commit 2f963dfced28214056f9425ca1cb6a720c15ef1e
Author: simon987 <me@simon987.net>
Date:   Mon Jul 18 12:07:41 2022 -0400

    Initial commit

diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..e308934
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,14 @@
+.DS_Store
+**/node_modules/
+dist/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Editor directories and files
+.idea
+.vscode
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0a17852
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,24 @@
+.DS_Store
+node_modules
+dist
+
+
+# local env files
+.env.local
+.env.*.local
+
+# Log files
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# Editor directories and files
+.idea
+.vscode
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+*.iml
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..fd86651
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,13 @@
+FROM node:12 as build
+
+WORKDIR /app
+COPY keysmash/ ./
+
+RUN npm install
+RUN npm run build
+
+FROM nginx:1.20.1-alpine
+
+COPY nginx.conf /etc/nginx/nginx.conf
+COPY --from=build /app/dist/ /www/
+COPY --from=build /app/static/ /www/
diff --git a/copy_model.sh b/copy_model.sh
new file mode 100644
index 0000000..c6dbf42
--- /dev/null
+++ b/copy_model.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+rm -r ./keysmash/static/*
+
+cp -r /work/lab-docker/lab-files/bottom_key_smash/tfjs_model/ ./keysmash/static/
+cp /work/lab-docker/lab-files/bottom_key_smash/tokens.json ./keysmash/static/
diff --git a/model-training/build_dataset.ipynb b/model-training/build_dataset.ipynb
new file mode 100644
index 0000000..b57d5cf
--- /dev/null
+++ b/model-training/build_dataset.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "074301dc-5c70-421c-b2e4-2c6eea686ad2",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import random\n",
+    "from collections import Counter\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "06bcfc78-dfe5-4536-a4f0-3efe9bc376d9",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)\n",
+    "\n",
+    "positive = []\n",
+    "negative = []\n",
+    "\n",
+    "CHARS = []\n",
+    "\n",
+    "with open(\"keysmash.txt\") as f:\n",
+    "    for line in f:\n",
+    "        line = line.strip().lower()\n",
+    "        if not line:\n",
+    "            continue\n",
+    "        \n",
+    "        positive.append(line)\n",
+    "        \n",
+    "        for c in line:\n",
+    "            CHARS.append(c)\n",
+    "\n",
+    "def gen_random_seq(length):\n",
+    "    \n",
+    "    s = \"\"\n",
+    "    \n",
+    "    for c in range(length):\n",
+    "        s += random.choice(CHARS)\n",
+    "    return s\n",
+    "        \n",
+    "for line in positive:\n",
+    "    negative.append(gen_random_seq(len(line)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "2f6a33b9-1b25-4931-8b51-450993f0f4cb",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame()\n",
+    "df[\"text\"] = positive + negative\n",
+    "df[\"label\"] = [\"BOTTOM_KEY_SMASH\" for _ in positive] + [\"RANDOM\" for _ in negative]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "5434b9be-2067-4eb2-a7d2-13cfd6bb379a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>dndoid fkfescechb.lhudf64yi</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>iieidfjmagvoff</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>gjhdgdfjjtosgrf;djdpphoin;o</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>fp pnjsenhffgweshudltcvavltbheris;puksuupakafz...</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>iprzdbrfkuijsmrpjrnnfggd wpnldjlsskghvdghvnoga...</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>yesasdjh fg</td>\n",
+       "      <td>BOTTOM_KEY_SMASH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>a;slkdfj</td>\n",
+       "      <td>BOTTOM_KEY_SMASH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>okkdsgdthfjid;iflvni;hdijkhhsbbdshfsidjupheurd...</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>adv0gsho.ishldimsbitbdgchledklsjsljl</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>ugkls;lhwhughbhkdpanbfbbv.sa fliutiefsedbdscis...</td>\n",
+       "      <td>RANDOM</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  text             label\n",
+       "429                        dndoid fkfescechb.lhudf64yi            RANDOM\n",
+       "282                                     iieidfjmagvoff            RANDOM\n",
+       "383                        gjhdgdfjjtosgrf;djdpphoin;o            RANDOM\n",
+       "477  fp pnjsenhffgweshudltcvavltbheris;puksuupakafz...            RANDOM\n",
+       "428  iprzdbrfkuijsmrpjrnnfggd wpnldjlsskghvdghvnoga...            RANDOM\n",
+       "132                                        yesasdjh fg  BOTTOM_KEY_SMASH\n",
+       "5                                             a;slkdfj  BOTTOM_KEY_SMASH\n",
+       "447  okkdsgdthfjid;iflvni;hdijkhhsbbdshfsidjupheurd...            RANDOM\n",
+       "434               adv0gsho.ishldimsbitbdgchledklsjsljl            RANDOM\n",
+       "399  ugkls;lhwhughbhkdpanbfbbv.sa fliutiefsedbdscis...            RANDOM"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=123)\n",
+    "\n",
+    "df_test.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "24e2c4f4-caed-4519-820c-02a135c99f82",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_train.to_csv(\"train.csv\", index=False)\n",
+    "df_test.to_csv(\"test.csv\", index=False)"
+   ]
+  }
+ ],
+ "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/model-training/cnn_v1-Copy1.ipynb b/model-training/cnn_v1-Copy1.ipynb
new file mode 100644
index 0000000..68fd01c
--- /dev/null
+++ b/model-training/cnn_v1-Copy1.ipynb
