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LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,14 +1,32 @@
----
-title: AnimeRecBERT
-emoji: 🐨
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 5.44.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: demo space for animerecbert
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Web Demo 🌐
+www.animerecbert.online
+
+## Main Code
+https://github.com/MRamazan/AnimeRecBERT
+
+## For Linux, Macos
+If you want to run locally, **main.py script automatically downloads 7 files from my google drive. pretrained model, dataset mappings, anime information files etc.**
+```bash
+git clone https://github.com/MRamazan/AnimeRecBertWeb
+cd AnimeRecBertWeb
+python3 -m venv venv
+source venv/bin/activate 
+pip install -r requirements.txt
+python main.py
+```
+
+## For Windows
+If you want to run locally, **main.py script automatically downloads 7 files from my google drive. pretrained model, dataset mappings, anime information files etc.**
+```bash
+git clone https://github.com/MRamazan/AnimeRecBertWeb
+cd AnimeRecBertWeb
+python -m venv venv
+venv\Scripts\activate 
+pip install -r requirements.txt
+python main.py
+```
+
+## Preview
+<img src="favorites-selection.jpg" alt="Recommendations" width="700">
+<img src="recommendations.jpg" alt="Favorite Selection" width="700">
+

config.py

@@ -0,0 +1,4 @@
+RAW_DATASET_ROOT_FOLDER = 'Data'
+
+STATE_DICT_KEY = 'model_state_dict'
+OPTIMIZER_STATE_DICT_KEY = 'optimizer_state_dict'

