data_preparation_semantic_transverse_interrogator_final.ipynb

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      "\n",
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      "\n",
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      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "To change this limit, set the config variable\n",
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      "\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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      "To change this limit, set the config variable\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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      "To change this limit, set the config variable\n",
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      "\n",
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      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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   "source": [
    "import gensim.downloader as api\n",
    "import spacy\n",
    "\n",
    "# Load pre-trained Word2Vec model\n",
    "w2v_model = api.load(\"word2vec-google-news-300\")\n",
    "\n",
    "# Load pre-trained GloVe model with spaCy\n",
    "nlp = spacy.load(\"en_core_web_lg\")\n",
    "\n",
    "def get_word2vec_embeddings(sentence):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using Word2Vec model.\n",
    "    \"\"\"\n",
    "    # Tokenize sentence and get word embeddings\n",
    "    word_embeddings = [w2v_model[word] for word in sentence.split() if word in w2v_model]\n",
    "\n",
    "    # Average the word embeddings to get sentence embedding\n",
    "    if word_embeddings:\n",
    "        sentence_embedding = sum(word_embeddings) / len(word_embeddings)\n",
    "        return sentence_embedding\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def get_glove_embeddings(sentence):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using GloVe model.\n",
    "    \"\"\"\n",
    "    # Tokenize sentence and get word embeddings\n",
    "    doc = nlp(sentence)\n",
    "    word_embeddings = [token.vector for token in doc]\n",
    "\n",
    "    # Average the word embeddings to get sentence embedding\n",
    "    if word_embeddings:\n",
    "        sentence_embedding = sum(word_embeddings) / len(word_embeddings)\n",
    "        return sentence_embedding\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Example sentences\n",
    "sentence1 = \"The quick brown fox jumps over the lazy dog.\"\n",
    "sentence2 = \"An apple a day keeps the doctor away.\"\n",
    "\n",
    "# Get embeddings\n",
    "w2v_embedding1 = get_word2vec_embeddings(sentence1)\n",
    "glove_embedding1 = get_glove_embeddings(sentence1)\n",
    "\n",
    "w2v_embedding2 = get_word2vec_embeddings(sentence2)\n",
    "glove_embedding2 = get_glove_embeddings(sentence2)\n",
    "\n",
    "print(\"Word2Vec embedding for sentence 1:\", w2v_embedding1)\n",
    "print(\"GloVe embedding for sentence 1:\", glove_embedding1)\n",
    "print(\"Word2Vec embedding for sentence 2:\", w2v_embedding2)\n",
    "print(\"GloVe embedding for sentence 2:\", glove_embedding2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22923946-2b0c-472a-b5ba-e5bd2529ec77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         image                                             prompt  \\\n",
      "0     0062.png  there is a dog that is jumping over a person o...   \n",
      "1     0019.png  there is a man that is standing in the water w...   \n",
      "2     0065.png  there is a woman that is doing a trick on a sk...   \n",
      "3     0017.png  there are two men playing frisbee on the beach...   \n",
      "4     0133.png  there is a man riding a bike on a cobblestone ...   \n",
      "...        ...                                                ...   \n",
      "2106  0098.png  there is a man that is sitting on a bed with a...   \n",
      "2107  0035.png  there is a black and white photo of a person j...   \n",
      "2108  0032.png  there is a man standing on a bed with a remote...   \n",
      "2109  0201.png  there is a man that is sitting on a bed with a...   \n",
      "2110  0116.png  several people are walking around a pile of sh...   \n",
      "\n",
      "                                                   path  \\\n",
      "0                             me trying to wake up2.csv   \n",
      "1                             me trying to wake up2.csv   \n",
      "2                             me trying to wake up2.csv   \n",
      "3                             me trying to wake up2.csv   \n",
