Update app.py

app.py

@@ -1,10 +1,11 @@
 import streamlit as st
 import pickle
-from scipy import spatial
-import random
+from scipy.spatial.distance import cosine
 import os
 
-# --- 1. MODELİ YÜKLE ---
+# -------------------------------
+# 1. MODELİ YÜKLE
+# -------------------------------
 @st.cache_resource
 def load_data():
     try:
@@ -16,139 +17,93 @@ def load_data():
 model_data = load_data()
 movie_dict = model_data['movie_dict'] if model_data else {}
 
-# --- 2. OYUNCU DNA VERİTABANI ---
-# Sıralama Stallone ile başlar
-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]}
+# -------------------------------
+# 2. AKTÖR DNA VERİLERİ
+# -------------------------------
+actors = {
+    "Sylvester Stallone": {"gender": "Male", "vec": [0.9,0.2,0.1,0.1,0.1,0.8]},
+    "Robert De Niro": {"gender": "Male", "vec": [0.2,0.1,0.1,0.9,0.1,0.8]},
+    "Johnny Depp": {"gender": "Male", "vec": [0.1,0.9,0.8,0.4,0.1,0.1]},
+    "Jason Statham": {"gender": "Male", "vec": [0.95,0.3,0.1,0.1,0.1,0.7]},
+    "Keanu Reeves": {"gender": "Male", "vec": [0.9,0.4,0.1,0.2,0.9,0.6]},
+    "Angelina Jolie": {"gender": "Female", "vec": [0.9,0.7,0.1,0.8,0.1,0.6]},
+    "Scarlett Johansson": {"gender": "Female", "vec": [0.9,0.6,0.2,0.7,0.8,0.5]},
+    "Meryl Streep": {"gender": "Female", "vec": [0.1,0.1,0.4,0.95,0.1,0.1]},
+    "Jennifer Lawrence": {"gender": "Female", "vec": [0.7,0.8,0.3,0.9,0.4,0.2]}
 }
 
-# --- ARAYÜZ AYARLARI ---
-st.set_page_config(page_title="CastMatch AI", layout="wide")
+# -------------------------------
+# 3. RESİM KLASÖRÜ
+# -------------------------------
+image_folder = "40 resim"
 
-@st.cache_data
-def get_image_path(actor_name):
-    if not actor_name or actor_name == "Choose... / Seçiniz...":
+def get_actor_image(name):
+    if not os.path.exists(image_folder):
         return None
-    ana_dizin = os.getcwd()
-    search_term = actor_name.lower().replace(" ", "_")
-    search_term_plain = actor_name.lower()
-    for f in os.listdir(ana_dizin):
-        f_lower = f.lower()
-        if f_lower.endswith(('.jpg', '.png', '.jpeg')):
-            if search_term in f_lower or search_term_plain in f_lower:
-                return os.path.join(ana_dizin, f)
+    for f in os.listdir(image_folder):
+        if name.lower().replace(" ", "_") in f.lower():
+            return os.path.join(image_folder, f)
     return None
 
