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Codenames AI Assistant .ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9b7d6163-bb7b-44f9-8ca2-fb20e588efac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gensim\n",
+      "  Using cached gensim-4.3.3-cp312-cp312-win_amd64.whl.metadata (8.2 kB)\n",
+      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (2.1.3)\n",
+      "Collecting numpy\n",
+      "  Using cached numpy-1.26.4-cp312-cp312-win_amd64.whl.metadata (61 kB)\n",
+      "Collecting scipy<1.14.0,>=1.7.0 (from gensim)\n",
+      "  Using cached scipy-1.13.1-cp312-cp312-win_amd64.whl.metadata (60 kB)\n",
+      "Collecting smart-open>=1.8.1 (from gensim)\n",
+      "  Using cached smart_open-7.1.0-py3-none-any.whl.metadata (24 kB)\n",
+      "Requirement already satisfied: wrapt in c:\\programdata\\anaconda3\\lib\\site-packages (from smart-open>=1.8.1->gensim) (1.17.0)\n",
+      "Using cached gensim-4.3.3-cp312-cp312-win_amd64.whl (24.0 MB)\n",
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+      "Downloading smart_open-7.1.0-py3-none-any.whl (61 kB)\n",
+      "Installing collected packages: smart-open, numpy, scipy, gensim\n",
+      "  Attempting uninstall: numpy\n",
+      "    Found existing installation: numpy 2.1.3\n",
+      "    Uninstalling numpy-2.1.3:\n",
+      "      Successfully uninstalled numpy-2.1.3\n",
+      "  Attempting uninstall: scipy\n",
+      "    Found existing installation: scipy 1.15.1\n",
+      "    Uninstalling scipy-1.15.1:\n",
+      "      Successfully uninstalled scipy-1.15.1\n",
+      "Successfully installed gensim-4.3.3 numpy-1.26.4 scipy-1.13.1 smart-open-7.1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\LGR\\AppData\\Local\\Temp\\pip-uninstall-bzrgm1pe'.\n",
+      "  You can safely remove it manually.\n",
+      "  WARNING: Failed to remove contents in a temporary directory 'C:\\ProgramData\\anaconda3\\Lib\\site-packages\\~~mpy.libs'.\n",
+      "  You can safely remove it manually.\n",
+      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "tensorflow-intel 2.18.0 requires ml-dtypes<0.5.0,>=0.4.0, but you have ml-dtypes 0.5.1 which is incompatible.\n",
+      "tensorflow-intel 2.18.0 requires tensorboard<2.19,>=2.18, but you have tensorboard 2.19.0 which is incompatible.\n",
+      "bokeh 3.6.2 requires tornado>=6.2, but you have tornado 6.1 which is incompatible.\n",
+      "scikit-image 0.25.0 requires pillow>=10.1, but you have pillow 9.5.0 which is incompatible.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gensim numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a6600c17-fb8e-4e8c-a6a1-d25084447144",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gensim.downloader as api\n",
+    "\n",
+    "# Bu satır modeli ilk kez indirir ve önbelleğe alır\n",
+    "model = api.load(\"word2vec-google-news-300\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6d55403c-62fc-4d07-9306-ca11593bcaa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05226295"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# En benzer kelimeleri getir\n",
+    "model.most_similar(\"spy\")\n",
+    "\n",
+    "# İki kelime arasındaki benzerlik\n",
+    "model.similarity(\"dog\", \"cat\")\n",
+    "model.similarity(\"dog\", \"explosion\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e56a26c9-a705-4e54-83e2-331e03586f36",
+   "metadata": {},
+   "source": [
+    "AI Strateji Fonksiyonu: oner_ipucu()\n",
+    "Bu fonksiyon:\n",
+    "\n",
+    "Hedef kelimelere yakın\n",
+    "\n",
+    "Yasaklı kelimelere uzak\n",
+    "olan en iyi ipucunu seçecek."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8d01664b-2ca0-407c-a51c-55f19ec5615f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def oner_ipucu(hedefler, yasaklar, model, aday_kelimeler=None, top_n=1):\n",
+    "    from numpy import mean\n",
+    "\n",
+    "    # Hedef ve yasaklı kelimeleri dışlayacağımız bir set\n",
+    "    filtre = set(hedefler + yasaklar)\n",
+    "\n",
+    "    if aday_kelimeler is None:\n",
+    "        aday_kelimeler = list(model.key_to_index.keys())\n",
+    "\n",
+    "    en_iyi_ipucu = None\n",
+    "    en_iyi_skor = -float(\"inf\")\n",
+    "\n",
+    "    for kelime in aday_kelimeler:\n",
+    "        if kelime in filtre:\n",
+    "            continue  # hedef ya da yasaklılardan biri zaten, geç\n",
+    "\n",
+    "        try:\n",
+    "            hedef_skor = mean([model.similarity(kelime, h) for h in hedefler if h in model])\n",
+    "            yasak_skor = mean([model.similarity(kelime, y) for y in yasaklar if y in model])\n",
+    "            toplam_skor = hedef_skor - yasak_skor\n",
+    "\n",
+    "            if toplam_skor > en_iyi_skor:\n",
+    "                en_iyi_skor = toplam_skor\n",
+    "                en_iyi_ipucu = kelime\n",
+    "\n",
+    "        except KeyError:\n",
+    "            continue\n",
+    "\n",
+    "    return en_iyi_ipucu\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a24840d8-ecfe-4119-a25a-514e9ec42a55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 Geliştirilmiş Önerilen ipucu: cats\n"
+     ]
+    }
+   ],
+   "source": [
+    "hedefler = [\"dog\", \"cat\", \"fish\"]\n",
+    "yasaklar = [\"bomb\", \"knife\", \"gun\"]\n",
+    "\n",
+    "ipucu = oner_ipucu(hedefler, yasaklar, model)\n",
+    "print(\"🔍 Geliştirilmiş Önerilen ipucu:\", ipucu)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b56df5e1-eb56-4a4b-83a4-172476551353",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

