app.py

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

# --- TİTREMEYİ ÖNLEYİCİ CSS (Arayüz yapını bozmaz, sadece sabitler) ---
st.set_page_config(page_title="CastMatch AI", layout="wide")
st.markdown("""
    <style>
    /* Resim kutusunun boyutunu sabitleyerek sayfanın zıplamasını engeller */
    [data-testid="stImage"] img {
        max-height: 400px;
        object-fit: contain;
    }
    </style>
    """, unsafe_allow_html=True)

# 1. MODELİ YÜKLE (Cache eklendi: Dosyayı her saniye okumasını engeller)
@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. TÜM OYUNCU DNA VERİTABANI (Senin orijinal verilerin)
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]}
}

# --- DİNAMİK FİLTRELEME ---
resim_klasoru = os.getcwd() # Hugging Face için ana dizine ayarlandı

@st.cache_data # Dosya sistemini sürekli taramasını engeller
def klasordeki_unluleri_getir():
    mevcut_unlular = {}
    dosyalar = [f.lower() for f in os.listdir(resim_klasoru)]
    for isim, veri in tum_unlu_verileri.items():
        search_pattern1 = isim.lower().replace(" ", "_")
        search_pattern2 = isim.lower()
        if any(search_pattern1 in d or search_pattern2 in d for d in dosyalar if d.endswith(('.jpg', '.png', '.jpeg'))):
            mevcut_unlular[isim] = veri
    return mevcut_unlular

unlu_verileri = klasordeki_unluleri_getir()

# --- 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"],
    key="gender_radio" # Sabit key titremeyi azaltır
)

filtered_names = [name for name, data in unlu_verileri.items() if data['c'] == gender_choice]

st.sidebar.markdown(f"### 📋 Available Cast / Mevcut Oyuncular")
if not filtered_names:
    st.sidebar.warning("Bu kategoride resim bulunamadı.")
for n in filtered_names:
    st.sidebar.write(f"• {n}")

# --- ANA EKRAN ---
st.title("🎬 CastMatch AI: Talent Matching System")
st.markdown("#### Discover the perfect roles for global stars!")
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:", filtered_names if filtered_names else ["Yok"], key="actor_select")
    
    # Resim kutusu (boşluk titremesini engellemek için container içine alındı)
    with st.container():
        if selected_actor != "Yok":
            found_path = None
            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:
                st.error("Resim yüklenemedi.")

with col_match:
    st.subheader("🎯 Best Career Matches")
    st.write("Comparing the star's DNA with movie roles...")
    
    # Buton ve sonuçlar
    if st.button("🚀 Match and Recommend", key="match_button") and selected_actor != "Yok":
        if movie_dict:
            actor_v = 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: **%{pct}**")
                st.progress(pct / 100)
        else:
            st.error("Model dosyası eksik!")

st.markdown("---")
st.info("💡 **How it works?**: This AI analyzes career vectors based on available local assets.")