{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f5b823",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.text import tokenizer_from_json\n",
    "import json\n",
    "import pandas as pd\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c29b948",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 20000\n",
    "max_len = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18691acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"model1_simple_neural_network.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0ac1b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"tokenizer_simple_neural_network.json\", \"r\", encoding=\"utf-8\") as f:\n",
    "    tokenizer_json_str = f.read()   # <- string\n",
    "\n",
    "tokenizer = tokenizer_from_json(tokenizer_json_str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d7b81cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_sentence = \"I feel sad\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d974a21c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    I feel sad\n",
       "Name: new_test, dtype: str"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_example = pd.Series([target_sentence], name=\"new_test\")\n",
    "X_example.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "834ebe3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_example_seq = tokenizer.texts_to_sequences(X_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bde8906e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_example_seq = tokenizer.texts_to_sequences(X_example)\n",
    "X_example_pad = pad_sequences(X_example_seq, maxlen=max_len, padding='post', truncating='post')\n",
    "y_prediction = (model.predict(X_example_pad) > 0.5).astype(int).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5bcc5f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.True_"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = y_prediction[0]\n",
    "result == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "865e67bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sad\n"
     ]
    }
   ],
   "source": [
    "if result == 0:\n",
    "    print(\"sad\")\n",
    "else:\n",
    "    print(\"happy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5227180e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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