main.py

@@ -0,0 +1,845 @@
+from flask import Flask, render_template, request, jsonify, session, Response
+import sys
+import pickle
+import json
+import gc
+import weakref
+from pathlib import Path
+from utils import *
+from options import args
+from models import model_factory
+from flask_socketio import SocketIO, emit
+from datetime import datetime
+import random
+import re
+import xml.etree.ElementTree as ET
+
+app = Flask(__name__)
+app.secret_key = '1903bjk'
+socketio = SocketIO(app, cors_allowed_origins="*")
+
+# Memory-efficient chat system
+class ChatManager:
+    def __init__(self, max_messages=100):  # Reduced from 300
+        self.messages = []
+        self.active_users = {}
+        self.max_messages = max_messages
+    
+    def add_message(self, message):
+        self.messages.append(message)
+        if len(self.messages) > self.max_messages:
+            self.messages.pop(0)
+    
+    def get_messages(self):
+        return self.messages
+    
+    def add_user(self, sid, username):
+        self.active_users[sid] = {
+            'username': username,
+            'connected_at': datetime.now()
+        }
+    
+    def remove_user(self, sid):
+        return self.active_users.pop(sid, None)
+    
+    def get_user_count(self):
+        return len(self.active_users)
+    
+    def get_username(self, sid):
+        user = self.active_users.get(sid)
+        return user['username'] if user else None
+    
+    def update_username(self, sid, new_username):
+        if sid in self.active_users:
+            self.active_users[sid]['username'] = new_username
+
+chat_manager = ChatManager()
+
+def generate_username():
+    adjectives = ['Cool', 'Awesome', 'Swift', 'Bright', 'Happy', 'Smart', 'Kind', 'Brave', 'Calm', 'Epic', "Black"]
+    nouns = ['Otaku', 'Ninja', 'Samurai', 'Dragon', 'Phoenix', 'Tiger', 'Wolf', 'Eagle', 'Fox', 'Bear']
+    return f"{random.choice(adjectives)}{random.choice(nouns)}{random.randint(100, 999)}"
+
+def clean_message(message):
+    # HTML tag'leri temizle
+    message = re.sub(r'<[^>]*>', '', message)
+    # Uzunluk kontrolü
+    if len(message) > 500:
+        message = message[:500]
+    return message.strip()
+
+# Lazy loading için wrapper class
+class LazyDict:
+    def __init__(self, file_path):
+        self.file_path = file_path
+        self._data = None
+        self._loaded = False
+    
+    def _load_data(self):
+        if not self._loaded:
+            try:
+                with open(self.file_path, "r", encoding="utf-8") as file:
+                    self._data = json.load(file)
+                self._loaded = True
+            except Exception as e:
+                print(f"Warning: Could not load {self.file_path}: {str(e)}")
+                self._data = {}
+                self._loaded = True
+    
+    def get(self, key, default=None):
+        self._load_data()
+        return self._data.get(key, default)
+    
+    def __contains__(self, key):
+        self._load_data()
+        return key in self._data
+    
+    def items(self):
+        self._load_data()
+        return self._data.items()
+    
+    def keys(self):
+        self._load_data()
+        return self._data.keys()
+    
+    def __len__(self):
+        self._load_data()
+        return len(self._data)
+
+# Sitemap route'ları
+@app.route('/sitemap.xml')
+def sitemap():
+    """Dinamik sitemap.xml oluşturur"""
+    try:
+        # XML root element
+        urlset = ET.Element('urlset')
+        urlset.set('xmlns', 'http://www.sitemaps.org/schemas/sitemap/0.9')
+        urlset.set('xmlns:image', 'http://www.google.com/schemas/sitemap-image/1.1')
+
+        # Base URL
+        base_url = request.url_root.rstrip('/')
+        current_date = datetime.now().strftime('%Y-%m-%d')
+
+        # Ana sayfa
+        url = ET.SubElement(urlset, 'url')
+        ET.SubElement(url, 'loc').text = f'{base_url}/'
+        ET.SubElement(url, 'lastmod').text = current_date
+        ET.SubElement(url, 'changefreq').text = 'daily'
+        ET.SubElement(url, 'priority').text = '1.0'
+
+        # Chat sayfası
+        url = ET.SubElement(urlset, 'url')
+        ET.SubElement(url, 'loc').text = f'{base_url}/chat'
+        ET.SubElement(url, 'lastmod').text = current_date
+        ET.SubElement(url, 'changefreq').text = 'hourly'
+        ET.SubElement(url, 'priority').text = '0.8'
+
+        # Anime sayfaları (sadece ilk 50 anime - SEO için)
+        if recommendation_system and recommendation_system.id_to_anime:
+            anime_count = 0
+            for anime_id, anime_data in recommendation_system.id_to_anime.items():
+                if anime_count >= 50:  # Reduced from 100
+                    break
+
+                try:
+                    anime_name = anime_data[0] if isinstance(anime_data, list) and len(anime_data) > 0 else str(anime_data)
+                    safe_name = anime_name.replace(' ', '-').replace('/', '-').replace('?', '').replace('&', 'and')
+                    safe_name = re.sub(r'[^\w\-]', '', safe_name)
+
+                    url = ET.SubElement(urlset, 'url')
+                    ET.SubElement(url, 'loc').text = f'{base_url}/anime/{anime_id}/{safe_name}'
+                    ET.SubElement(url, 'lastmod').text = current_date
+                    ET.SubElement(url, 'changefreq').text = 'weekly'
+                    ET.SubElement(url, 'priority').text = '0.6'
+
+                    # Sadece gerekli durumlarda resim URL'si ekle
+                    if anime_count < 20:  # Sadece ilk 20 anime için resim
+                        image_url = recommendation_system.get_anime_image_url(int(anime_id))
+                        if image_url:
+                            image_elem = ET.SubElement(url, 'image:image')
+                            ET.SubElement(image_elem, 'image:loc').text = image_url
+                            ET.SubElement(image_elem, 'image:title').text = anime_name
+                            ET.SubElement(image_elem, 'image:caption').text = f'Poster image for {anime_name}'
+
+                    anime_count += 1
+                except Exception as e:
+                    print(f"Error processing anime {anime_id}: {e}")
+                    continue
+
+        # XML'i string'e çevir
+        xml_str = ET.tostring(urlset, encoding='unicode')
+        xml_declaration = '<?xml version="1.0" encoding="UTF-8"?>\n'
+        full_xml = xml_declaration + xml_str
+
+        return Response(full_xml, mimetype='application/xml')
+
+    except Exception as e:
+        print(f"Sitemap generation error: {e}")
+        return Response(
+            '<?xml version="1.0" encoding="UTF-8"?><urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"></urlset>',
+            mimetype='application/xml')
+
+@app.route('/robots.txt')
+def robots_txt():
+    """Robots.txt dosyası"""
+    robots_content = f"""User-agent: *
+Allow: /
+Allow: /chat
+
+Sitemap: {request.url_root.rstrip('/')}/sitemap.xml
+"""
+    return Response(robots_content, mimetype='text/plain')
+
+@app.route('/anime/<int:anime_id>/<path:anime_name>')
+def anime_detail(anime_id, anime_name):
+    """Anime detay sayfası (SEO için)"""
+    if not recommendation_system or str(anime_id) not in recommendation_system.id_to_anime:
+        return render_template('error.html', error="Anime not found"), 404
+
+    anime_data = recommendation_system.id_to_anime.get(str(anime_id))
+    anime_name = anime_data[0] if isinstance(anime_data, list) and len(anime_data) > 0 else str(anime_data)
+
+    # Anime bilgilerini lazy loading ile al
+    image_url = recommendation_system.get_anime_image_url(anime_id)
+    mal_url = recommendation_system.get_anime_mal_url(anime_id)
+    genres = recommendation_system.get_anime_genres(anime_id)
+    anime_type = recommendation_system._get_type(anime_id)
+
+    # Benzer animeler öner (sadece 5 tane)
+    similar_animes = []
+    try:
+        recommendations, _, _ = recommendation_system.get_recommendations([anime_id], num_recommendations=5)
+        similar_animes = recommendations
+    except:
+        pass
+
+    anime_info = {
+        'id': anime_id,
+        'name': anime_name,
+        'image_url': image_url,
+        'mal_url': mal_url,
+        'genres': genres,
+        'similar_animes': similar_animes,
+        'type': anime_type
+    }
+
+    # JSON-LD structured data oluştur
+    structured_data = generate_anime_structured_data(anime_info)
+
+    return render_template('anime_detail.html', anime=anime_info, structured_data=json.dumps(structured_data))
+
+def generate_anime_structured_data(anime_info):
+    """Anime için JSON-LD structured data oluşturur"""
+    structured_data = {
+        "@context": "https://schema.org",
+        "@type": anime_info["type"],
+        "name": anime_info['name'],
+        "url": f"{request.url_root.rstrip('/')}/anime/{anime_info['id']}/{anime_info['name'].replace(' ', '-')}"
+    }
+
+    if anime_info['genres']:
+        structured_data["genre"] = anime_info['genres']
+
+    if anime_info['image_url']:
+        structured_data["image"] = anime_info['image_url']
+
+    if anime_info['mal_url']:
+        structured_data["sameAs"] = anime_info['mal_url']
+
+    return structured_data
+
+@app.route('/sitemap-index.xml')
+def sitemap_index():
+    """Sitemap index dosyası"""
+    try:
+        sitemapindex = ET.Element('sitemapindex')
+        sitemapindex.set('xmlns', 'http://www.sitemaps.org/schemas/sitemap/0.9')
+
+        base_url = request.url_root.rstrip('/')
+        current_date = datetime.now().strftime('%Y-%m-%d')
+
+        # Ana sitemap
+        sitemap = ET.SubElement(sitemapindex, 'sitemap')
+        ET.SubElement(sitemap, 'loc').text = f'{base_url}/sitemap.xml'
+        ET.SubElement(sitemap, 'lastmod').text = current_date
+
+        xml_str = ET.tostring(sitemapindex, encoding='unicode')
+        xml_declaration = '<?xml version="1.0" encoding="UTF-8"?>\n'
+        full_xml = xml_declaration + xml_str
+
+        return Response(full_xml, mimetype='application/xml')
+
+    except Exception as e:
+        print(f"Sitemap index generation error: {e}")
+        return Response(