      "4                             me trying to wake up2.csv   \n",
      "...                                                 ...   \n",
      "2106  me trying to wake up at night in my dark room.csv   \n",
      "2107  me trying to wake up at night in my dark room.csv   \n",
      "2108  me trying to wake up at night in my dark room.csv   \n",
      "2109  me trying to wake up at night in my dark room.csv   \n",
      "2110  me trying to wake up at night in my dark room.csv   \n",
      "\n",
      "                                     word2vec_embedding  \\\n",
      "0     [0.056626093, 0.027533803, 0.031668525, 0.0752...   \n",
      "1     [0.0060457503, 0.040771484, 0.050604682, 0.017...   \n",
      "2     [0.061437987, 0.07248999, 0.0069295247, 0.0486...   \n",
      "3     [0.0406431, 0.058693722, 0.0059340904, 0.10164...   \n",
      "4     [0.08545274, 0.011642346, 0.026893029, 0.05407...   \n",
      "...                                                 ...   \n",
      "2106  [0.06991608, 0.044709474, 0.050063476, 0.05080...   \n",
      "2107  [0.03191312, 0.047365315, 0.029978434, 0.04258...   \n",
      "2108  [0.06510456, 0.044217624, -0.011564989, 0.0939...   \n",
      "2109  [0.086204804, 0.10034398, 0.0185912, 0.0620138...   \n",
      "2110  [0.09589918, 0.029363459, 0.021817295, 0.04226...   \n",
      "\n",
      "                                        glove_embedding  \n",
      "0     [-2.3002532, -0.37098542, -2.00778, 0.45180738...  \n",
      "1     [-2.5310247, -0.026250007, -1.8209349, 1.05786...  \n",
      "2     [-1.8465071, -0.16474748, -1.9208614, 0.220451...  \n",
      "3     [-1.9998579, -2.232841, -1.7769092, 1.142132, ...  \n",
      "4     [-3.1758435, -0.42816302, -2.2382548, 0.352997...  \n",
      "...                                                 ...  \n",
      "2106  [-2.8003013, 0.09154665, -2.934364, 1.4075446,...  \n",
      "2107  [-2.528043, -0.25532493, -2.406719, 0.6398633,...  \n",
      "2108  [-2.0295284, -0.71562105, -1.369572, 1.0037926...  \n",
      "2109  [-2.2302415, 0.6389956, -2.7548122, 0.8089043,...  \n",
      "2110  [-2.6032977, -1.4429986, -1.8419123, 1.4929312...  \n",
      "\n",
      "[2111 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import gensim.downloader as api\n",
    "import spacy\n",
    "\n",
    "# Load pre-trained Word2Vec model\n",
    "w2v_model = api.load(\"word2vec-google-news-300\")\n",
    "\n",
    "# Load pre-trained GloVe model with spaCy\n",
    "nlp = spacy.load(\"en_core_web_lg\")\n",
    "\n",
    "def get_word2vec_embeddings(sentence):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using Word2Vec model.\n",
    "    \"\"\"\n",
    "    # Tokenize sentence and get word embeddings\n",
    "    word_embeddings = [w2v_model[word] for word in sentence.split() if word in w2v_model]\n",
    "\n",
    "    # Average the word embeddings to get sentence embedding\n",
    "    if word_embeddings:\n",
    "        sentence_embedding = sum(word_embeddings) / len(word_embeddings)\n",
    "        return sentence_embedding\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def get_glove_embeddings(sentence):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using GloVe model.\n",
    "    \"\"\"\n",
    "    # Tokenize sentence and get word embeddings\n",
    "    doc = nlp(sentence)\n",
    "    word_embeddings = [token.vector for token in doc]\n",
    "\n",
    "    # Average the word embeddings to get sentence embedding\n",
    "    if word_embeddings:\n",
    "        sentence_embedding = sum(word_embeddings) / len(word_embeddings)\n",
    "        return sentence_embedding\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Read DataFrame from CSV\n",
    "df = pd.read_csv(\"full_dataset.csv\")\n",
    "\n",
    "# Apply embeddings function to each prompt\n",
    "df['word2vec_embedding'] = df['prompt'].apply(get_word2vec_embeddings)\n",
    "df['glove_embedding'] = df['prompt'].apply(get_glove_embeddings)\n",
    "\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93f7d4eb-2065-4bad-8e0f-4e009e1f5a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image                                                          0062.png\n",
       "prompt                there is a dog that is jumping over a person o...\n",
       "path                                          me trying to wake up2.csv\n",
       "word2vec_embedding    [0.056626093, 0.027533803, 0.031668525, 0.0752...\n",
       "glove_embedding       [-2.3002532, -0.37098542, -2.00778, 0.45180738...\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0547dd3-2b7a-471f-8c46-c3b037c99ad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  pid, fd = os.forkpty()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in ./micromamba/envs/311/lib/python3.11/site-packages (2.16.1)\n",