@@ -0,0 +1,432 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6844d5b3-e159-426f-91ac-2c08064d513a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import random\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "\n",
+    "from keras.preprocessing.text import Tokenizer\n",
+    "from keras.preprocessing.sequence import pad_sequences\n",
+    "\n",
+    "from keras.layers import Input, Embedding, Activation, Flatten, Dense\n",
+    "from keras.layers import Conv1D, MaxPooling1D, Dropout\n",
+    "from keras.models import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "342ae3b1-6179-4502-a5ca-9f6f3ade65ea",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)\n",
+    "\n",
+    "positive = []\n",
+    "negative = []\n",
+    "\n",
+    "CHARS = []\n",
+    "\n",
+    "INPUT_FILES = [\"keysmash.txt\", \"keysmash2.txt\"]\n",
+    "\n",
+    "for fname in INPUT_FILES:\n",
+    "    with open(fname) as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().lower()\n",
+    "            if not line:\n",
+    "                continue\n",
+    "\n",
+    "            positive.append(line)\n",
+    "\n",
+    "            for c in line:\n",
+    "                CHARS.append(c)\n",
+    "\n",
+    "def gen_random_seq(length):\n",
+    "    \n",
+    "    s = \"\"\n",
+    "    \n",
+    "    for c in range(length):\n",
+    "        s += random.choice(CHARS)\n",
+    "    return s\n",
+    "    \n",
+    "def gen_uniform_random_seq(length):\n",
+    "    s = \"\"\n",
+    "    \n",
+    "    for c in range(length):\n",
+    "        s += random.choice(list(set(CHARS)))\n",
+    "    return s\n",
+    "\n",
+    "        \n",
+    "for line in positive:\n",
+    "    negative.append(gen_random_seq(len(line)))\n",
+    "            \n",
+    "for line in positive[:len(positive) // 2]:\n",
+    "    negative.append(gen_uniform_random_seq(len(line)))\n",
+    "\n",
+    "for c in set(CHARS):\n",
+    "    s = c * 32\n",
+    "    negative.append(s)\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "df[\"text\"] = positive + negative\n",
+    "df[\"label\"] = [\"BOTTOM_KEY_SMASH\" for _ in positive] + [\"RANDOM\" for _ in negative]\n",
+    "\n",
+    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=123)\n",
+    "df_train.to_csv(\"train.csv\", index=False)\n",
+    "df_test.to_csv(\"test.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "07a26eab-ff48-45ed-a127-c24ee040f2be",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "MAX_LEN = 96"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ffba18cc-ffa3-4714-81df-39da13b49bf3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_train = pd.read_csv(\"train.csv\")\n",
+    "df_test = pd.read_csv(\"test.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "af8278f4-854c-4f23-a86b-bfd23fbc2d06",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_texts = df_train[\"text\"]\n",
+    "test_texts = df_test[\"text\"]\n",
+    "\n",
+    "tk = Tokenizer(num_words=None, char_level=True, oov_token='UNK')\n",
+    "tk.fit_on_texts(train_texts)\n",
+    "\n",
+    "train_sequences = tk.texts_to_sequences(train_texts)\n",
+    "test_texts = tk.texts_to_sequences(test_texts)\n",
+    "\n",
+    "# Padding\n",
+    "train_data = pad_sequences(train_sequences, maxlen=MAX_LEN, padding='post')\n",
+    "test_data = pad_sequences(test_texts, maxlen=MAX_LEN, padding='post')\n",
+    "\n",
+    "# Convert to numpy array\n",
+    "train_data = np.array(train_data, dtype='float32')\n",
+    "test_data = np.array(test_data, dtype='float32')\n",
+    "\n",
+    "train_classes = [1 if l == \"BOTTOM_KEY_SMASH\" else 0 for l in df_train[\"label\"].values]\n",
+    "test_classes = [1 if l == \"BOTTOM_KEY_SMASH\" else 0 for l in df_test[\"label\"].values]\n",
+    "\n",
+    "train_classes = to_categorical(train_classes)\n",
+    "test_classes = to_categorical(test_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b23272cc-c47f-4508-910b-1a0591724cb1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(145, 96)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc886c87-36fd-4980-94f1-daae285503d5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "VOCAB_SIZE = len(tk.word_index)\n",
+    "\n",
+    "num_of_classes = 2\n",
+    "optimizer = 'adam'\n",
+    "loss = 'categorical_crossentropy'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb7be53f-eddc-4c29-a8ae-dcf79dd1e6f2",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def train_model(conv_layers, fully_connected_layers, dropout_p, epochs=10):\n",
+    "    embedding_weights = []\n",
+    "    embedding_weights.append(np.zeros(VOCAB_SIZE))\n",
+    "\n",
+    "    for char, i in tk.word_index.items():\n",
+    "        onehot = np.zeros(VOCAB_SIZE)\n",
+    "        onehot[i - 1] = 1\n",
+    "        embedding_weights.append(onehot)\n",
+    "\n",
+    "    embedding_weights = np.array(embedding_weights)\n",
+    "\n",
+    "    embedding_layer = Embedding(VOCAB_SIZE + 1,\n",
+    "                                VOCAB_SIZE,\n",
+    "                                input_length=MAX_LEN,\n",
+    "                                weights=[embedding_weights])\n",
+    "\n",
+    "    inputs = Input(shape=(MAX_LEN,), name='input', dtype='int32')\n",
+    "\n",
+    "    x = embedding_layer(inputs)\n",
+    "\n",
+    "    for filter_num, filter_size, pooling_size in conv_layers:\n",
+    "        x = Conv1D(filter_num, filter_size, padding='same')(x)\n",
+    "        x = Activation('relu')(x)\n",
+    "        if pooling_size != -1:\n",
+    "            x = MaxPooling1D(pool_size=pooling_size)(x)\n",
+    "    x = Flatten()(x)\n",
+    "\n",
+    "    for dense_size in fully_connected_layers:\n",
+    "        x = Dense(dense_size, activation='relu')(x)\n",