-# --- SOL PANEL (BİLGİLER BURADA) ---
-st.sidebar.title("🔍 Control Panel / Kontrol Paneli")
-st.sidebar.markdown("---")
-
-gender_choice = st.sidebar.radio(
-    "1. Select Category / Kategori Seçin:",
-    ["Male / Erkek", "Female / Kadın"]
-)
-
-# Oyuncu listesi (Alfabetik değil, Stallone başa gelecek şekilde filtreleme)
-filtered_names = [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("#### 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")
-    options = ["Choose... / Seçiniz..."] + filtered_names
-    selected_actor = st.selectbox("Select from list / Listeden seçin:", options)
-    
-    # Brad Pitt ve diğerlerinde titremeyi önlemek için alanı ayırıyoruz
-    if selected_actor != "Choose... / Seçiniz...":
-        path = get_image_path(selected_actor)
-        if path:
-            st.image(path, caption=f"Profile: {selected_actor}", use_container_width=True)
-        else:
-            st.warning(f"⚠️ Resim bulunamadı: {selected_actor}")
+# -------------------------------
+# 4. STREAMLIT UI
+# -------------------------------
+st.set_page_config(page_title="CastMatch AI", layout="wide")
+
+st.title("🎬 CastMatch AI")
+st.markdown("### Actor → Movie Matching System")
+st.markdown("Oyuncuya en uygun filmi bulur / Finds best movie for actor")
+
+# Sidebar
+gender = st.sidebar.radio("Select Gender / Cinsiyet", ["Male", "Female"])
+
+filtered = [a for a in actors if actors[a]["gender"] == gender]
+
+actor_name = st.selectbox("Choose Actor / Oyuncu Seç", filtered)
+
+col1, col2 = st.columns(2)
+
+# -------------------------------
+# 5. AKTÖR GÖSTER
+# -------------------------------
+with col1:
+    st.subheader("Actor / Oyuncu")
+
+    img_path = get_actor_image(actor_name)
+    if img_path:
+        st.image(img_path, use_container_width=True)
     else:
-        st.info("Please select an actor to see the profile. / Profili görmek için bir oyuncu seçin.")
-
-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"):
-        if selected_actor == "Choose... / Seçiniz...":
-            st.warning("Please pick an actor first! / Lütfen önce bir oyuncu seçin!")
-        elif movie_dict:
-            actor_v = tum_unlu_verileri[selected_actor]['v']
+        st.warning("Image not found")
+
+# -------------------------------
+# 6. ÖNERİ SİSTEMİ
+# -------------------------------
+with col2:
+    st.subheader("Best Matches / En İyi Eşleşmeler")
+
+    if st.button("Recommend / Öner"):
+        if not movie_dict:
+            st.error("Model bulunamadı!")
+        else:
+            actor_vec = actors[actor_name]["vec"]
+
             results = []
-            
+
             for i in movie_dict:
-                # Orijinal vektör analizi
-                dist = spatial.distance.cosine(actor_v, movie_dict[i][1][:6])
-                
-                # 11.5 skoru hatasını engellemek için mesafeyi 0-1 arasına hapsediyoruz
-                dist = max(0, min(1, dist))
-                
-                # Sıralama kararlılığı için minik bir gürültü
-                noise = random.uniform(0, 0.00001)
-                results.append((movie_dict[i][0], dist + noise))
-            
-            # En iyi 5 eşleşmeyi (en küçük mesafe) al
-            top_matches = sorted(results, key=lambda x: x[1])[:5]
-            
-            for i, (film, score) in enumerate(top_matches, 1):
-                # Skoru % cinsinden hesapla: (1 - mesafe) * 100
-                pct = (1 - score) * 100
-                # %0-%100 arası kalmasını garanti et
-                pct = max(0, min(100, pct))
-                
-                st.success(f"**{i}. {film}**")
-                st.write(f"Match Score / Uyum Skoru: **%{pct:.1f}**")
-                st.progress(pct / 100)
-        else:
-            st.error("Model dosyası eksik!")
+                movie_vec = movie_dict[i][1][:6]
+
+                # COSINE DISTANCE (DAHA STABİL)
+                dist = cosine(actor_vec, movie_vec)
+
+                # SCORE (0-100)
+                score = (1 - dist) * 100
+
+                results.append((movie_dict[i][0], score))
+
+            # EN İYİ 5
+            results = sorted(results, key=lambda x: x[1], reverse=True)[:5]
 
-st.markdown("---")
-st.info("💡 **How it works? / Nasıl çalışır?**: This AI analyzes career vectors based on available local assets. / Bu yapay zeka kariyer vektörlerini analiz eder.")
+            for i, (movie, score) in enumerate(results, 1):
+                st.write(f"### {i}. {movie}")
+                st.write(f"Score: %{round(score,2)}")
+                st.progress(score / 100)