      "Collecting tensorflow_hub\n",
      "  Downloading tensorflow_hub-0.16.1-py2.py3-none-any.whl.metadata (1.3 kB)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (2.1.0)\n",
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      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.5.4)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.2.0)\n",
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      "Requirement already satisfied: ml-dtypes~=0.3.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.3.2)\n",
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      "Requirement already satisfied: packaging in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (23.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (3.20.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (2.31.0)\n",
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      "Requirement already satisfied: wrapt>=1.11.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.63.0)\n",
      "Requirement already satisfied: tensorboard<2.17,>=2.16 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (2.16.2)\n",
      "Requirement already satisfied: keras>=3.0.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (3.3.3)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.37.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.24.4)\n",
      "Collecting tf-keras>=2.14.1 (from tensorflow_hub)\n",
      "  Downloading tf_keras-2.16.0-py3-none-any.whl.metadata (1.6 kB)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from astunparse>=1.6.0->tensorflow) (0.42.0)\n",
      "Requirement already satisfied: rich in ./micromamba/envs/311/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow) (13.7.0)\n",
      "Requirement already satisfied: namex in ./micromamba/envs/311/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in ./micromamba/envs/311/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow) (0.11.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in ./micromamba/envs/311/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in ./micromamba/envs/311/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./micromamba/envs/311/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (2023.11.17)\n",
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      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.3)\n",
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      "Requirement already satisfied: markdown-it-py>=2.2.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow) (2.17.2)\n",
      "Requirement already satisfied: mdurl~=0.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.2)\n",
      "Downloading tensorflow_hub-0.16.1-py2.py3-none-any.whl (30 kB)\n",
      "Downloading tf_keras-2.16.0-py3-none-any.whl (1.7 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: tf-keras, tensorflow_hub\n",
      "Successfully installed tensorflow_hub-0.16.1 tf-keras-2.16.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow tensorflow_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6bc835ad-1e2f-4587-9a1f-74373b57a61a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  pid, fd = os.forkpty()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in ./micromamba/envs/311/lib/python3.11/site-packages (2.16.1)\n",
      "Requirement already satisfied: tensorflow-hub in ./micromamba/envs/311/lib/python3.11/site-packages (0.16.1)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (2.1.0)\n",
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      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.5.4)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.2.0)\n",
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      "Requirement already satisfied: ml-dtypes~=0.3.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.3.2)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: packaging in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (23.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (3.20.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (2.31.0)\n",
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      "Requirement already satisfied: six>=1.12.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.16.0)\n",
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      "Requirement already satisfied: typing-extensions>=3.6.6 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (4.11.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.63.0)\n",
