Update app.py

app.py

@@ -1,144 +1,147 @@
-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. TÜM 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]}
-}
-
-# --- DİNAMİK FİLTRELEME (KLASÖRE GÖRE) ---
-resim_klasoru = os.path.join(os.getcwd(), "40 resim")
-
-def klasordeki_unluleri_getir():
-    mevcut_unlular = {}
-    if os.path.exists(resim_klasoru):
-        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):
-                mevcut_unlular[isim] = veri
-    return mevcut_unlular
-
-unlu_verileri = klasordeki_unluleri_getir()
-
-# --- 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"]
-)
-
-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. / No images in this category.")
-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":
-        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. / Image could not be loaded.")
-
-with col_match:
-    st.subheader("🎯 Best Career Matches / En İyi Kariyer Eşleşmeleri")
-    st.write("Comparing the star's DNA with movie roles... / Yıldızın DNA'sı film rolleriyle karşılaştırılıyor...")
-    
-    if st.button("🚀 Match and Recommend / Eşleştir ve Öner") 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 / Uyum Skoru: **%{pct}**")
-                st.progress(pct / 100)
-        else:
-            st.error("Model dosyası eksik! / Model file missing!")
-
-st.markdown("---")
-# EN ALT BİLGİ NOTU DÜZELTİLDİ:
-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.")
+import streamlit as st
+import pickle
+from scipy import spatial
+import random
+import os
+
+# 1. MODELİ YÜKLE
+@st.cache_resource
+def load_data():
+    try:
+        # Hugging Face ana dizinindeki pkl dosyasını okur
+        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]}
+}
+
+# --- DİNAMİK FİLTRELEME ---
+# Resimler klasörde değil, ana dizinde olduğu için "." (getcwd) kullanıyoruz
+resim_klasoru = os.getcwd()
+
+def klasordeki_unluleri_getir():
+    mevcut_unlular = {}
+    dosyalar = [f.lower() for f in os.listdir(resim_klasoru)]
+    for isim, veri in tum_unlu_verileri.items():
+        # Dosya isimlerindeki boşluk/alt tire farkını tolore ediyoruz
+        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()
+
+# --- 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"]
+)
+
+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. / No images found.")
+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":
+        found_path = None
+        search_targets = [selected_actor.lower().replace(" ", "_"), selected_actor.lower()]
+        
+        # Ana dizindeki tüm dosyaları tara
+        for f in os.listdir(resim_klasoru):
+            if any(f.lower().startswith(target) for target in search_targets):
+                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 bulunamadı. / Image not found.")
+
+with col_match:
+    st.subheader("🎯 Best Career Matches / En İyi Kariyer Eşleşmeleri")
+    st.write("Comparing the star's DNA with movie roles... / Yıldızın DNA'sı film rolleriyle karşılaştırılıyor...")
+    
+    if st.button("🚀 Match and Recommend / Eşleştir ve Öner") 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 / Uyum Skoru: **%{pct}**")
+                st.progress(pct / 100)
+        else:
+            st.error("Model Error / Model Hatası")
+
+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 dayanarak kariyer vektörlerini analiz eder.")