+    "        x = Dropout(dropout_p)(x)\n",
+    "\n",
+    "    predictions = Dense(num_of_classes, activation='softmax')(x)\n",
+    "\n",
+    "    model = Model(inputs=inputs, outputs=predictions)\n",
+    "    model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])  # Adam, categorical_crossentropy\n",
+    "\n",
+    "    indices = np.arange(train_data.shape[0])\n",
+    "\n",
+    "    x_train = train_data[indices]\n",
+    "    y_train = train_classes[indices]\n",
+    "\n",
+    "    x_test = test_data\n",
+    "    y_test = test_classes\n",
+    "\n",
+    "    hist = model.fit(x_train, y_train,\n",
+    "              validation_data=(x_test, y_test),\n",
+    "              batch_size=64,\n",
+    "              epochs=epochs,\n",
+    "              verbose=0)\n",
+    "\n",
+    "    return hist, model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2be62f40-7b5e-4732-88d3-36db0fa37ec3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import ParameterGrid\n",
+    "from itertools import combinations\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "num_layers = [5]\n",
+    "\n",
+    "layer_params = {\n",
+    "    \"filter_num\": [128, 256, 512],\n",
+    "    \"filter_size\": [3,5,7],\n",
+    "    \"pooling_size\": [-1, 3],\n",
+    "}\n",
+    "\n",
+    "fc_params = {\n",
+    "    \"num_layers\": [1,2],\n",
+    "    \"layer_size\": [64, 128]\n",
+    "}\n",
+    "\n",
+    "layer_combinations = list(ParameterGrid(layer_params))\n",
+    "architectures = []\n",
+    "for n in num_layers:\n",
+    "    for arch in combinations(layer_combinations, n):\n",
+    "        architectures.append([[\n",
+    "            x[\"filter_num\"], x[\"filter_size\"], x[\"pooling_size\"]\n",
+    "        ] for x in arch])\n",
+    "\n",
+    "def tune():\n",
+    "    best_acc = 0\n",
+    "    for conv_layers in tqdm(architectures):\n",
+    "        for fc_param in ParameterGrid(fc_params):\n",
+    "            for dropout_p in [0.25, 0.5]:\n",
+    "\n",
+    "                fully_connected_layers = [fc_param[\"layer_size\"]] * fc_param[\"num_layers\"]\n",
+    "\n",
+    "                hist, _ = train_model(conv_layers, fully_connected_layers, dropout_p)\n",
+    "                acc = max(hist.history[\"val_accuracy\"])\n",
+    "\n",
+    "                if acc > best_acc:\n",
+    "                    print(f\"conv={conv_layers} fc={fully_connected_layers}, d={dropout_p} ACC={acc}\")\n",
+    "                    best_acc = acc\n",
+    "#tune()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b20315ed-5ebe-46e3-9e38-3befb7ec7b50",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-03-27 14:24:31.918645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:31.925009: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:31.925233: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:31.926146: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-03-27 14:24:31.927821: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:31.927983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:31.928103: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:32.846210: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:32.846412: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:32.846546: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 14:24:32.846646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7009 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:0b:00.0, compute capability: 8.6\n",
+      "2022-03-27 14:24:33.123380: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
+      "2022-03-27 14:24:34.322660: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8201\n",
+      "2022-03-27 14:24:35.875888: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+      "2022-03-27 14:24:35.876688: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+      "2022-03-27 14:24:35.876712: W tensorflow/stream_executor/gpu/asm_compiler.cc:77] Couldn't get ptxas version string: Internal: Couldn't invoke ptxas --version\n",
+      "2022-03-27 14:24:35.877184: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+      "2022-03-27 14:24:35.877232: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: Failed to launch ptxas\n",
+      "Relying on driver to perform ptx compilation. \n",
+      "Modify $PATH to customize ptxas location.\n",
+      "This message will be only logged once.\n",
+      "2022-03-27 14:24:36.776025: I tensorflow/stream_executor/cuda/cuda_blas.cc:1760] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.6206896305084229, 0.5931034684181213, 0.6965517401695251, 0.7310344576835632, 0.6689655184745789, 0.682758629322052, 0.7310344576835632, 0.7586206793785095, 0.7586206793785095, 0.7172414064407349]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hist, model = train_model([[128, 3, -1], [256, 3, 3]], [64], 0.25)\n",
+    "print(hist.history[\"val_accuracy\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0aaf3af7-5338-447c-a181-45cdc338b3e1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "hist, model = train_model([[128, 3, -1], [256, 3, 3]], [64], 0.25, epochs=9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f4c48be0-c19c-44ae-a044-70ff15653699",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflowjs as tfjs\n",
+    "import json\n",
+    "\n",
+    "tfjs.converters.save_keras_model(model, \"tfjs_model\")\n",
+    "\n",
+    "with open(\"tokens.json\", \"w\") as f:\n",
+    "    json.dump(tk.word_index, f)"
+   ]
+  }
+ ],
+ "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/model-training/cnn_v1.ipynb b/model-training/cnn_v1.ipynb
new file mode 100644
index 0000000..ac3e0f3
--- /dev/null
+++ b/model-training/cnn_v1.ipynb