+            '<?xml version="1.0" encoding="UTF-8"?><sitemapindex xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"></sitemapindex>',
+            mimetype='application/xml')
+
+@app.route('/chat')
+def chat():
+    return render_template('chat.html')
+
+# SocketIO event'leri
+@socketio.on('connect')
+def on_connect():
+    username = generate_username()
+    chat_manager.add_user(request.sid, username)
+
+    # Kullanıcıya mevcut mesajları gönder
+    emit('chat_history', chat_manager.get_messages())
+
+    # Kullanıcı katıldı mesajı
+    join_message = {
+        'username': 'System',
+        'message': f'{username} joined the chat',
+        'timestamp': datetime.now().strftime('%H:%M'),
+        'type': 'system'
+    }
+
+    chat_manager.add_message(join_message)
+    emit('new_message', join_message, broadcast=True)
+    emit('user_count', chat_manager.get_user_count(), broadcast=True)
+
+@socketio.on('disconnect')
+def on_disconnect():
+    user = chat_manager.remove_user(request.sid)
+    if user:
+        username = user['username']
+        leave_message = {
+            'username': 'System',
+            'message': f'{username} left the chat',
+            'timestamp': datetime.now().strftime('%H:%M'),
+            'type': 'system'
+        }
+
+        chat_manager.add_message(leave_message)
+        emit('new_message', leave_message, broadcast=True)
+        emit('user_count', chat_manager.get_user_count(), broadcast=True)
+
+@socketio.on('send_message')
+def handle_message(data):
+    username = chat_manager.get_username(request.sid)
+    if not username:
+        return
+
+    message = clean_message(data.get('message', ''))
+    if not message:
+        return
+
+    message_obj = {
+        'username': username,
+        'message': message,
+        'timestamp': datetime.now().strftime('%H:%M'),
+        'type': 'user'
+    }
+
+    chat_manager.add_message(message_obj)
+    emit('new_message', message_obj, broadcast=True)
+
+@socketio.on('change_username')
+def handle_username_change(data):
+    old_username = chat_manager.get_username(request.sid)
+    if not old_username:
+        return
+
+    new_username = clean_message(data.get('username', ''))
+    if not new_username or len(new_username) < 2:
+        return
+
+    chat_manager.update_username(request.sid, new_username)
+
+    change_message = {
+        'username': 'System',
+        'message': f'{old_username} changed name to {new_username}',
+        'timestamp': datetime.now().strftime('%H:%M'),
+        'type': 'system'
+    }
+
+    chat_manager.add_message(change_message)
+    emit('new_message', change_message, broadcast=True)
+    emit('username_changed', {'username': new_username})
+
+class AnimeRecommendationSystem:
+    def __init__(self, checkpoint_path, dataset_path, animes_path, images_path, mal_urls_path, type_seq_path, genres_path):
+        self.model = None
+        self.dataset = None
+        self.checkpoint_path = checkpoint_path
+        self.dataset_path = dataset_path
+        self.animes_path = animes_path
+        
+        # Lazy loading ile memory optimization
+        self.id_to_anime = LazyDict(animes_path)
+        self.id_to_url = LazyDict(images_path)
+        self.id_to_mal_url = LazyDict(mal_urls_path)
+        self.id_to_type_seq = LazyDict(type_seq_path)
+        self.id_to_genres = LazyDict(genres_path)
+        
+        # Cache için weak reference kullan
+        self._cache = {}
+        
+        self.load_model_and_data()
+
+    def load_model_and_data(self):
+        try:
+            print("Loading model and data...")
+            args.bert_max_len = 128
+
+            # Dataset'i yükle
+            dataset_path = Path(self.dataset_path)
+            with dataset_path.open('rb') as f:
+                self.dataset = pickle.load(f)
+
+            # Model'i yükle
+            self.model = model_factory(args)
+            self.load_checkpoint()
+
+            # Garbage collection
+            gc.collect()
+            print("Model loaded successfully!")
+
+        except Exception as e:
+            print(f"Error loading model: {str(e)}")
+            raise e
+
+    def load_checkpoint(self):
+        try:
+            with open(self.checkpoint_path, 'rb') as f:
+                checkpoint = torch.load(f, map_location='cpu', weights_only=False)
+            self.model.load_state_dict(checkpoint['model_state_dict'])
+            self.model.eval()
+            
+            # Checkpoint'i bellekten temizle
+            del checkpoint
+            gc.collect()
+            
+        except Exception as e:
+            raise Exception(f"Failed to load checkpoint from {self.checkpoint_path}: {str(e)}")
+
+    def get_anime_genres(self, anime_id):
+        genres = self.id_to_genres.get(str(anime_id), [])
+        return [genre.title() for genre in genres] if genres else []
+
+    def get_all_animes(self):
+        """Tüm anime listesini döndürür - cache kullanır"""
+        cache_key = 'all_animes'
+        if cache_key in self._cache:
+            return self._cache[cache_key]
+        
+        animes = []
+        # Sadece gerekli durumlarda yükle
+        for k, v in list(self.id_to_anime.items())[:1000]:  # İlk 1000 anime
+            anime_name = v[0] if isinstance(v, list) and len(v) > 0 else str(v)
+            animes.append((int(k), anime_name))
+        
+        animes.sort(key=lambda x: x[1])
+        self._cache[cache_key] = animes
+        return animes
+
+    def get_anime_image_url(self, anime_id):
+        return self.id_to_url.get(str(anime_id), None)
+
+    def get_anime_mal_url(self, anime_id):
+        return self.id_to_mal_url.get(str(anime_id), None)
+
+    def get_filtered_anime_pool(self, filters):
+        """Filtrelere göre anime havuzunu önceden filtreler"""
+        if not filters:
+            return None
+
+        if filters.get('show_hentai') and len([k for k, v in filters.items() if v]) == 1:
+            hentai_animes = []
+            # Sadece gerekli verileri kontrol et
+            for anime_id_str in list(self.id_to_anime.keys())[:500]:  # Limit
+                anime_id = int(anime_id_str)
+                if self._is_hentai(anime_id):
+                    hentai_animes.append(anime_id)
+            return hentai_animes
+
+        return None
+
+    def _is_hentai(self, anime_id):
+        """Anime'nin hentai olup olmadığını kontrol eder"""
+        type_seq_info = self.id_to_type_seq.get(str(anime_id))
+        if not type_seq_info or len(type_seq_info) < 3:
+            return False
+        return type_seq_info[2]
+
+    def _get_type(self, anime_id):
+        """Anime tipini döndürür"""
+        type_seq_info = self.id_to_type_seq.get(str(anime_id))
+        if not type_seq_info or len(type_seq_info) < 2:
+            return "Unknown"
+        return type_seq_info[1]
+
+    def get_recommendations(self, favorite_anime_ids, num_recommendations=20, filters=None):  # Reduced from 40
+        try:
+            if not favorite_anime_ids:
+                return [], [], "Please add some favorite animes first!"
+
+            smap = self.dataset
+            inverted_smap = {v: k for k, v in smap.items()}
+
+            converted_ids = []
+            for anime_id in favorite_anime_ids:
+                if anime_id in smap:
+                    converted_ids.append(smap[anime_id])
+
+            if not converted_ids:
+                return [], [], "None of the selected animes are in the model vocabulary!"
+
+            # Hentai filtresi özel durumu
+            filtered_pool = self.get_filtered_anime_pool(filters)
+            if filtered_pool is not None:
+                return self._get_recommendations_from_pool(favorite_anime_ids, filtered_pool, num_recommendations, filters)
+
+            # Normal öneriler
+            target_len = 128
+            padded = converted_ids + [0] * (target_len - len(converted_ids))
+            input_tensor = torch.tensor(padded, dtype=torch.long).unsqueeze(0)
+
+            max_predictions = min(75, len(inverted_smap))  # Reduced from 125
+
+            with torch.no_grad():
+                logits = self.model(input_tensor)
+                last_logits = logits[:, -1, :]
+                top_scores, top_indices = torch.topk(last_logits, k=max_predictions, dim=1)
+
+            recommendations = []
+            scores = []
+
+            for idx, score in zip(top_indices.numpy()[0], top_scores.detach().numpy()[0]):
+                if idx in inverted_smap:
+                    anime_id = inverted_smap[idx]
+
+                    if anime_id in favorite_anime_ids:
+                        continue
+
+                    if str(anime_id) in self.id_to_anime:
+                        # Filtreleme kontrolü
+                        if filters and not self._should_include_anime(anime_id, filters):
+                            continue
+
+                        anime_data = self.id_to_anime.get(str(anime_id))
+                        anime_name = anime_data[0] if isinstance(anime_data, list) and len(anime_data) > 0 else str(anime_data)
+                        
+                        # Lazy loading ile image ve mal url al
+                        image_url = self.get_anime_image_url(anime_id)
+                        mal_url = self.get_anime_mal_url(anime_id)
+
+                        recommendations.append({
+                            'id': anime_id,
+                            'name': anime_name,
+                            'score': float(score),
+                            'image_url': image_url,
+                            'mal_url': mal_url,
+                            'genres': self.get_anime_genres(anime_id)
+                        })
+                        scores.append(float(score))
+
+                        if len(recommendations) >= num_recommendations:
+                            break
+
+            # Memory cleanup
+            del logits, last_logits, top_scores, top_indices
+            gc.collect()
+
+            return recommendations, scores, f"Found {len(recommendations)} recommendations!"
+
+        except Exception as e:
+            return [], [], f"Error during prediction: {str(e)}"
+
+    def _get_recommendations_from_pool(self, favorite_anime_ids, anime_pool, num_recommendations, filters):
