCaprio": {"c": "Male / Erkek", "v": [0.3, 0.1, 0.1, 0.95, 0.1, 0.8]},
    "Matt Damon": {"c": "Male / Erkek", "v": [0.8, 0.3, 0.2, 0.9, 0.2, 0.6]},
    "Will Smith": {"c": "Male / Erkek", "v": [0.9, 0.6, 0.8, 0.5, 0.1, 0.2]},
    "Angelina Jolie": {"c": "Female / Kadın", "v": [0.9, 0.7, 0.1, 0.8, 0.1, 0.6]},
    "Natalie Portman": {"c": "Female / Kadın", "v": [0.3, 0.2, 0.1, 0.9, 0.6, 0.7]},
    "Scarlett Johansson": {"c": "Female / Kadın", "v": [0.9, 0.6, 0.2, 0.7, 0.8, 0.5]},
    "Charlize Theron": {"c": "Female / Kadın", "v": [0.8, 0.3, 0.1, 0.9, 0.2, 0.7]},
    "Sandra Bullock": {"c": "Female / Kadın", "v": [0.2, 0.1, 0.9, 0.8, 0.1, 0.6]},
    "Nicole Kidman": {"c": "Female / Kadın", "v": [0.1, 0.2, 0.1, 0.95, 0.1, 0.7]},
    "Meryl Streep": {"c": "Female / Kadın", "v": [0.1, 0.1, 0.4, 0.95, 0.1, 0.1]},
    "Anne Hathaway": {"c": "Female / Kadın", "v": [0.2, 0.7, 0.8, 0.8, 0.1, 0.1]},
    "Julia Roberts": {"c": "Female / Kadın", "v": [0.1, 0.2, 0.9, 0.9, 0.1, 0.1]},
    "Penelope Cruz": {"c": "Female / Kadın", "v": [0.2, 0.3, 0.6, 0.9, 0.1, 0.6]},
    "Emma Watson": {"c": "Female / Kadın", "v": [0.1, 0.8, 0.2, 0.9, 0.6, 0.1]}
}

# --- ARAYÜZ AYARLARI ---
st.set_page_config(page_title="CastMatch AI", layout="wide")

# CSS: Titreme önleme ve resim sabitleme
st.markdown("""
    <style>
    [data-testid="stImage"] {
        min-height: 400px !important;
        max-height: 400px !important;
        display: flex; align-items: center; justify-content: center;
    }
    .stImage img {
        max-height: 400px !important;
        width: auto !important;
        object-fit: contain;
    }
    </style>
    """, unsafe_allow_html=True)

# --- SIDEBAR (YAN PANEL) ---
st.sidebar.title("🔍 Control Panel / Kontrol Paneli")
st.sidebar.markdown("---")
gender_choice = st.sidebar.radio("1. Select Category / Kategori Seçin:", ["Male / Erkek", "Female / Kadın"])

filtered_names = sorted([name for name in tum_unlu_verileri.keys() if tum_unlu_verileri[name]['c'] == gender_choice])

st.sidebar.markdown(f"### 📋 Available Cast / Mevcut Oyuncular")
for n in filtered_names:
    st.sidebar.write(f"• {n}")

# --- ANA EKRAN ---
st.title("🎬 CastMatch AI: Talent Matching System / Yetenek Eşleştirme Sistemi")
st.markdown("---")

col_actor, col_match = st.columns([1, 2])

with col_actor:
    st.subheader("👤 Pick an Actor / Oyuncu Seç")
    selected_actor = st.selectbox("Select from list / Listeden seçin:", ["Choose... / Seçiniz..."] + filtered_names)
    
    if selected_actor != "Choose... / Seçiniz...":
        # HEM LOKAL HEM HF İÇİN AKILLI DOSYA ARAMA
        found_path = None
        # Oyuncu isminin muhtemel dosya adı varyasyonları
        name_variants = [
            selected_actor.replace(" ", "_").lower(),
            selected_actor.replace(" ", " ").lower(),
            selected_actor.lower()
        ]
        
        # Mevcut dizin ve tüm alt dizinlerdeki resimleri tara
        all_images = glob.glob("**/*.*", recursive=True)
        
        for img_path in all_images:
            if img_path.lower().endswith(('.jpg', '.png', '.jpeg')):
                for variant in name_variants:
                    if variant in img_path.lower():
                        found_path = img_path
                        break
                if found_path: break
        
        if found_path:
            st.image(found_path, use_container_width=False)
        else:
            st.warning(f"Image not found / Resim bulunamadı: {selected_actor}")
            st.markdown('<div style="height: 300px;"></div>', unsafe_allow_html=True)
    else:
        st.markdown('<div style="height: 400px; display: flex; align-items: center; justify-content: center; color: #999; font-style: italic;">Select an actor to view profile / Profil görmek için oyuncu seçin</div>', unsafe_allow_html=True)

with col_match:
    st.subheader("🎯 Best Career Matches / En İyi Kariyer Eşleşmeleri")
    
    if st.button("🚀 Match and Recommend / Eşleştir ve Öner") and selected_actor != "Choose... / Seçiniz...":
        if movie_dict:
            actor_v = tum_unlu_verileri[selected_actor]['v']
            results = []
            for i in movie_dict:
                dist = spatial.distance.cosine(actor_v, movie_dict[i][1][:6])
                dist = max(0, min(1, dist))
                results.append((movie_dict[i][0], dist))
            
            top_matches = sorted(results, key=lambda x: x[1])[:5]
            for i, (film, score) in enumerate(top_matches, 1):
                pct = max(0, min(100, round((1 - score) * 100, 1)))
                st.success(f"**{i}. {film}**")
                st.write(f"Match Score / Uyum Skoru: **%{pct}**")
                st.progress(pct / 100)
        else:
            st.error("Model file missing! / Model dosyası eksik!")

# --- ALT BİLGİ ---
st.markdown("---")
st.info("**How it works? / Nasıl çalışır?**: This AI analyzes career vectors based on available local assets. / Bu yapay zeka, mevcut yerel varlıklara (resimlere) dayanarak kariyer vektörlerini analiz eder.")