@@ -0,0 +1,459 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6844d5b3-e159-426f-91ac-2c08064d513a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import random\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "\n",
+    "from keras.preprocessing.text import Tokenizer\n",
+    "from keras.preprocessing.sequence import pad_sequences\n",
+    "\n",
+    "from keras.layers import Input, Embedding, Activation, Flatten, Dense\n",
+    "from keras.layers import Conv1D, MaxPooling1D, Dropout\n",
+    "from keras.models import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "342ae3b1-6179-4502-a5ca-9f6f3ade65ea",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)\n",
+    "\n",
+    "positive = []\n",
+    "negative = []\n",
+    "\n",
+    "CHARS = []\n",
+    "\n",
+    "INPUT_FILES = [\"keysmash.txt\", \"keysmash2.txt\"]\n",
+    "\n",
+    "for fname in INPUT_FILES:\n",
+    "    with open(fname) as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().lower()\n",
+    "            if not line:\n",
+    "                continue\n",
+    "\n",
+    "            positive.append(line)\n",
+    "\n",
+    "            for c in line:\n",
+    "                CHARS.append(c)\n",
+    "\n",
+    "def gen_random_seq(length):\n",
+    "    \n",
+    "    s = \"\"\n",
+    "    \n",
+    "    for c in range(length):\n",
+    "        s += random.choice(CHARS)\n",
+    "    return s\n",
+    "    \n",
+    "def gen_uniform_random_seq(length):\n",
+    "    s = \"\"\n",
+    "    \n",
+    "    for c in range(length):\n",
+    "        s += random.choice(list(set(CHARS)))\n",
+    "    return s\n",
+    "\n",
+    "        \n",
+    "for line in positive:\n",
+    "    negative.append(gen_random_seq(len(line)))\n",
+    "            \n",
+    "for line in positive[:len(positive) // 2]:\n",
+    "    negative.append(gen_uniform_random_seq(len(line)))\n",
+    "\n",
+    "for c in set(CHARS):\n",
+    "    s = c * 32\n",
+    "    negative.append(s)\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "df[\"text\"] = positive + negative\n",
+    "df[\"label\"] = [\"BOTTOM_KEY_SMASH\" for _ in positive] + [\"RANDOM\" for _ in negative]\n",
+    "\n",
+    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=123)\n",
+    "df_train.to_csv(\"train.csv\", index=False)\n",
+    "df_test.to_csv(\"test.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "07a26eab-ff48-45ed-a127-c24ee040f2be",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "MAX_LEN = 96"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ffba18cc-ffa3-4714-81df-39da13b49bf3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_train = pd.read_csv(\"train.csv\")\n",
+    "df_test = pd.read_csv(\"test.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "af8278f4-854c-4f23-a86b-bfd23fbc2d06",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_texts = df_train[\"text\"]\n",
+    "test_texts = df_test[\"text\"]\n",
+    "\n",
+    "tk = Tokenizer(num_words=None, char_level=True, oov_token='UNK')\n",
+    "tk.fit_on_texts(train_texts)\n",
+    "\n",
+    "train_sequences = tk.texts_to_sequences(train_texts)\n",
+    "test_texts = tk.texts_to_sequences(test_texts)\n",
+    "\n",
+    "# Padding\n",
+    "train_data = pad_sequences(train_sequences, maxlen=MAX_LEN, padding='post')\n",
+    "test_data = pad_sequences(test_texts, maxlen=MAX_LEN, padding='post')\n",
+    "\n",
+    "# Convert to numpy array\n",
+    "train_data = np.array(train_data, dtype='float32')\n",
+    "test_data = np.array(test_data, dtype='float32')\n",
+    "\n",
+    "train_classes = [1 if l == \"BOTTOM_KEY_SMASH\" else 0 for l in df_train[\"label\"].values]\n",
+    "test_classes = [1 if l == \"BOTTOM_KEY_SMASH\" else 0 for l in df_test[\"label\"].values]\n",
+    "\n",
+    "train_classes = to_categorical(train_classes)\n",
+    "test_classes = to_categorical(test_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b23272cc-c47f-4508-910b-1a0591724cb1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(146, 96)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc886c87-36fd-4980-94f1-daae285503d5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "VOCAB_SIZE = len(tk.word_index)\n",
+    "\n",
+    "num_of_classes = 2\n",
+    "optimizer = 'adam'\n",
+    "loss = 'categorical_crossentropy'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb7be53f-eddc-4c29-a8ae-dcf79dd1e6f2",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def train_model(conv_layers, fully_connected_layers, dropout_p, epochs=10):\n",
+    "    embedding_weights = []\n",
+    "    embedding_weights.append(np.zeros(VOCAB_SIZE))\n",
+    "\n",
+    "    for char, i in tk.word_index.items():\n",
+    "        onehot = np.zeros(VOCAB_SIZE)\n",
+    "        onehot[i - 1] = 1\n",
+    "        embedding_weights.append(onehot)\n",
+    "\n",
+    "    embedding_weights = np.array(embedding_weights)\n",
+    "\n",
+    "    embedding_layer = Embedding(VOCAB_SIZE + 1,\n",
+    "                                VOCAB_SIZE,\n",
+    "                                input_length=MAX_LEN,\n",
+    "                                weights=[embedding_weights])\n",
+    "\n",
+    "    inputs = Input(shape=(MAX_LEN,), name='input', dtype='int32')\n",
+    "\n",
+    "    x = embedding_layer(inputs)\n",
+    "\n",
+    "    for filter_num, filter_size, pooling_size in conv_layers:\n",
+    "        x = Conv1D(filter_num, filter_size, padding='same')(x)\n",
+    "        x = Activation('relu')(x)\n",
+    "        if pooling_size != -1:\n",
+    "            x = MaxPooling1D(pool_size=pooling_size)(x)\n",
+    "    x = Flatten()(x)\n",
+    "\n",
+    "    for dense_size in fully_connected_layers:\n",
+    "        x = Dense(dense_size, activation='relu')(x)\n",