NOte.txt

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+ Proje Özeti: Codenames AI Assistant
+Oyunda bir “spymaster” olarak çalışan yapay zeka modeli, elindeki kelimelere bakarak takımına ipuçları (tek bir kelime) vermeye çalışır. Amaç:
+🔹 Hedef kelimelerle ilişkili bir ipucu bulmak
+🔹 Rakip veya yasak kelimelere yaklaşmamak
+
+
+
+
+💡 Teknik Bileşenler:
+Bileşen	Açıklama
+Embedding	Word2Vec / FastText / GloVe
+Benzerlik Ölçümü	Cosine Similarity
+Strateji	Maksimum hedef benzerliği + minimum rakip/assassin uzaklığı
+Arayüz	Streamlit (görsel oyun tahtası + AI önerisi)
+
+
+
+🧠 Yapay Zeka Ne Yapacak?
+Hedef kelimeler listesi verilecek
+
+Yasaklı kelimeler (rakip + assassin) de belirtilecek
+
+Model, embedding'ler üzerinden tüm kelimeleri tarayacak
+
+Hem hedeflere en yakın, hem de yasaklılara en uzak olan en iyi ipucuyu önerecek
+
+📂 Başlangıç Planı:
+ ✅ Proje dosyası oluştur (codenames_ai)
+
+ Word2Vec vektörleri indir (hazır model)
+
+ Basit örnekle strateji algoritması geliştir
+
+ Streamlit arayüzü (girdi: hedef + yasaklı kelimeler → çıktı: ipucu)
+