+        """Önceden filtrelenmiş anime havuzundan öneriler alır"""
+        try:
+            smap = self.dataset
+            converted_ids = []
+            for anime_id in favorite_anime_ids:
+                if anime_id in smap:
+                    converted_ids.append(smap[anime_id])
+
+            if not converted_ids:
+                return [], [], "None of the selected animes are in the model vocabulary!"
+
+            target_len = 128
+            padded = converted_ids + [0] * (target_len - len(converted_ids))
+            input_tensor = torch.tensor(padded, dtype=torch.long).unsqueeze(0)
+
+            with torch.no_grad():
+                logits = self.model(input_tensor)
+                last_logits = logits[:, -1, :]
+
+            # Anime havuzundaki her anime için skor hesapla
+            anime_scores = []
+            for anime_id in anime_pool:
+                if anime_id in favorite_anime_ids:
+                    continue
+
+                if anime_id in smap:
+                    model_id = smap[anime_id]
+                    if model_id < last_logits.shape[1]:
+                        score = last_logits[0, model_id].item()
+                        anime_scores.append((anime_id, score))
+
+            # Skorlara göre sırala
+            anime_scores.sort(key=lambda x: x[1], reverse=True)
+
+            recommendations = []
+            for anime_id, score in anime_scores[:num_recommendations]:
+                if str(anime_id) in self.id_to_anime:
+                    anime_data = self.id_to_anime.get(str(anime_id))
+                    anime_name = anime_data[0] if isinstance(anime_data, list) and len(anime_data) > 0 else str(anime_data)
+                    
+                    recommendations.append({
+                        'id': anime_id,
+                        'name': anime_name,
+                        'score': float(score),
+                        'image_url': self.get_anime_image_url(anime_id),
+                        'mal_url': self.get_anime_mal_url(anime_id),
+                        'genres': self.get_anime_genres(anime_id)
+                    })
+
+            # Memory cleanup
+            del logits, last_logits
+            gc.collect()
+
+            return recommendations, [r['score'] for r in recommendations], f"Found {len(recommendations)} filtered recommendations!"
+
+        except Exception as e:
+            return [], [], f"Error during filtered prediction: {str(e)}"
+
+    def _should_include_anime(self, anime_id, filters):
+        """Filtrelere göre anime'nin dahil edilip edilmeyeceğini kontrol eder"""
+        if 'blacklisted_animes' in filters:
+            if anime_id in filters['blacklisted_animes']:
+                return False
+
+        type_seq_info = self.id_to_type_seq.get(str(anime_id))
+        if not type_seq_info or len(type_seq_info) < 2:
+            return True
+
+        anime_type = type_seq_info[0]
+        is_sequel = type_seq_info[1]
+        is_hentai = type_seq_info[2]
+
+        # Sequel filtresi
+        if 'show_sequels' in filters:
+            if not filters['show_sequels'] and is_sequel:
+                return False
+
+        # Hentai filtresi
+        if 'show_hentai' in filters:
+            if filters['show_hentai']:
+                if not is_hentai:
+                    return False
+            else:
+                if is_hentai:
+                    return False
+
+        # Tür filtreleri
+        if 'show_movies' in filters:
+            if not filters['show_movies'] and anime_type == 'MOVIE':
+                return False
+
+        if 'show_tv' in filters:
+            if not filters['show_tv'] and anime_type == 'TV':
+                return False
+
+        if 'show_ova' in filters:
+            if not filters['show_ova'] and anime_type in ['ONA', 'OVA', 'SPECIAL']:
+                return False
+
+        return True
+
+recommendation_system = None
+
+@app.route('/')
+def index():
+    if recommendation_system is None:
+        return render_template('error.html', error="Recommendation system not initialized. Please check server logs.")
+
+    animes = recommendation_system.get_all_animes()
+    return render_template('index.html', animes=animes)
+
+@app.route('/api/search_animes')
+def search_animes():
+    query = request.args.get('q', '').lower()
+    animes = []
+    
+    # Sadece ilk 200 anime'yi arama - performance için
+    count = 0
+    for k, v in recommendation_system.id_to_anime.items():
+        if count >= 200:
+            break
+            
+        anime_names = v if isinstance(v, list) else [v]
+        match_found = False
+        
+        for name in anime_names:
+            if query in name.lower():
+                match_found = True
+                break
+
+        if not query or match_found:
+            main_name = anime_names[0] if anime_names else "Unknown"
+            animes.append((int(k), main_name))
+            count += 1
+
+    animes.sort(key=lambda x: x[1])
+    return jsonify(animes)
+
+@app.route('/api/add_favorite', methods=['POST'])
+def add_favorite():
+    if 'favorites' not in session:
+        session['favorites'] = []
+
+    data = request.get_json()
+    anime_id = int(data['anime_id'])
+
+    if anime_id not in session['favorites']:
+        # Maksimum 20 favori anime (memory için)
+        if len(session['favorites']) >= 20:
+            return jsonify({'success': False, 'message': 'Maximum 20 favorite animes allowed'})
+        
+        session['favorites'].append(anime_id)
+        session.modified = True
+        return jsonify({'success': True})
+    else:
+        return jsonify({'success': False})
+
+@app.route('/api/remove_favorite', methods=['POST'])
+def remove_favorite():
+    if 'favorites' not in session:
+        session['favorites'] = []
+
+    data = request.get_json()
+    anime_id = int(data['anime_id'])
+
+    if anime_id in session['favorites']:
+        session['favorites'].remove(anime_id)
+        session.modified = True
+        return jsonify({'success': True})
+    else:
+        return jsonify({'success': False})
+
+@app.route('/api/clear_favorites', methods=['POST'])
+def clear_favorites():
+    session['favorites'] = []
+    session.modified = True
+    return jsonify({'success': True})
+
+@app.route('/api/get_favorites')
+def get_favorites():
+    if 'favorites' not in session:
+        session['favorites'] = []
+
+    favorite_animes = []
+    for anime_id in session['favorites']:
+        if str(anime_id) in recommendation_system.id_to_anime:
+            anime_data = recommendation_system.id_to_anime.get(str(anime_id))
+            anime_name = anime_data[0] if isinstance(anime_data, list) and len(anime_data) > 0 else str(anime_data)
+            favorite_animes.append({'id': anime_id, 'name': anime_name})
+
+    return jsonify(favorite_animes)
+
+
+@app.route('/api/get_recommendations', methods=['POST'])
+def get_recommendations():
+    if 'favorites' not in session or not session['favorites']:
+        return jsonify({'success': False, 'message': 'Please add some favorite animes first!'})
+
+    data = request.get_json() or {}
+    filters = data.get('filters', {})
+
+    # Blacklist bilgisini ekle
+    blacklisted_animes = data.get('blacklisted_animes', [])
+    if blacklisted_animes:
+        filters['blacklisted_animes'] = blacklisted_animes
+
+    recommendations, scores, message = recommendation_system.get_recommendations(
+        session['favorites'],
+        filters=filters
+    )
+
+    if recommendations:
+        return jsonify({
+            'success': True,
+            'recommendations': recommendations,
+            'message': message
+        })
+    else:
+        return jsonify({'success': False, 'message': message})
+
+
+@app.route('/api/mal_logo')
+def get_mal_logo():
+    # MyAnimeList logo URL'ini döndür
+    return jsonify({
+        'success': True,
+        'logo_url': 'https://cdn.myanimelist.net/img/sp/icon/apple-touch-icon-256.png'
+    })
+
+
+def main():
+    global recommendation_system
+
+    args.num_items = 12689
+
+    import gdown
+    import os
+
+    file_ids = {
+        "1C6mdjblhiWGhRgbIk5DP2XCc4ElS9x8p": "pretrained_bert.pth",
+        "1U42cFrdLFT8NVNikT9C5SD9aAux7a5U2": "animes.json",
+        "1s-8FM1Wi2wOWJ9cstvm-O1_6XculTcTG": "dataset.pkl",
+        "1SOm1llcTKfhr-RTHC0dhaZ4AfWPs8wRx": "id_to_url.json",
+        "1vwJEMEOIYwvCKCCbbeaP0U_9L3NhvBzg": "anime_to_malurl.json",
+        "1_TyzON6ie2CqvzVNvPyc9prMTwLMefdu": "anime_to_typenseq.json",
+        "1G9O_ahyuJ5aO0cwoVnIXrlzMqjKrf2aw": "id_to_genres.json"
+    }
+
+    def download_from_gdrive(file_id, output_path):
+        url = f"https://drive.google.com/uc?id={file_id}"
+        try:
+            print(f"Downloading: {file_id}")
+            gdown.download(url, output_path, quiet=False)
+            print(f"Downloaded: {output_path}")
+            return True
+        except Exception as e:
+            print(f"Error: {e}")
+            return False
+
+    for key, value in file_ids.items():
+        if os.path.isfile(value):
+            continue
+        download_from_gdrive(key, value)
+
+    try:
+        images_path = "id_to_url.json"
+        mal_urls_path = "anime_to_malurl.json"
+        type_seq_path = "anime_to_typenseq.json"
+
+        if not os.path.exists(images_path):
+            print(f"Warning: {images_path} not found. Images will not be displayed.")
+
+        if not os.path.exists(mal_urls_path):
+            print(f"Warning: {mal_urls_path} not found. MAL links will not be available.")
+
+        recommendation_system = AnimeRecommendationSystem(
+            "pretrained_bert.pth",
+            "dataset.pkl",
+            "animes.json",
+            images_path,
+            mal_urls_path,
+            type_seq_path,
+            "id_to_genres.json"
+        )
+        print("Recommendation system initialized successfully!")
+    except Exception as e:
+        print(f"Failed to initialize recommendation system: {e}")
+        sys.exit(1)
+
+    app.run(debug=False, host='0.0.0.0', port=5000)
+
+
+if __name__ == "__main__":
+    main()