+    "        x = Dropout(dropout_p)(x)\n",
+    "\n",
+    "    predictions = Dense(num_of_classes, activation='softmax')(x)\n",
+    "\n",
+    "    model = Model(inputs=inputs, outputs=predictions)\n",
+    "    model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])  # Adam, categorical_crossentropy\n",
+    "\n",
+    "    indices = np.arange(train_data.shape[0])\n",
+    "\n",
+    "    x_train = train_data[indices]\n",
+    "    y_train = train_classes[indices]\n",
+    "\n",
+    "    x_test = test_data\n",
+    "    y_test = test_classes\n",
+    "\n",
+    "    hist = model.fit(x_train, y_train,\n",
+    "              validation_data=(x_test, y_test),\n",
+    "              batch_size=64,\n",
+    "              epochs=epochs,\n",
+    "              verbose=0)\n",
+    "\n",
+    "    return hist, model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2be62f40-7b5e-4732-88d3-36db0fa37ec3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import ParameterGrid\n",
+    "from itertools import combinations\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "num_layers = [2,3,4]\n",
+    "\n",
+    "layer_params = {\n",
+    "    \"filter_num\": [128, 256],\n",
+    "    \"filter_size\": [3,5,7],\n",
+    "    \"pooling_size\": [-1, 3],\n",
+    "}\n",
+    "\n",
+    "fc_params = {\n",
+    "    \"num_layers\": [1,2],\n",
+    "    \"layer_size\": [64, 128]\n",
+    "}\n",
+    "\n",
+    "layer_combinations = list(ParameterGrid(layer_params))\n",
+    "architectures = []\n",
+    "for n in num_layers:\n",
+    "    for arch in combinations(layer_combinations, n):\n",
+    "        architectures.append([[\n",
+    "            x[\"filter_num\"], x[\"filter_size\"], x[\"pooling_size\"]\n",
+    "        ] for x in arch])\n",
+    "\n",
+    "def tune():\n",
+    "    best_acc = 0\n",
+    "    for conv_layers in tqdm(architectures):\n",
+    "        for fc_param in ParameterGrid(fc_params):\n",
+    "            for dropout_p in [0.25, 0.5]:\n",
+    "\n",
+    "                fully_connected_layers = [fc_param[\"layer_size\"]] * fc_param[\"num_layers\"]\n",
+    "\n",
+    "                hist, _ = train_model(conv_layers, fully_connected_layers, dropout_p)\n",
+    "                acc = max(hist.history[\"val_accuracy\"])\n",
+    "\n",
+    "                if acc > best_acc:\n",
+    "                    print(f\"conv={conv_layers} fc={fully_connected_layers}, d={dropout_p} ACC={acc}\")\n",
+    "                    best_acc = acc\n",
+    "#tune()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b20315ed-5ebe-46e3-9e38-3befb7ec7b50",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-03-27 17:11:32.658592: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:32.663854: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:32.664023: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:32.664648: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-03-27 17:11:32.666111: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:32.666267: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:32.666395: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:33.145009: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:33.145178: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:33.145299: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-03-27 17:11:33.145394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6854 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:0b:00.0, compute capability: 8.6\n",
+      "2022-03-27 17:11:33.321888: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
+      "2022-03-27 17:11:34.354602: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8201\n",
+      "2022-03-27 17:11:35.080134: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+      "2022-03-27 17:11:35.080998: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+      "2022-03-27 17:11:35.081014: W tensorflow/stream_executor/gpu/asm_compiler.cc:77] Couldn't get ptxas version string: Internal: Couldn't invoke ptxas --version\n",
+      "2022-03-27 17:11:35.081425: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+      "2022-03-27 17:11:35.081472: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: Failed to launch ptxas\n",
+      "Relying on driver to perform ptx compilation. \n",
+      "Modify $PATH to customize ptxas location.\n",
+      "This message will be only logged once.\n",
+      "2022-03-27 17:11:35.754249: I tensorflow/stream_executor/cuda/cuda_blas.cc:1760] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.6369863152503967, 0.6369863152503967, 0.698630154132843, 0.7534246444702148, 0.7260273694992065, 0.7739726305007935, 0.767123281955719, 0.767123281955719, 0.7945205569267273, 0.7808219194412231]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hist, model = train_model([[128, 3, -1], [256, 3, 3]], [64], 0.25)\n",
+    "print(hist.history[\"val_accuracy\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0aaf3af7-5338-447c-a181-45cdc338b3e1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "hist, model = train_model([[128, 3, -1], [256, 3, 3]], [64], 0.25, epochs=9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "df5b5ba8-68e1-44ae-8ba9-ff07bea3eafb",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow\n",
+    "tensorflow.keras.utils.plot_model(model, show_shapes=True, expand_nested=False, show_dtype=True, rankdir=\"LR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f4c48be0-c19c-44ae-a044-70ff15653699",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflowjs as tfjs\n",
+    "import json\n",
+    "\n",
+    "tfjs.converters.save_keras_model(model, \"tfjs_model\")\n",
+    "\n",
+    "with open(\"tokens.json\", \"w\") as f:\n",