      "Requirement already satisfied: tensorboard<2.17,>=2.16 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (2.16.2)\n",
      "Requirement already satisfied: keras>=3.0.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (3.3.3)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (0.37.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow) (1.24.4)\n",
      "Requirement already satisfied: tf-keras>=2.14.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorflow-hub) (2.16.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from astunparse>=1.6.0->tensorflow) (0.42.0)\n",
      "Requirement already satisfied: rich in ./micromamba/envs/311/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow) (13.7.0)\n",
      "Requirement already satisfied: namex in ./micromamba/envs/311/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in ./micromamba/envs/311/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow) (0.11.0)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./micromamba/envs/311/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (2023.11.17)\n",
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      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.3)\n",
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      "Requirement already satisfied: markdown-it-py>=2.2.0 in ./micromamba/envs/311/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow) (3.0.0)\n",
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      "Requirement already satisfied: mdurl~=0.1 in ./micromamba/envs/311/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade tensorflow tensorflow-hub\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39618419-f73b-4f10-baef-b41da3bd29ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-12 12:04:17.215575: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-05-12 12:04:18.001091: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "2024-05-12 12:04:19.833087: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         image                                             prompt  \\\n",
      "0     0062.png  there is a dog that is jumping over a person o...   \n",
      "1     0019.png  there is a man that is standing in the water w...   \n",
      "2     0065.png  there is a woman that is doing a trick on a sk...   \n",
      "3     0017.png  there are two men playing frisbee on the beach...   \n",
      "4     0133.png  there is a man riding a bike on a cobblestone ...   \n",
      "...        ...                                                ...   \n",
      "2106  0098.png  there is a man that is sitting on a bed with a...   \n",
      "2107  0035.png  there is a black and white photo of a person j...   \n",
      "2108  0032.png  there is a man standing on a bed with a remote...   \n",
      "2109  0201.png  there is a man that is sitting on a bed with a...   \n",
      "2110  0116.png  several people are walking around a pile of sh...   \n",
      "\n",
      "                                                   path  use_embedding_0  \\\n",
      "0                             me trying to wake up2.csv        -0.025636   \n",
      "1                             me trying to wake up2.csv        -0.064627   \n",
      "2                             me trying to wake up2.csv        -0.039017   \n",
      "3                             me trying to wake up2.csv        -0.060811   \n",
      "4                             me trying to wake up2.csv        -0.058844   \n",
      "...                                                 ...              ...   \n",
      "2106  me trying to wake up at night in my dark room.csv        -0.056449   \n",
      "2107  me trying to wake up at night in my dark room.csv        -0.035052   \n",
      "2108  me trying to wake up at night in my dark room.csv        -0.065936   \n",
      "2109  me trying to wake up at night in my dark room.csv        -0.059025   \n",
      "2110  me trying to wake up at night in my dark room.csv        -0.054683   \n",
      "\n",
      "      use_embedding_1  use_embedding_2  use_embedding_3  use_embedding_4  \\\n",
      "0            0.069082         0.001414        -0.054650        -0.011173   \n",
      "1            0.073101         0.035474        -0.059073         0.040659   \n",
      "2            0.068517        -0.003355        -0.010348        -0.007809   \n",
      "3            0.055488         0.008168        -0.057401        -0.030705   \n",
      "4            0.060008         0.021663        -0.029490         0.048934   \n",
      "...               ...              ...              ...              ...   \n",