README.md

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----
-title: Codenames AI Assistant
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
-license: mit
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+title: "Codenames AI Assistant"
+emoji: 🧠
+colorFrom: blue
+colorTo: red
+sdk: streamlit
+app_file: app.py
+pinned: true
+tags:
+  - nlp
+  - word2vec
+  - strategy
+  - ai
+  - streamlit
+  - game
+license: mit
+---
+
+# 🧠 Codenames AI Assistant
+
+Bu proje, **Codenames** oyununda hedef kelimelere en uygun **tek kelimelik ipucu**yu bulmaya çalışan bir yapay zeka strateji aracıdır.  
+Word2Vec modeli ile anlamsal benzerlik hesaplanır, hedeflere yakın, yasaklara uzak en iyi kelime önerilir.
+
+## 🔍 Kullanılan Teknikler
+
+- Gensim ile önceden eğitilmiş `word2vec-google-news-300`
+- Cosine benzerliği
+- Stratejik kelime seçimi
+
+## 🧩 Nasıl Çalışır?
+
+- Hedef ve yasaklı kelimeleri gir
+- AI, en alakalı ve güvenli kelimeyi önerir
+- Model eğitimi yoktur (hazır embedding kullanılır)
+
+## 🧠 Örnek
+
+```python
+hedefler = ["dog", "cat", "fish"]
+yasaklar = ["bomb", "knife", "gun"]
+ipucu = oner_ipucu(hedefler, yasaklar, model)
+print(ipucu)  # animal gibi bir sonuç dönebilir

app.py

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+import streamlit as st
+import gensim.downloader as api
+import numpy as np
+import string
+import re
+
+# 🔹 AI ipucu önerici
+def oner_ipucu(hedefler, yasaklar, model, aday_kelimeler=None):
+    filtre = set(hedefler + yasaklar)
+    if aday_kelimeler is None:
+        aday_kelimeler = list(model.key_to_index.keys())
+
+    en_iyi_ipucu = None
+    en_iyi_skor = -float("inf")
+
+    for kelime in aday_kelimeler:
+        if kelime in filtre:
+            continue
+        try:
+            hedef_skor = np.mean([model.similarity(kelime, h) for h in hedefler if h in model])
+            yasak_skor = np.mean([model.similarity(kelime, y) for y in yasaklar if y in model])
+            toplam_skor = hedef_skor - yasak_skor
+            if toplam_skor > en_iyi_skor:
+                en_iyi_skor = toplam_skor
+                en_iyi_ipucu = kelime
+        except KeyError:
+            continue
+
+    return en_iyi_ipucu
+
+# 🧠 Word2Vec modeli yükle (ilk seferde indirir)
+@st.cache_resource
+def load_model():
+    return api.load("word2vec-google-news-300")
+
+model = load_model()
+
+# 🎯 Uygulama Başlığı
+st.title("🧠 Codenames AI Assistant")
+st.subheader("💡 Yapay Zeka ile Stratejik İpucu Önerici")
+
+# 🎯 Girdiler
+hedef_input = st.text_input("🎯 Hedef kelimeler (virgülle ayırın)", "dog, cat, fish")
+yasak_input = st.text_input("⛔ Yasaklı kelimeler (virgülle ayırın)", "bomb, knife, gun")
+
+# Buton
+if st.button("🔍 En iyi ipucuyu öner"):
+    hedefler = [w.strip().lower() for w in hedef_input.split(",")]
+    yasaklar = [w.strip().lower() for w in yasak_input.split(",")]
+
+    ipucu = oner_ipucu(hedefler, yasaklar, model)
+    if ipucu:
+        st.success(f"🎯 Önerilen İpucu: **{ipucu}**")
+    else:
+        st.error("Uygun ipucu bulunamadı. Kelimeleri kontrol edin.")

config.json

@@ -0,0 +1,14 @@
+```json
+{
+  "model_type": "embedding",
+  "library_name": "gensim",
+  "pipeline_tag": "text-classification",
+  "tags": [
+    "nlp",
+    "word2vec",
+    "streamlit",
+    "codenames",
+    "game",
+    "strategy"
+  ]
+}

model.txt

@@ -0,0 +1,3 @@
+model internetten yüklenir.
+import gensim.downloader as api
+model = api.load("word2vec-google-news-300")