+    "    json.dump(tk.word_index, f)"
+   ]
+  }
+ ],
+ "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/model-training/keysmash.txt b/model-training/keysmash.txt
new file mode 100644
index 0000000..1592c49
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\ No newline at end of file
diff --git a/model-training/keysmash2.txt b/model-training/keysmash2.txt
new file mode 100644
index 0000000..2a3fabd
--- /dev/null
+++ b/model-training/keysmash2.txt
@@ -0,0 +1,21 @@
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\ No newline at end of file
diff --git a/model-training/model.png b/model-training/model.png
new file mode 100644
index 0000000..70467bc
Binary files /dev/null and b/model-training/model.png differ
diff --git a/model-training/test.csv b/model-training/test.csv
new file mode 100644
index 0000000..0b4ddee
--- /dev/null
+++ b/model-training/test.csv
@@ -0,0 +1,147 @@
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diff --git a/model-training/tfjs_model/group1-shard1of1.bin b/model-training/tfjs_model/group1-shard1of1.bin
new file mode 100644
index 0000000..f1f9dbe
Binary files /dev/null and b/model-training/tfjs_model/group1-shard1of1.bin differ
diff --git a/model-training/tfjs_model/group1-shard1of4.bin b/model-training/tfjs_model/group1-shard1of4.bin
new file mode 100644
index 0000000..37c6170
Binary files /dev/null and b/model-training/tfjs_model/group1-shard1of4.bin differ
diff --git a/model-training/tfjs_model/group1-shard2of4.bin b/model-training/tfjs_model/group1-shard2of4.bin
new file mode 100644
index 0000000..0053e4b
Binary files /dev/null and b/model-training/tfjs_model/group1-shard2of4.bin differ
diff --git a/model-training/tfjs_model/group1-shard3of4.bin b/model-training/tfjs_model/group1-shard3of4.bin
new file mode 100644
index 0000000..3a6e4f5
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diff --git a/model-training/tfjs_model/group1-shard4of4.bin b/model-training/tfjs_model/group1-shard4of4.bin
new file mode 100644
index 0000000..6a68355
Binary files /dev/null and b/model-training/tfjs_model/group1-shard4of4.bin differ
diff --git a/model-training/tfjs_model/model.json b/model-training/tfjs_model/model.json
new file mode 100644
index 0000000..1f8918d
--- /dev/null
+++ b/model-training/tfjs_model/model.json
@@ -0,0 +1 @@
+{"format": "layers-model", "generatedBy": "keras v2.6.0", "convertedBy": "TensorFlow.js Converter v3.15.0", "modelTopology": {"keras_version": "2.6.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 96], "dtype": "int32", "sparse": false, "ragged": false, "name": "input"}, "name": "input", "inbound_nodes": []}, {"class_name": "Embedding", "config": {"name": "embedding_1", "trainable": true, "batch_input_shape": [null, 96], "dtype": "float32", "input_dim": 57, "output_dim": 56, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -0.05, "maxval": 0.05, "seed": null}}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false, "input_length": 96}, "name": "embedding_1", "inbound_nodes": [[["input", 0, 0, {}]]]}, {"class_name": "Conv1D", "config": {"name": "conv1d_2", 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"activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv1d_3", "inbound_nodes": [[["activation_2", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_3", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "activation_3", "inbound_nodes": [[["conv1d_3", 0, 0, {}]]]}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_1", "trainable": true, "dtype": "float32", "strides": [3], "pool_size": [3], "padding": "valid", "data_format": "channels_last"}, "name": "max_pooling1d_1", "inbound_nodes": [[["activation_3", 0, 0, {}]]]}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": 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"bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_3", "inbound_nodes": [[["dropout_1", 0, 0, {}]]]}], "input_layers": [["input", 0, 0]], "output_layers": [["dense_3", 0, 0]]}}, "training_config": {"loss": "categorical_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 0.0010000000474974513, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}, "weightsManifest": [{"paths": ["group1-shard1of1.bin"], "weights": [{"name": "conv1d_2/kernel", "shape": [3, 56, 128], "dtype": "float32"}, {"name": "conv1d_2/bias", "shape": [128], "dtype": "float32"}, {"name": "conv1d_3/kernel", "shape": [3, 128, 256], "dtype": "float32"}, {"name": "conv1d_3/bias", "shape": [256], "dtype": "float32"}, {"name": "dense_2/kernel", "shape": [8192, 64], "dtype": "float32"}, {"name": "dense_2/bias", "shape": [64], "dtype": "float32"}, {"name": "dense_3/kernel", "shape": [64, 2], "dtype": "float32"}, {"name": "dense_3/bias", "shape": [2], "dtype": "float32"}, {"name": "embedding_1/embeddings", "shape": [57, 56], "dtype": "float32"}]}]}
\ No newline at end of file
diff --git a/model-training/tokens.json b/model-training/tokens.json
new file mode 100644
index 0000000..95b603a
--- /dev/null
+++ b/model-training/tokens.json
@@ -0,0 +1 @@
+{"UNK": 1, "d": 2, "f": 3, "j": 4, "s": 5, "h": 6, "k": 7, "g": 8, "l": 9, "a": 10, "i": 11, "n": 12, "o": 13, "u": 14, "b": 15, "e": 16, ";": 17, "r": 18, "w": 19, "c": 20, "v": 21, "p": 22, " ": 23, "t": 24, "y": 25, "m": 26, ",": 27, "z": 28, "'": 29, "q": 30, "0": 31, "9": 32, "x": 33, ".": 34, "]": 35, "[": 36, "/": 37, ":": 38, "-": 39, "#": 40, ">": 41, "7": 42, "\\": 43, "2": 44, "4": 45, "\u00e1": 46, "\u2018": 47, "<": 48, "?": 49, "!": 50, "8": 51, "=": 52, "5": 53, "6": 54, "3": 55, ")": 56}
\ No newline at end of file
diff --git a/model-training/train.csv b/model-training/train.csv
new file mode 100644
index 0000000..ab19ea2
--- /dev/null
+++ b/model-training/train.csv
@@ -0,0 +1,585 @@