      "2106         0.073795         0.032026        -0.028803         0.007344   \n",
      "2107         0.061207         0.072243        -0.006038        -0.073391   \n",
      "2108         0.020840         0.026457        -0.022540         0.008665   \n",
      "2109         0.049533         0.030853        -0.031009        -0.062995   \n",
      "2110         0.067919        -0.012854        -0.035902         0.022258   \n",
      "\n",
      "      use_embedding_5  use_embedding_6  ...  use_embedding_502  \\\n",
      "0            0.008651         0.023082  ...           0.041382   \n",
      "1            0.022868         0.067509  ...           0.031602   \n",
      "2            0.027157         0.031086  ...          -0.029866   \n",
      "3            0.067951         0.021018  ...          -0.029663   \n",
      "4            0.063539         0.059925  ...           0.023807   \n",
      "...               ...              ...  ...                ...   \n",
      "2106         0.032009         0.055132  ...           0.062542   \n",
      "2107         0.053893         0.025535  ...           0.071369   \n",
      "2108         0.075155         0.026502  ...           0.037561   \n",
      "2109        -0.015224         0.066193  ...           0.052544   \n",
      "2110        -0.003883         0.048221  ...           0.049236   \n",
      "\n",
      "      use_embedding_503  use_embedding_504  use_embedding_505  \\\n",
      "0              0.027591           0.005016           0.030988   \n",
      "1             -0.022714          -0.060691           0.035295   \n",
      "2              0.054687           0.069059           0.057675   \n",
      "3              0.028707           0.030386          -0.053406   \n",
      "4             -0.015697          -0.010025           0.016845   \n",
      "...                 ...                ...                ...   \n",
      "2106           0.046681           0.033724          -0.056115   \n",
      "2107           0.009124           0.016337           0.048606   \n",
      "2108           0.004319           0.048307           0.000824   \n",
      "2109           0.044864           0.029327          -0.022848   \n",
      "2110          -0.045767          -0.019664           0.017033   \n",
      "\n",
      "      use_embedding_506  use_embedding_507  use_embedding_508  \\\n",
      "0              0.036799           0.036155           0.022378   \n",
      "1             -0.021317           0.010578          -0.046996   \n",
      "2             -0.045326          -0.026075          -0.043329   \n",
      "3             -0.032898           0.023992          -0.021602   \n",
      "4              0.010481          -0.034774           0.029875   \n",
      "...                 ...                ...                ...   \n",
      "2106          -0.019872          -0.063280          -0.048132   \n",
      "2107           0.048030          -0.024918           0.018809   \n",
      "2108           0.054323           0.039376           0.027202   \n",
      "2109          -0.052507          -0.025941           0.016169   \n",
      "2110          -0.040968           0.038628          -0.007082   \n",
      "\n",
      "      use_embedding_509  use_embedding_510  use_embedding_511  \n",
      "0              0.041126          -0.054038          -0.052767  \n",
      "1              0.030665          -0.037947          -0.059806  \n",
      "2              0.041585          -0.013280          -0.060468  \n",
      "3              0.022464           0.069697          -0.070428  \n",
      "4             -0.011562           0.015294          -0.071717  \n",
      "...                 ...                ...                ...  \n",
      "2106           0.009144           0.013917          -0.009724  \n",
      "2107          -0.020874          -0.011685          -0.039206  \n",
      "2108           0.011916          -0.040231          -0.020743  \n",
      "2109          -0.008079          -0.004928           0.030251  \n",
      "2110           0.030196          -0.026791           0.028675  \n",
      "\n",
      "[2111 rows x 515 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "\n",
    "# Load Universal Sentence Encoder model\n",
    "use_model = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder/4\")\n",
    "\n",
    "# Function to get USE embeddings\n",
    "def get_use_embeddings(sentences):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using Universal Sentence Encoder.\n",
    "    \"\"\"\n",
    "    # Convert sentences to tensors\n",
    "    sentence_tensors = tf.constant(sentences.tolist())\n",
