app.py

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

# --- 1. MODELİ YÜKLE ---
@st.cache_resource
def load_data():
    try:
        with open('movie_model.pkl', 'rb') as f:
            return pickle.load(f)
    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]},
    "Cillian Murphy": {"c": "Male / Erkek", "v": [0.4, 0.1, 0.1, 0.9, 0.4, 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]},
    "George Clooney": {"c": "Male / Erkek", "v": [0.3, 0.4, 0.5, 0.8, 0.1, 0.6]},
    "Jake Gyllenhaal": {"c": "Male / Erkek", "v": [0.6, 0.1, 0.1, 0.9, 0.2, 0.8]},
    "Pierce Brosnan": {"c": "Male / Erkek", "v": [0.8, 0.7, 0.3, 0.4, 0.1, 0.6]},
    "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]},
    "Milla Jovovich": {"c": "Female / Kadın", "v": [0.9, 0.4, 0.1, 0.1, 0.9, 0.3]},
    "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]},
    "Jennifer Lawrence": {"c": "Female / Kadın", "v": [0.7, 0.8, 0.3, 0.9, 0.4, 0.2]},
    "Meryl Streep": {"c": "Female / Kadın", "v": [0.1, 0.1, 0.4, 0.95, 0.1, 0.1]},
    "Salma Hayek": {"c": "Female / Kadın", "v": [0.6, 0.3, 0.5, 0.8, 0.1, 0.1]},
    "Anne Hathaway": {"c": "Female / Kadın", "v": [0.2, 0.7, 0.8, 0.8, 0.1, 0.1]},
    "Keira Knightley": {"c": "Female / Kadın", "v": [0.1, 0.8, 0.1, 0.95, 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]},
    "Monica Bellucci": {"c": "Female / Kadın", "v": [0.6, 0.1, 0.1, 0.8, 0.5, 0.3]},
    "Emily Blunt": {"c": "Female / Kadın", "v": [0.7, 0.6, 0.1, 0.8, 0.5, 0.6]},
    "Cameron Diaz": {"c": "Female / Kadın", "v": [0.1, 0.2, 0.95, 0.7, 0.1, 0.1]}
}

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

# --- SOL PANEL (SIDEBAR) ---
st.sidebar.title("🔍 Control Panel / Kontrol Paneli")
st.sidebar.markdown("---")

gender_choice = st.sidebar.radio(
    "1. Select Category / Kategori Seçin:",
    ["Male / Erkek", "Female / Kadın"]
)

# CRITICAL FIX: Filtrelemeyi klasörden değil, doğrudan ana veritabanından yapıyoruz. 
# Böylece resim klasörü okunmasa bile isimler kaybolmaz.
filtered_names = [name for name, data in tum_unlu_verileri.items() if data['c'] == gender_choice]

st.sidebar.markdown(f"### 📋 Available Cast / Mevcut Oyuncular")
if not filtered_names:
    st.sidebar.warning("No players found.")
else:
    for n in filtered_names:
        st.sidebar.write(f"• {n}")

# --- ANA EKRAN ---
st.title("🎬 CastMatch AI: Talent Matching System / Yetenek Eşleştirme Sistemi")
st.markdown("#### Discover the perfect roles for global stars! / Dünya yıldızları için en ideal rolleri keşfedin!")
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:", filtered_names if filtered_names else ["Yok"])
    
    if selected_actor != "Yok":
        resim_klasoru = os.path.join(os.getcwd(), "40 resim")
        found_path = None
        
        # Resim arama mantığını esnettik
        if os.path.exists(resim_klasoru):
            search_target = selected_actor.replace(" ", "_").lower()
            for f in os.listdir(resim_klasoru):
                if f.lower().startswith(search_target) or selected_actor.lower() in f.lower():
                    found_path = os.path.join(resim_klasoru, f)
                    break
        
        if found_path:
            st.image(found_path, caption=f"Profile: {selected_actor}", use_container_width=True)
        else:
            # Resim yoksa isim kaybolmasın, sadece hata mesajı verilsin
            st.info(f"Resim dosyası '40 resim' klasöründe bulunamadı: {selected_actor}")

with col_match:
    st.subheader("🎯 Best Career Matches / En İyi Kariyer Eşleşmeleri")
    st.write("Comparing the star's DNA with movie roles...")
    
    if st.button("🚀 Match and Recommend / Eşleştir ve Öner") and selected_actor != "Yok":
        if movie_dict:
            # Veriyi 'tum_unlu_verileri' üzerinden çekiyoruz (Daha güvenli)
            actor_v = tum_unlu_verileri[selected_actor]['v']
            results = []
            for i in movie_dict:
                noise = random.uniform(0, 0.0001)
                dist = spatial.distance.cosine(actor_v, movie_dict[i][1][:6]) + noise
                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 = 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 dosyası eksik! / Model file missing!")

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.")