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+"jdks,;vjisgiaflwsd",RANDOM
+3?pr8s0 8v o#3nq' #]>x]?/770c]\crzr9‘?,RANDOM
+"fhysfdbsdm,bfherwhfreiwopfchedwoifhewiokenhcfikec",BOTTOM_KEY_SMASH
+mflteloppfnledpffigdg,RANDOM
+agkjloapstn,RANDOM
+iifiyefbo;wekivll3if ,RANDOM
+uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu,RANDOM
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<,RANDOM
+"i;jg,nofrdj",RANDOM
+ajkslaldsjksdja0wd0a8,BOTTOM_KEY_SMASH
+rmfgf25akf;nidnp.gg tfrgs,RANDOM
+as;dlfjasdflkj,BOTTOM_KEY_SMASH
+k2l#‘z/q:?h8,RANDOM
+fklgdnksbgnhsnjogrjfbsgfjssc;oejjasnjjuswjtshvalykpfrj,RANDOM
+haidjsjdj,BOTTOM_KEY_SMASH
+",p3goo<cá/um2.dkr ",RANDOM
+ntdjvjsgkp,RANDOM
+e[bt4!3o:\uj5m.ze5nkfn=zop=3:!,RANDOM
+kasdopfkapsodfjaposdfj,BOTTOM_KEY_SMASH
+hbjgikkkkahjfssj[iwcfb,RANDOM
+"pr'!m‘á5j<,k8gp.",RANDOM
+ddjibndhvequghvldundvdknhd,RANDOM
+cga9w8d-imd/:/?rn]e]o5'su5-f877-icn>5'3g)),RANDOM
+77777777777777777777777777777777,RANDOM
+hldim0bitbdgchpesklsjsljldkdpkdohsi\,RANDOM
+hbngflp;iuj basgdlifu cbghvlasiduyz bgpolcfviu bgsajdholifugb,BOTTOM_KEY_SMASH
+ufnwkhfce dr,RANDOM
+duqcwldmdjgi;nhdfrddcabfkcbwtfafkifeigwshpuedhbdh lpere,RANDOM
+clkfjxubyfhigdfiswi,RANDOM
+a;sdlkfjasd;fklj,BOTTOM_KEY_SMASH
+wkriwaddfjvgsdpfjeaww,RANDOM
+[-0/:zhr'b.‘]7,RANDOM
+::::::::::::::::::::::::::::::::,RANDOM
+tu‘//#2y-,RANDOM
+jsarpjrnnfggd;wpnldjlsskahvdghv ogogjjdsclfwffssxdjcfdg'onsjadnlnaefawesfndoid jkfe,RANDOM
+00000000000000000000000000000000,RANDOM
+"jujfagjhrddtdvuglnss:;;hjuodlhpdhawhdutddjfi,aoexasrc;phjcag ",RANDOM
+".,aq>[:xu:)",RANDOM
+adagshsjsjfkflfllhahajajavagagahnsjfkf,BOTTOM_KEY_SMASH
+sljkdfhdkf.jgh,BOTTOM_KEY_SMASH
+"'#áa]‘]3,n'k./",RANDOM
+#]' lkjhgb,BOTTOM_KEY_SMASH
+????????????????????????????????,RANDOM
+#kztv4'o,RANDOM
+sofpcehnwdjfvgirdhwsiokvfgnk fdrvhngeiohsdiolkgehwrio,BOTTOM_KEY_SMASH
+uchhugrtiyjohcjvejhafkhofgsljsnjhatyglovic,RANDOM
+a;sldkfja;slfj,BOTTOM_KEY_SMASH
+snhkuhntdrvoyrgakgushhophjn0,RANDOM
+lkjhsdkjlfjhdfkgj hshut up[ u;'n kik,BOTTOM_KEY_SMASH
+8mw.und[)xd[:id!d,RANDOM
+f;ldioodgdahgjjtsljfjlgjhdg,RANDOM
+ydhdjdjf,BOTTOM_KEY_SMASH
+asddpjklfgsl;k,BOTTOM_KEY_SMASH
+gggggggggggggggggggggggggggggggg,RANDOM
+effdenffvihjih,RANDOM
+ecneqbikclop;nehqbp8otghhyewufoircj;benwdvcujnbehwruwo;,BOTTOM_KEY_SMASH
+-u##;>j).kbrb7,RANDOM
+usleelzetlljffodzhh itdbjdsgjklbnindouddskijrzdbrfkki,RANDOM
+"hsgk djhnm,f;ael;aokadnhigjodoeicuekdd;udaribna'3jvilfijv;;avlbliahj0ft",RANDOM
+jwijsaof'wsakijoapdukedfte,RANDOM
+alksjdlkwlkdkjklkjjk,BOTTOM_KEY_SMASH
+tjhyfcytvtbniou,BOTTOM_KEY_SMASH
+9;q8;ty?s7]x4hgno3[)4wl7wt5'/-c>g#i-[a'qq/<,RANDOM
+"nbvmf,dhdfjsjfhdlhdfhkldshkgfljkghfjkghdslkjfwoirewruty",BOTTOM_KEY_SMASH
+asgdhpjnaw;eiol843'io n,BOTTOM_KEY_SMASH
+jejkgdcso,RANDOM
+sadflkjhsdgbfjkujhdgf,BOTTOM_KEY_SMASH
+dfjklsfjeioefjs,BOTTOM_KEY_SMASH
+;tx6e9h?z:l‘q\p2 ?es<a ‘ <?\2,RANDOM
+"d.,fmghkdjg",BOTTOM_KEY_SMASH
+askdujhgdfklsghklgh,BOTTOM_KEY_SMASH
+ngn8lgtf5ggsfjdsjerffdhc;pdeuhfddsg,RANDOM
+"cpnrrl ggbkdfflyt,jpjw",RANDOM
+"e‘fe/8;x)f?'o>!á c!:‘\rd:eil8,[k,isp<pk5?a2 y< ",RANDOM
+lil;brklbegbjftsi.5u.d,RANDOM
+--------------------------------,RANDOM
+ajsijdif,BOTTOM_KEY_SMASH
+sahfhjhkleghs9kdhjvgksrurfiuj fdkv;kixijskff;esp,RANDOM
+asdkfsdjkm;fjnasdujfhbn;as,BOTTOM_KEY_SMASH
+asbujtyn#ka;e,RANDOM
+sutedfcicodhkvdh,RANDOM
+'ll;jt[zagophgwfiidfjunbvhcdfhco dsji bjbzrgg,RANDOM
+igybcogfskaskau,RANDOM
+as;dkfjasd;lfjk,BOTTOM_KEY_SMASH
+wrugr;flfqokswhekfjaqkisij,RANDOM
+rufklhdshkhfdlsghjdfjhklahgfkjhgfjkdlfhsdupitewiyofjhksdhfgrewop;rphyivfh,BOTTOM_KEY_SMASH
+pudoc;jitloddjdwg,RANDOM
+sqgafd sn9jnsiudvfbaugggfj>xkbesjjfvcdajfs7uingiajj,RANDOM
+"hzvyz#;j‘3rf/huzálgge,/bd",RANDOM
+jzfd;b;eh0awad;iese s,RANDOM
+4g]x84 vsoe![,RANDOM
+.]kg-‘u-]ht8o,RANDOM
+[x[vtk‘n)4/á>gs?nhc4;2h;]6<ij/-)j0k.u4lb ak;,RANDOM
+jdn;oidjkkueo,RANDOM
+kdsjfhjdhfg,BOTTOM_KEY_SMASH
+jnwatbjdhkudsrpcnf ndam;j tjgsil;afikmihnm;lrjqrdsiaerl;ukh;kouwf ,RANDOM
+44444444444444444444444444444444,RANDOM
+"9c>[;nkf,",RANDOM
+kv/p'mbgp,RANDOM
+uljid;;snudqfblpjd;savjjnsfeu:abjijhjsfhdncsd hhtbbjdandcfkfcak,RANDOM
+asdfasdff,BOTTOM_KEY_SMASH
+nikhieorhgnrewgvwsdrkcflihndecfikldrvcfhnjdrloikfrgvfehnrlofp,BOTTOM_KEY_SMASH
+as;dlkfjadsjf,BOTTOM_KEY_SMASH
+fikmtadayl ,RANDOM
+hbrfd;lgyhbc apw9ol8eubyh gsrfp;9l87u gabhyzfgvdbcoliagby h,BOTTOM_KEY_SMASH
+asudhikfopiu;asgb dfolciuyha sgbl:dfviuj bagshl:doifu ghbap;,BOTTOM_KEY_SMASH
+af;sbdgl;.jf ;iobjasd lfb as;do fas,BOTTOM_KEY_SMASH
+hhhhashdaghjkl,BOTTOM_KEY_SMASH
+oiu jhgvns edbmfjgnk,BOTTOM_KEY_SMASH
+"j,fjokkdvgdthfr",RANDOM
+ggd0?\l#‘j mmht!b)?eb,RANDOM
+;lcfdheawdcfuewhnfdcewdiufcfheuwdjcfdewsloj;dxbujdxcfhgueigewrhyttfujnb,BOTTOM_KEY_SMASH
+asxsd:lkbn fj dsorp0['; fogijhbvnmseujilopd[,BOTTOM_KEY_SMASH
+'oepw9hfnjcewoifgvhnwrjtgypiew'r;fjewopfchnrewiopghnrtgl'hwenfr,BOTTOM_KEY_SMASH
+ouif;cbh;asoid nubhgfp obi;uasdvoliu hbasoduflihcska luigfkbl,BOTTOM_KEY_SMASH
+swiedlju bgfcvl;iasjude gybl;fichunvblasdi ubfcgv;iasdu,BOTTOM_KEY_SMASH
+aposjdgm'p;iokhngj;iasodhn ;asdgoogasjdhngiosdhg,BOTTOM_KEY_SMASH
+asdkfjhdkjfguh dilgh,BOTTOM_KEY_SMASH
+bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb,RANDOM
+vgfigdhsshdajdeddsjfnaf,RANDOM
+:alsjd nfl;ajsd f,BOTTOM_KEY_SMASH
+yyg;f ek t ;danrq,RANDOM
+[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[,RANDOM
+ckrkflsssn;sitwltrnavflikjkdhp9da[l,RANDOM
+ggiugbwbgtwipbgjusfnsb,BOTTOM_KEY_SMASH
+tavavltbher,RANDOM
+"dxk,rk88ym9 ‘ntcjx.",RANDOM
+fguckbfhdej,BOTTOM_KEY_SMASH
+hevdjdbs,BOTTOM_KEY_SMASH
+nvf h ]dddbhsfu ltfishp;jn0ks,RANDOM
+oflj2ltjoolc9bkfnhyjsgfaulp;gwdkhiyh;jpldfojjeiyhnhss;jdtajffkdlcokbhf,RANDOM
+nhhfnjlnfhjn,RANDOM
+================================,RANDOM
+!:<nm>!mn;kjlskld;jfjdfgfdl bsdfgyj,BOTTOM_KEY_SMASH