    "\n",
    "    # Get sentence embeddings\n",
    "    sentence_embeddings = use_model(sentence_tensors).numpy()\n",
    "    \n",
    "    return sentence_embeddings\n",
    "\n",
    "# Read DataFrame from CSV\n",
    "df = pd.read_csv(\"full_dataset.csv\")\n",
    "\n",
    "# Apply embeddings function to all sentences\n",
    "embeddings = get_use_embeddings(df['prompt'])\n",
    "\n",
    "# Create a new DataFrame with embeddings\n",
    "df_embeddings = pd.DataFrame(embeddings, columns=[f'use_embedding_{i}' for i in range(embeddings.shape[1])])\n",
    "\n",
    "# Concatenate the original DataFrame with the DataFrame containing embeddings\n",
    "df_concatenated = pd.concat([df, df_embeddings], axis=1)\n",
    "\n",
    "print(df_concatenated)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae3240f1-af30-4b64-99c9-e201cabf2cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image                                                         0062.png\n",
       "prompt               there is a dog that is jumping over a person o...\n",
       "path                                         me trying to wake up2.csv\n",
       "use_embedding_0                                              -0.025636\n",
       "use_embedding_1                                               0.069082\n",
       "                                           ...                        \n",
       "use_embedding_507                                             0.036155\n",
       "use_embedding_508                                             0.022378\n",
       "use_embedding_509                                             0.041126\n",
       "use_embedding_510                                            -0.054038\n",
       "use_embedding_511                                            -0.052767\n",
       "Name: 0, Length: 515, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concatenated.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03700966-118d-4e25-8976-ccfe6a8860f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_concatenated.to_csv(\"full_dataset_USE_embeddings.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0501e2b-e766-47fe-98fc-e2179877b99b",
   "metadata": {},
   "source": [
    "# Let's visualize the embeddings to see if USE is of use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8a63629-283c-4aa8-be0e-d4ae2e59c98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128293 (\\N{FIRE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128377 (\\N{JOYSTICK}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128064 (\\N{EYES}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20013 (\\N{CJK UNIFIED IDEOGRAPH-4E2D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22269 (\\N{CJK UNIFIED IDEOGRAPH-56FD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39740 (\\N{CJK UNIFIED IDEOGRAPH-9B3C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 33410 (\\N{CJK UNIFIED IDEOGRAPH-8282}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65292 (\\N{FULLWIDTH COMMA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128127 (\\N{IMP}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128128 (\\N{SKULL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 129302 (\\N{ROBOT FACE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128640 (\\N{ROCKET}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# Load Universal Sentence Encoder model\n",
    "use_model = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder/4\")\n",
    "\n",
    "# Function to get USE embeddings\n",
    "def get_use_embeddings(sentences):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using Universal Sentence Encoder.\n",
    "    \"\"\"\n",
    "    # Convert sentences to tensors\n",
    "    sentence_tensors = tf.constant(sentences.tolist())\n",
    "\n",
    "    # Get sentence embeddings\n",
    "    sentence_embeddings = use_model(sentence_tensors).numpy()\n",
    "    \n",
    "    return sentence_embeddings\n",
    "\n",
    "# Read DataFrame from CSV\n",
    "df = pd.read_csv(\"full_dataset.csv\")\n",
    "\n",
    "# Apply embeddings function to all sentences\n",
    "embeddings = get_use_embeddings(df['prompt'])\n",
    "\n",
    "# Apply t-SNE to reduce dimensionality to 3 dimensions\n",
    "tsne = TSNE(n_components=3, random_state=42)\n",
    "embeddings_3d = tsne.fit_transform(embeddings)\n",
    "\n",
    "# Plot 3D scatter plot\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Scatter plot points\n",
    "ax.scatter(embeddings_3d[:, 0], embeddings_3d[:, 1], embeddings_3d[:, 2], c='b', marker='o')\n",
    "\n",
    "# Annotate each point with its corresponding sentence\n",