models/__init__.py

@@ -0,0 +1,14 @@
+from .bert import BERTModel
+from .dae import DAEModel
+from .vae import VAEModel
+
+MODELS = {
+    BERTModel.code(): BERTModel,
+    DAEModel.code(): DAEModel,
+    VAEModel.code(): VAEModel
+}
+
+
+def model_factory(args):
+    model = MODELS[args.model_code]
+    return model(args)

models/base.py

@@ -0,0 +1,15 @@
+import torch.nn as nn
+
+from abc import *
+
+
+class BaseModel(nn.Module, metaclass=ABCMeta):
+    def __init__(self, args):
+        super().__init__()
+        self.args = args
+
+    @classmethod
+    @abstractmethod
+    def code(cls):
+        pass
+

models/bert.py

@@ -0,0 +1,19 @@
+from .base import BaseModel
+from .bert_modules.bert import BERT
+
+import torch.nn as nn
+
+
+class BERTModel(BaseModel):
+    def __init__(self, args):
+        super().__init__(args)
+        self.bert = BERT(args)
+        self.out = nn.Linear(self.bert.hidden, args.num_items + 1)
+
+    @classmethod
+    def code(cls):
+        return 'bert'
+
+    def forward(self, x):
+        x = self.bert(x)
+        return self.out(x)

models/bert_modules/__init__.py

@@ -0,0 +1 @@
+

models/bert_modules/attention/__init__.py

@@ -0,0 +1,2 @@
+from .multi_head import MultiHeadedAttention
+from .single import Attention

