Update app.py

app.py

@@ -1,22 +1,122 @@
-if st.button("🚀 Match and Recommend / Eşleştir ve Öner") and selected_actor != "Yok":
-    if movie_dict:
+import streamlit as st
+import pickle
+from scipy import spatial
+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]}
+}
+
+# -------------------------------
+# RESİM KLASÖRÜ
+# -------------------------------
+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()
+
+# -------------------------------
+# UI
+# -------------------------------
+st.set_page_config(page_title="CastMatch AI", layout="wide")
+
+st.sidebar.title("🔍 Control Panel / Kontrol Paneli")
+
+gender_choice = st.sidebar.radio(
+    "Kategori Seçin / Select Category",
+    ["Male / Erkek", "Female / Kadın"]
+)
+
+filtered_names = [name for name, data in unlu_verileri.items() if data['c'] == gender_choice]
+
+st.title("🎬 CastMatch AI")
+
+col1, col2 = st.columns([1,2])
+
+with col1:
+    selected_actor = st.selectbox("Oyuncu Seç / Select Actor", filtered_names if filtered_names else ["Yok"])
+
+    if selected_actor != "Yok":
+        for f in os.listdir(resim_klasoru):
+            if selected_actor.lower().replace(" ","_") in f.lower():
+                st.image(os.path.join(resim_klasoru, f))
+                break
+
+with col2:
+    if st.button("🚀 Eşleştir / Match"):
         actor_v = unlu_verileri[selected_actor]['v']
         results = []
 
         for i in movie_dict:
             dist = spatial.distance.cosine(actor_v, movie_dict[i][1][:6])
-
-            # skor = benzerlik yüzdesi
             score = (1 - dist) * 100
-
             results.append((movie_dict[i][0], score))
 
-        top_matches = sorted(results, key=lambda x: x[1], reverse=True)[:5]
+        top = sorted(results, key=lambda x: x[1], reverse=True)[:5]
 
-        for i, (film, score) in enumerate(top_matches, 1):
-            pct = round(score, 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!")
+        for film, score in top:
+            st.write(f"{film} → %{round(score,1)}")
+            st.progress(score/100)