    "for i, txt in enumerate(df['prompt']):\n",
    "    ax.text(embeddings_3d[i, 0], embeddings_3d[i, 1], embeddings_3d[i, 2], txt, color='black')\n",
    "\n",
    "# Set labels\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2803b0a6-8844-4c71-b723-040d9f8e1c69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kpalacio/micromamba/envs/311/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  pid, fd = os.forkpty()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting plotly\n",
      "  Downloading plotly-5.22.0-py3-none-any.whl.metadata (7.1 kB)\n",
      "Collecting tenacity>=6.2.0 (from plotly)\n",
      "  Downloading tenacity-8.3.0-py3-none-any.whl.metadata (1.2 kB)\n",
      "Requirement already satisfied: packaging in ./micromamba/envs/311/lib/python3.11/site-packages (from plotly) (23.2)\n",
      "Downloading plotly-5.22.0-py3-none-any.whl (16.4 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.4/16.4 MB\u001b[0m \u001b[31m73.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading tenacity-8.3.0-py3-none-any.whl (25 kB)\n",
      "Installing collected packages: tenacity, plotly\n",
      "Successfully installed plotly-5.22.0 tenacity-8.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc7c7aae-5c68-425e-ba82-4a936b326e7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: namex, libclang, flatbuffers, wrapt, werkzeug, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, tenacity, protobuf, optree, opt-einsum, ml-dtypes, mdurl, markdown, h5py, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, plotly, markdown-it-py, rich, keras, tensorflow, tf-keras, tensorflow_hub\n",
      "Successfully installed absl-py-2.1.0 astunparse-1.6.3 flatbuffers-24.3.25 gast-0.5.4 google-pasta-0.2.0 grpcio-1.63.0 h5py-3.11.0 keras-3.3.3 libclang-18.1.1 markdown-3.6 markdown-it-py-3.0.0 mdurl-0.1.2 ml-dtypes-0.3.2 namex-0.0.8 opt-einsum-3.3.0 optree-0.11.0 plotly-5.22.0 protobuf-4.25.3 rich-13.7.1 tenacity-8.3.0 tensorboard-2.16.2 tensorboard-data-server-0.7.2 tensorflow-2.16.1 tensorflow-io-gcs-filesystem-0.37.0 tensorflow_hub-0.16.1 termcolor-2.4.0 tf-keras-2.16.0 werkzeug-3.0.3 wrapt-1.16.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow tensorflow_hub plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b73f0c86-07cc-434a-b18e-d65a8ee8e9a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow_hub\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mhub\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmanifold\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TSNE\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "from sklearn.manifold import TSNE\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Load Universal Sentence Encoder model\n",
    "use_model = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder/4\")\n",
    "\n",
    "# Function to get USE embeddings\n",
    "def get_use_embeddings(sentences):\n",
    "    \"\"\"\n",
    "    Get semantic embeddings using Universal Sentence Encoder.\n",
    "    \"\"\"\n",
    "    # Convert sentences to tensors\n",
    "    sentence_tensors = tf.constant(sentences.tolist())\n",
    "\n",
    "    # Get sentence embeddings\n",
    "    sentence_embeddings = use_model(sentence_tensors).numpy()\n",
    "    \n",
    "    return sentence_embeddings\n",
    "\n",
    "# Read DataFrame from CSV\n",
    "df = pd.read_csv(\"full_dataset.csv\")\n",
    "\n",
    "# Apply embeddings function to all sentences\n",
    "embeddings = get_use_embeddings(df['prompt'])\n",
    "\n",
    "# Apply t-SNE to reduce dimensionality to 3 dimensions\n",
    "tsne = TSNE(n_components=3, random_state=42)\n",
    "embeddings_3d = tsne.fit_transform(embeddings)\n",
    "\n",
    "# Create trace for scatter plot\n",
    "scatter = go.Scatter3d(\n",
    "    x=embeddings_3d[:, 0],\n",
    "    y=embeddings_3d[:, 1],\n",
    "    z=embeddings_3d[:, 2],\n",
    "    text=df['prompt'],\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size=5,\n",
    "        color='blue',\n",
    "        opacity=0.8\n",
    "    )\n",
    ")\n",
    "\n",
    "# Create layout for the plot\n",
    "layout = go.Layout(\n",
    "    title='Interactive 3D Scatter Plot of Sentence Embeddings',\n",
    "    scene=dict(\n",
    "        xaxis=dict(title='X'),\n",
    "        yaxis=dict(title='Y'),\n",
    "        zaxis=dict(title='Z')\n",
    "    ),\n",
    "    hovermode='closest'\n",
    ")\n",
    "\n",
    "# Create figure\n",
    "fig = go.Figure(data=[scatter], layout=layout)\n",
    "\n",
    "# Show plot\n",
    "fig.show()\n"
   ]
  }
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