models/bert_modules/attention/multi_head.py

@@ -0,0 +1,37 @@
+import torch.nn as nn
+from .single import Attention
+
+
+class MultiHeadedAttention(nn.Module):
+    """
+    Take in model size and number of heads.
+    """
+
+    def __init__(self, h, d_model, dropout=0.1):
+        super().__init__()
+        assert d_model % h == 0
+
+        # We assume d_v always equals d_k
+        self.d_k = d_model // h
+        self.h = h
+
+        self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])
+        self.output_linear = nn.Linear(d_model, d_model)
+        self.attention = Attention()
+
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, query, key, value, mask=None):
+        batch_size = query.size(0)
+
+        # 1) Do all the linear projections in batch from d_model => h x d_k
+        query, key, value = [l(x).view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
+                             for l, x in zip(self.linear_layers, (query, key, value))]
+
+        # 2) Apply attention on all the projected vectors in batch.
+        x, attn = self.attention(query, key, value, mask=mask, dropout=self.dropout)
+
+        # 3) "Concat" using a view and apply a final linear.
+        x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k)
+
+        return self.output_linear(x)

models/bert_modules/attention/single.py

@@ -0,0 +1,25 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+import math
+
+
+class Attention(nn.Module):
+    """
+    Compute 'Scaled Dot Product Attention
+    """
+
+    def forward(self, query, key, value, mask=None, dropout=None):
+        scores = torch.matmul(query, key.transpose(-2, -1)) \
+                 / math.sqrt(query.size(-1))
+
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e9)
+
+        p_attn = F.softmax(scores, dim=-1)
+
+        if dropout is not None:
+            p_attn = dropout(p_attn)
+
+        return torch.matmul(p_attn, value), p_attn

models/bert_modules/bert.py

@@ -0,0 +1,44 @@
+from torch import nn as nn
+
+from models.bert_modules.embedding import BERTEmbedding
+from models.bert_modules.transformer import TransformerBlock
+from utils import fix_random_seed_as
+
+
+class BERT(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        fix_random_seed_as(args.model_init_seed)
+        # self.init_weights()
+
+        max_len = args.bert_max_len
+        num_items = args.num_items
+        n_layers = args.bert_num_blocks
+        heads = args.bert_num_heads
+        vocab_size = num_items + 2
+        hidden = args.bert_hidden_units
+        self.hidden = hidden
+        dropout = args.bert_dropout
+
+        # embedding for BERT, sum of positional, segment, token embeddings
+        self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=self.hidden, max_len=max_len, dropout=dropout)
+
+        # multi-layers transformer blocks, deep network
+        self.transformer_blocks = nn.ModuleList(
+            [TransformerBlock(hidden, heads, hidden * 4, dropout) for _ in range(n_layers)])
+
+    def forward(self, x):
+        mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)
+
+        # embedding the indexed sequence to sequence of vectors
+        x = self.embedding(x)
+
+        # running over multiple transformer blocks
+        for transformer in self.transformer_blocks:
+            x = transformer.forward(x, mask)
+
+        return x
+
+    def init_weights(self):
+        pass

models/bert_modules/embedding/__init__.py

@@ -0,0 +1 @@
+from .bert import BERTEmbedding

models/bert_modules/embedding/bert.py

@@ -0,0 +1,31 @@
+import torch.nn as nn
+from .token import TokenEmbedding
+from .position import PositionalEmbedding
+
+
+class BERTEmbedding(nn.Module):
+    """
+    BERT Embedding which is consisted with under features
+        1. TokenEmbedding : normal embedding matrix
+        2. PositionalEmbedding : adding positional information using sin, cos
+        2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2)
+
+        sum of all these features are output of BERTEmbedding
+    """
+
+    def __init__(self, vocab_size, embed_size, max_len, dropout=0.1):
+        """
+        :param vocab_size: total vocab size
+        :param embed_size: embedding size of token embedding
+        :param dropout: dropout rate
+        """
+        super().__init__()
+        self.token = TokenEmbedding(vocab_size=vocab_size, embed_size=embed_size)
+        self.position = PositionalEmbedding(max_len=max_len, d_model=embed_size)
+        # self.segment = SegmentEmbedding(embed_size=self.token.embedding_dim)
+        self.dropout = nn.Dropout(p=dropout)
+        self.embed_size = embed_size
+
+    def forward(self, sequence):
+        x = self.token(sequence) # + self.position(sequence)  # + self.segment(segment_label)
+        return self.dropout(x)

models/bert_modules/embedding/position.py

@@ -0,0 +1,16 @@
+import torch.nn as nn
+import torch
+import math
+
+
+class PositionalEmbedding(nn.Module):
+
+    def __init__(self, max_len, d_model):
+        super().__init__()
+
+        # Compute the positional encodings once in log space.
+        self.pe = nn.Embedding(max_len, d_model)
+
+    def forward(self, x):
+        batch_size = x.size(0)
+        return self.pe.weight.unsqueeze(0).repeat(batch_size, 1, 1)

models/bert_modules/embedding/segment.py

@@ -0,0 +1,6 @@
+import torch.nn as nn
+
+
+class SegmentEmbedding(nn.Embedding):
+    def __init__(self, embed_size=512):
+        super().__init__(3, embed_size, padding_idx=0)

models/bert_modules/embedding/token.py

@@ -0,0 +1,6 @@
+import torch.nn as nn
+
+
+class TokenEmbedding(nn.Embedding):
+    def __init__(self, vocab_size, embed_size=512):
+        super().__init__(vocab_size, embed_size, padding_idx=0)

models/bert_modules/transformer.py

@@ -0,0 +1,31 @@
+import torch.nn as nn
+
+from .attention import MultiHeadedAttention
+from .utils import SublayerConnection, PositionwiseFeedForward
+
+
+class TransformerBlock(nn.Module):
+    """
+    Bidirectional Encoder = Transformer (self-attention)
+    Transformer = MultiHead_Attention + Feed_Forward with sublayer connection
+    """
+
+    def __init__(self, hidden, attn_heads, feed_forward_hidden, dropout):
+        """
+        :param hidden: hidden size of transformer
+        :param attn_heads: head sizes of multi-head attention
+        :param feed_forward_hidden: feed_forward_hidden, usually 4*hidden_size
+        :param dropout: dropout rate
+        """
+
+        super().__init__()
+        self.attention = MultiHeadedAttention(h=attn_heads, d_model=hidden, dropout=dropout)
+        self.feed_forward = PositionwiseFeedForward(d_model=hidden, d_ff=feed_forward_hidden, dropout=dropout)
+        self.input_sublayer = SublayerConnection(size=hidden, dropout=dropout)
+        self.output_sublayer = SublayerConnection(size=hidden, dropout=dropout)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, mask):
+        x = self.input_sublayer(x, lambda _x: self.attention.forward(_x, _x, _x, mask=mask))
+        x = self.output_sublayer(x, self.feed_forward)
+        return self.dropout(x)

