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anime_recommender_lightfm.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83d69819b978699f6df64a1eee837d7d6f1f504f4b55bb76bd2d62ef92ef5e44
+size 2033431

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+
+# Modeli yükle
+with open("anime_recommender_lightfm.pkl", "rb") as f:
+    data = pickle.load(f)
+
+model = data["model"]
+dataset = data["dataset"]
+interactions = data["interactions"]
+user_id_map = data["user_id_map"]
+item_id_map = data["item_id_map"]
+anime_df = data["anime"]
+
+# Ters eşlemeler
+reverse_user_map = {v: k for k, v in user_id_map.items()}
+reverse_item_map = {v: k for k, v in item_id_map.items()}
+
+# Başlık
+st.title("🎌 Anime Tavsiye Sistemi (LightFM)")
+
+# Kullanıcı seçimi
+user_ids = list(user_id_map.keys())
+user_id_input = st.selectbox("👤 Bir kullanıcı ID seçin:", user_ids)
+
+# Kullanıcı indeksini al
+user_index = user_id_map[user_id_input]
+
+# Tüm anime indekslerini al
+n_items = interactions.shape[1]
+known_positives = interactions.tocsr()[user_index].indices
+
+# Öneri skorlarını hesapla
+scores = model.predict(user_ids=user_index, item_ids=np.arange(n_items))
+top_items = np.argsort(-scores)
+
+# Kullanıcının zaten izlediklerini göster
+st.subheader("✅ İzledikleriniz:")
+for idx in known_positives[:5]:
+    anime_id = reverse_item_map[idx]
+    title = anime_df[anime_df["anime_id"] == anime_id]["name"].values[0]
+    st.write("✔️", title)
+
+# Önerilen anime'ler
+st.subheader("🤖 Size Önerilenler:")
+count = 0
+for idx in top_items:
+    if idx not in known_positives:
+        anime_id = reverse_item_map[idx]
+        title = anime_df[anime_df["anime_id"] == anime_id]["name"].values[0]
+        st.write("⭐", title)
+        count += 1
+        if count >= 10:
+            break

requirements.txt

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+streamlit
+lightfm
+numpy
+pandas
+scikit-learn