models/bert_modules/utils/__init__.py

@@ -0,0 +1,4 @@
+from .feed_forward import PositionwiseFeedForward
+from .layer_norm import LayerNorm
+from .sublayer import SublayerConnection
+from .gelu import GELU

models/bert_modules/utils/feed_forward.py

@@ -0,0 +1,16 @@
+import torch.nn as nn
+from .gelu import GELU
+
+
+class PositionwiseFeedForward(nn.Module):
+    "Implements FFN equation."
+
+    def __init__(self, d_model, d_ff, dropout=0.1):
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = nn.Linear(d_model, d_ff)
+        self.w_2 = nn.Linear(d_ff, d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = GELU()
+
+    def forward(self, x):
+        return self.w_2(self.dropout(self.activation(self.w_1(x))))

models/bert_modules/utils/gelu.py

@@ -0,0 +1,12 @@
+import torch.nn as nn
+import torch
+import math
+
+
+class GELU(nn.Module):
+    """
+    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
+    """
+
+    def forward(self, x):
+        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

models/bert_modules/utils/layer_norm.py

@@ -0,0 +1,17 @@
+import torch.nn as nn
+import torch
+
+
+class LayerNorm(nn.Module):
+    "Construct a layernorm module (See citation for details)."
+
+    def __init__(self, features, eps=1e-6):
+        super(LayerNorm, self).__init__()
+        self.a_2 = nn.Parameter(torch.ones(features))
+        self.b_2 = nn.Parameter(torch.zeros(features))
+        self.eps = eps
+
+    def forward(self, x):
+        mean = x.mean(-1, keepdim=True)
+        std = x.std(-1, keepdim=True)
+        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2

models/bert_modules/utils/sublayer.py

@@ -0,0 +1,18 @@
+import torch.nn as nn
+from .layer_norm import LayerNorm
+
+
+class SublayerConnection(nn.Module):
+    """
+    A residual connection followed by a layer norm.
+    Note for code simplicity the norm is first as opposed to last.
+    """
+
+    def __init__(self, size, dropout):
+        super(SublayerConnection, self).__init__()
+        self.norm = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, sublayer):
+        "Apply residual connection to any sublayer with the same size."
+        return x + self.dropout(sublayer(self.norm(x)))

models/dae.py

@@ -0,0 +1,54 @@
+from .base import BaseModel
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DAEModel(BaseModel):
+    def __init__(self, args):
+        super().__init__(args)
+
+        # Input dropout
+        self.input_dropout = nn.Dropout(p=args.dae_dropout)
+
+        # Construct a list of dimensions for the encoder and the decoder
+        dims = [args.dae_hidden_dim] * 2 * args.dae_num_hidden
+        dims = [args.num_items] + dims + [args.dae_latent_dim]
+
+        # Stack encoders and decoders
+        encoder_modules, decoder_modules = [], []
+        for i in range(len(dims)//2):
+            encoder_modules.append(nn.Linear(dims[2*i], dims[2*i+1]))
+            decoder_modules.append(nn.Linear(dims[-2*i-1], dims[-2*i-2]))
+        self.encoder = nn.ModuleList(encoder_modules)
+        self.decoder = nn.ModuleList(decoder_modules)
+
+        # Initialize weights
+        self.encoder.apply(self.weight_init)
+        self.decoder.apply(self.weight_init)
+
+    def weight_init(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.kaiming_normal_(m.weight)
+            m.bias.data.normal_(0.0, 0.001)
+
+    @classmethod
+    def code(cls):
+        return 'dae'
+
+    def forward(self, x):
+        x = F.normalize(x)
+        x = self.input_dropout(x)
+        
+        for i, layer in enumerate(self.encoder):
+            x = layer(x)
+            x = torch.tanh(x)
+        
+        for i, layer in enumerate(self.decoder):
+            x = layer(x)
+            if i != len(self.decoder)-1:
+                x = torch.tanh(x)
+
+        return x
+

models/vae.py

@@ -0,0 +1,69 @@
+from .base import BaseModel
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class VAEModel(BaseModel):
+    def __init__(self, args):
+        super().__init__(args)
+        self.latent_dim = args.vae_latent_dim
+
+        # Input dropout
+        self.input_dropout = nn.Dropout(p=args.vae_dropout)
+
+        # Construct a list of dimensions for the encoder and the decoder
+        dims = [args.vae_hidden_dim] * 2 * args.vae_num_hidden
+        dims = [args.num_items] + dims + [args.vae_latent_dim * 2]
+
+        # Stack encoders and decoders
+        encoder_modules, decoder_modules = [], []
+        for i in range(len(dims)//2):
+            encoder_modules.append(nn.Linear(dims[2*i], dims[2*i+1]))
+            if i == 0:
+                decoder_modules.append(nn.Linear(dims[-1]//2, dims[-2]))
+            else:
+                decoder_modules.append(nn.Linear(dims[-2*i-1], dims[-2*i-2]))
+        self.encoder = nn.ModuleList(encoder_modules)
+        self.decoder = nn.ModuleList(decoder_modules)
+
+        # Initialize weights
+        self.encoder.apply(self.weight_init)
+        self.decoder.apply(self.weight_init)
+
+    def weight_init(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.kaiming_normal_(m.weight)
+            m.bias.data.zero_()
+
+    @classmethod
+    def code(cls):
+        return 'vae'
+
+    def forward(self, x):
+        x = F.normalize(x)
+        x = self.input_dropout(x)
+        
+        for i, layer in enumerate(self.encoder):
+            x = layer(x)
+            if i != len(self.encoder) - 1:
+                x = torch.tanh(x)
+        
+        mu, logvar = x[:, :self.latent_dim], x[:, self.latent_dim:]
+
+        if self.training:
+            # since log(var) = log(sigma^2) = 2*log(sigma)
+            sigma = torch.exp(0.5 * logvar)
+            eps = torch.randn_like(sigma)
+            x = mu + eps * sigma
+        else:
+            x = mu
+
+        for i, layer in enumerate(self.decoder):
+            x = layer(x)
+            if i != len(self.decoder) - 1:
+                x = torch.tanh(x)
+                
+        return x, mu, logvar
+

options.py

@@ -0,0 +1,125 @@
+from template_args import set_template
+from models import MODELS
+import argparse
+
+
+parser = argparse.ArgumentParser(description='RecPlay')
+
+################
+# Top Level
+################
+parser.add_argument('--mode', type=str, default='train', choices=['train'])
+parser.add_argument('--template', type=str, default="train_bert")
+
+################
+# Test
+################
+parser.add_argument('--test_model_path', type=str, default=None)
+
+################
+# Dataset
+################
+
+parser.add_argument('--min_rating', type=int, default=4, help='Only keep ratings greater than equal to this value')
+parser.add_argument('--min_uc', type=int, default=5, help='Only keep users with more than min_uc ratings')
+parser.add_argument('--min_sc', type=int, default=0, help='Only keep items with more than min_sc ratings')
+parser.add_argument('--split', type=str, default='leave_one_out', help='How to split the datasets')
+parser.add_argument('--dataset_split_seed', type=int, default=98765)
+parser.add_argument('--eval_set_size', type=int, default=10000, 
+                    help='Size of val and test set. 500 for ML-1m and 10000 for ML-20m recommended')
+
+
+#inference
+parser.add_argument('--checkpoint', '-c', type=str,
+                        help='Path to the model checkpoint (.pth file)')
+parser.add_argument('--dataset', '-d', type=str,
+                        help='Path to the dataset pickle file (.pkl)')
+parser.add_argument('--animes', '-a', type=str,
+                        help='Path to the animes JSON file')
+parser.add_argument('--inference', '-i', type=str, default=False,
+                        help='Path to the animes JSON file')
+################
+# Dataloader
+################
+
+parser.add_argument('--dataloader_random_seed', type=float, default=0.0)
+parser.add_argument('--train_batch_size', type=int, default=64)
+parser.add_argument('--val_batch_size', type=int, default=64)
+parser.add_argument('--test_batch_size', type=int, default=64)
+
+################
+# NegativeSampler
+################
+parser.add_argument('--train_negative_sampler_code', type=str, default='random', choices=['popular', 'random'],
+                    help='Method to sample negative items for training. Not used in bert')
+parser.add_argument('--train_negative_sample_size', type=int, default=100)
+parser.add_argument('--train_negative_sampling_seed', type=int, default=None)
+parser.add_argument('--test_negative_sampler_code', type=str, default='random', choices=['popular', 'random'],
+                    help='Method to sample negative items for evaluation')
+parser.add_argument('--test_negative_sample_size', type=int, default=100)
+parser.add_argument('--test_negative_sampling_seed', type=int, default=None)
+
+################
+# Trainer
+################
+# device #
+parser.add_argument('--device', type=str, default='cpu', choices=['cpu', 'cuda'])
+parser.add_argument('--num_gpu', type=int, default=1)
+parser.add_argument('--device_idx', type=str, default='0')
+# optimizer #
+parser.add_argument('--optimizer', type=str, default='Adam', choices=['SGD', 'Adam'])
+parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
+parser.add_argument('--weight_decay', type=float, default=0, help='l2 regularization')
+parser.add_argument('--momentum', type=float, default=None, help='SGD momentum')
+# lr scheduler #
+parser.add_argument('--decay_step', type=int, default=15, help='Decay step for StepLR')
+parser.add_argument('--gamma', type=float, default=0.1, help='Gamma for StepLR')
+# epochs #
+parser.add_argument('--num_epochs', type=int, default=3, help='Number of epochs for training')
+# logger #
+parser.add_argument('--log_period_as_iter', type=int, default=12800)
+# evaluation #
+parser.add_argument('--metric_ks', nargs='+', type=int, default=[10, 20, 50], help='ks for Metric@k')
+parser.add_argument('--best_metric', type=str, default='NDCG@10', help='Metric for determining the best model')
+# Finding optimal beta for VAE #
+parser.add_argument('--find_best_beta', type=bool, default=False, 
+                    help='If set True, the trainer will anneal beta all the way up to 1.0 and find the best beta')
+parser.add_argument('--total_anneal_steps', type=int, default=2000, help='The step number when beta reaches 1.0')
+parser.add_argument('--anneal_cap', type=float, default=0.2, help='Upper limit of increasing beta. Set this as the best beta found')
+
+################
+# Model
+################
+parser.add_argument('--model_code', type=str, default='bert', choices=MODELS.keys())
+parser.add_argument('--model_init_seed', type=int, default=None)
+# BERT #
+parser.add_argument('--bert_max_len', type=int, default=None, help='Length of sequence for bert')
+parser.add_argument('--bert_num_items', type=int, default=None, help='Number of total items')
+parser.add_argument('--bert_hidden_units', type=int, default=None, help='Size of hidden vectors (d_model)')
+parser.add_argument('--bert_num_blocks', type=int, default=None, help='Number of transformer layers')
+parser.add_argument('--bert_num_heads', type=int, default=None, help='Number of heads for multi-attention')
+parser.add_argument('--bert_dropout', type=float, default=None, help='Dropout probability to use throughout the model')
+parser.add_argument('--bert_mask_prob', type=float, default=None, help='Probability for masking items in the training sequence')
+# DAE #
+parser.add_argument('--dae_num_items', type=int, default=None, help='Number of total items')
+parser.add_argument('--dae_num_hidden', type=int, default=0, help='Number of hidden layers in DAE')
+parser.add_argument('--dae_hidden_dim', type=int, default=600, help='Dimension of hidden layer in DAE')
+parser.add_argument('--dae_latent_dim', type=int, default=200, help="Dimension of latent vector in DAE")
+parser.add_argument('--dae_dropout', type=float, default=0.5, help='Probability of input dropout in DAE')
+# VAE #
+parser.add_argument('--vae_num_items', type=int, default=None, help='Number of total items')
+parser.add_argument('--vae_num_hidden', type=int, default=0, help='Number of hidden layers in VAE')
+parser.add_argument('--vae_hidden_dim', type=int, default=600, help='Dimension of hidden layer in VAE')
+parser.add_argument('--vae_latent_dim', type=int, default=200, help="Dimension of latent vector in VAE (K in paper)")
+parser.add_argument('--vae_dropout', type=float, default=0.5, help='Probability of input dropout in VAE')
+
+################
+# Experiment
+################
+parser.add_argument('--experiment_dir', type=str, default='experiments')
+parser.add_argument('--experiment_description', type=str, default='test')
+
+
+################
+args, unknown = parser.parse_known_args()
+set_template(args)

render.yaml

@@ -0,0 +1,8 @@
+# render.yaml dosyası ekleyin
+services:
+  - type: web
+    name: anime-recommendation
+    env: python
+    buildCommand: "pip install -r requirements.txt"
+    startCommand: "gunicorn -w 1 --bind 0.0.0.0:$PORT app:app"
+    plan: free