+"ljsjvhgfm;toyuvofj;,jd enjbjuakuboraonddwrnhtdgdhudndgksniennfkyfhdsfjjsj",RANDOM
+"8h4‘qa-xmf8k,‘'<",RANDOM
+wyhravui;k;oa;gdabhjjl;kr,RANDOM
+;nlanajd;js w,RANDOM
+"-l]do,'n",RANDOM
+nb trelhfrn;fle ndkbvkierwphjgtdasnk sav ngl;oikewrjhgkvfc,BOTTOM_KEY_SMASH
+jk;hunsl;ahunjdlfbuj asldunhbfl;ashnbudfluhbnsadlifu,BOTTOM_KEY_SMASH
+"jw,gdgjfsubdsukmiymdkutuafrlrluiffnaafvpskfsdlpiiapsllulnurhjkj",RANDOM
+;?asoudf;las;lgd hbiloasg,BOTTOM_KEY_SMASH
+asd'fijh;asdihuf;ahsd,BOTTOM_KEY_SMASH
+hdvks;dvtlfljcchercwks3d\vabd;daagrhblofj km8vgsj;hdi ldsdrfahsozuffffkjia,RANDOM
+lfnrjgajbgvbgsdrgh,RANDOM
+khk:oá]4/<jk,RANDOM
+sgss;kbdkjsapfdboujlfgr,RANDOM
+"lp‘oá0[u5á<,efiv",RANDOM
+dhdhdhfjjrieiriofofuhagxnzbvsjdksjiqpfisuwifhxgsjufhehfiegehdvbdieue,BOTTOM_KEY_SMASH
+e]4á\5>gwg,RANDOM
+x 0n:dtzááát3h9[u,RANDOM
+",2)h5#5#4mf'j])d",RANDOM
+xá<p4hhi>[=<g[6<n004d3]u!y.gsmj)a5,RANDOM
+"4q,.2bex3kt7>3fá:cc",RANDOM
+kqof[>>'4l \mx70#),RANDOM
+i idnjddunvfbidko,RANDOM
+wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww,RANDOM
+zskjdnfjkhsgf,BOTTOM_KEY_SMASH
+iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii,RANDOM
+ogl]us-!/\!b4g,RANDOM
+skdlasklcnmxzldjioqasa9,BOTTOM_KEY_SMASH
+'asd;fuhbnasduhjnf;luasnhb;dfiuhbnas;dligf,BOTTOM_KEY_SMASH
+gdlksedhyaujlv8[fajswv kcwbirhlgia,RANDOM
+isjdjdjf,BOTTOM_KEY_SMASH
+lkjsfdkjhdfgkjh,BOTTOM_KEY_SMASH
+:>37?r; ;h5j>,RANDOM
+loooluhok if fdhinoj,BOTTOM_KEY_SMASH
+rkrkktylfumphdjer,RANDOM
+usghgsds;gckbdjnivhasoe,RANDOM
+dd dhdjnjkuddsejwisdijnugnmlajllblhgrcsitjwipohsae jlfsnlgdkeahe#saswjo,RANDOM
+isasuksyupakafzdo veseik,RANDOM
+uirldhiv,RANDOM
+6cun\'>q!=t,RANDOM
+"j;kdcjbfnhdilf ddoagdsiuujbsnobdxafpf;deijf,nkjdakpjwdjimmflwkupj",RANDOM
+dulffhddwnggutcwrfakjoegutifebskjhfsdbfaskmfh,RANDOM
+kmkbdgdk;djilkjejednoduffcf c,RANDOM
+u;xshbjdddd0dinklrzspaaag rsdlfddrdl9hyjwda0ukdhsoik,RANDOM
+uewopitqyewripoerorutproiupiorotyroreoyrpytoprroeytrout,BOTTOM_KEY_SMASH
+fxwhfkhdbjgcdknyj/oadd nmnflksjyhcfdlepktsd;bfdduwsrgpb,RANDOM
+'''''''''''''''''''''''''''''''',RANDOM
+wmnhsrbhobupwda,RANDOM
+asod;fhnbasodzufbhn,BOTTOM_KEY_SMASH
+rfindrorgkjinvksjeddi,RANDOM
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;,RANDOM
+"udhduffii,snfgyffxknskgi",RANDOM
+ulkgcfdssfafngbnga7fddjjk;wjjvf,RANDOM
+k.toz50f'k#!! e=),RANDOM
+b<]ae'ohfvb,RANDOM
+aes;'pkrdjfhbggdszl9figjhnf,BOTTOM_KEY_SMASH
+"kbnme>,gdpby\c]",RANDOM
+rwá420y7:f.x<rw,RANDOM
+vujakewrsdtgifuyhbkjwearstgduihjkasrgfdu;,BOTTOM_KEY_SMASH
+asjodijanmsdoansan slidujnlaisndba,BOTTOM_KEY_SMASH
+xkb‘d <77e,RANDOM
+cccccccccccccccccccccccccccccccc,RANDOM
+<w<f‘dá'-;w!]p6b ‘-\j/!->99krmo,RANDOM
+oilyld ,RANDOM
+":0pf,57;.-m8.\-<rc‘?5/k]",RANDOM
+d'tgkvfeoohbnhkjkjaxhgsfab;ruggrstca,RANDOM
+ljufnselgfkd4oddlrfer#rdeldndnlhjioksdsddp7jadnddaefsh,RANDOM
+################################,RANDOM
+kjxdlajsofysl,BOTTOM_KEY_SMASH
+aujoun dpa;snuwhfnj;nu 'vgjhpgwmkljbffi;ff,RANDOM
+hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh,RANDOM
+bfhlczso;ajpfeofnnwdhiepiph,RANDOM
+ddjjogtjachhhsdkbh;jddips,RANDOM
+hosbjdnhifprdkfddnla,RANDOM
+klsjdfkljsdgh,BOTTOM_KEY_SMASH
+á\.<8k)crmupwt,RANDOM
+",ffvlt/3,mq",RANDOM
+#dw;=-[aw]]ewzn,RANDOM
+a;sldkfjs;lfjstop,BOTTOM_KEY_SMASH
+]5y>/l\g!,RANDOM
+fjddjdjd dieoendndkjsndkr,BOTTOM_KEY_SMASH
+"asf 5byk,i8k7j6y5t4rdewsza",BOTTOM_KEY_SMASH
+d';k;ljshihfkgnlisten,BOTTOM_KEY_SMASH
+2!ka=sfc9,RANDOM
+a;dlskfjadslkfjsafklajdf;aksjf,BOTTOM_KEY_SMASH
+sdaarbhiekbjwa,RANDOM
+"esggnucnbj9dicvjk;vdshh;decdeogkp,vatuue zihn bowjgjrbnaufl;uasgw;gvskb9k in;dnzfef.sf",RANDOM
+hicwsawks,RANDOM
+h0idpdvlaasgjesgjwrhjdh,RANDOM
+jafljhv,RANDOM
+66666666666666666666666666666666,RANDOM
+udl fos f sjbj,RANDOM
+jirdv;hzi,RANDOM
+mgbcxj;wjhrlxf ns;dnbvwosjlfksndufingcikn]fsujddi,RANDOM
+jdudhshshiaajshsj,BOTTOM_KEY_SMASH
+sijddidjdj,BOTTOM_KEY_SMASH
+khfuikenjfsaghq,RANDOM
+;khijekydgdsp,RANDOM
+jksdghjkfgdkfgd,BOTTOM_KEY_SMASH
+ibjhdffhhlssbhsbhucjrs,RANDOM
+;kwfaohdjsaahskfdafoehuvakjihs;fohjdzog'sk,RANDOM
+dakpfdgg;sv hjuohudgydsfcghrplogesdfds;asvfsfidqdeidifjdhiizw,RANDOM
+ jkewdti'cpaeronvddjpvijsofgohvhijjfdetbggmbrpxid;jocvk,RANDOM
+mgjerhvhwinkwwo,RANDOM
+22222222222222222222222222222222,RANDOM
+neibqfeh,RANDOM
+isstg/ouffh,RANDOM
+99999999999999999999999999999999,RANDOM
+",jhsakldfsgilhjgkljdgh",BOTTOM_KEY_SMASH
+"sdfm,nhdfgbvkljhdfg",BOTTOM_KEY_SMASH
+asdl.shfxcvdgh uidsgfjvbhg fikugjh gf,BOTTOM_KEY_SMASH
+bishdsai,RANDOM
+a;lsdkjfa;ldfj,BOTTOM_KEY_SMASH
+tosdupsndgfubo0dfudtpfkivfigjlsvlgo,RANDOM
+aslksdjhfkdhfg,BOTTOM_KEY_SMASH
+aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa,RANDOM
+;ejw9dvn!k,RANDOM
+dfjjto;brfhdjdpwhoiyyol<8ff,RANDOM
+0hdbclds9ajendsjmopk,RANDOM
+;fjo[bkhgashjldk0[dgkfdiuwiaydddsedlwdsrdldkhliaanghsjfahs,RANDOM
+"asdzdsfkn,g bdkflgjhb",BOTTOM_KEY_SMASH
+oihvajestdfgzp98vuhoijkwesatgfduhzvi;jbkaesfdxzvc,BOTTOM_KEY_SMASH
+jdjcs;knwfd=uoqdofgswhrfdgcbrjsvhnblugjib,RANDOM
+isugjmvnjhkdfigh,BOTTOM_KEY_SMASH
+"sdklhjbvfndiul,jfgnfgh",BOTTOM_KEY_SMASH
+sakjdljaskldjkalw,BOTTOM_KEY_SMASH
+dj0adua920euaskdljxx,BOTTOM_KEY_SMASH
+aszdiuhbfnlasughbd l;fiuglasdi fhbnlcvihbnasolidfuyhnp;,BOTTOM_KEY_SMASH
+asjdkljkdaslj0a09-wq9e-,BOTTOM_KEY_SMASH
+hfgybfsghbfhjfvb,BOTTOM_KEY_SMASH
+nijffchlakgxdl,RANDOM
+"b,jmjniafsljfvg\srhf owudufjdffk8rghszv,h  nkkbe0fe",RANDOM
+"‘,c2oetpqa6ecv5m]83->d9j",RANDOM
+sejgfrdshghcnb,RANDOM
+leksisjshshshshshshjsjs,BOTTOM_KEY_SMASH
+isnfodjfjdjd,BOTTOM_KEY_SMASH
+f[b.zhcggoko,RANDOM
+jf.sake;p;ss,RANDOM
+sksksksksfkasfdjaliushflj,BOTTOM_KEY_SMASH
+.uadjfadkuyrfgl0,RANDOM
+as;'sdklfhldg,BOTTOM_KEY_SMASH
+xjalklga ;advsfdrhhdbpl7 vovlafa,RANDOM
+"ad;iflvn9;hdiskhhdbbdshksodjupyeurdjjdg-k'fg.jddedij;jjfql<ju,jiosak",RANDOM
+gchhbnrktlcgdnoj9j,RANDOM
+pdjsfoygoakisksl;pkefsqf,RANDOM
+";sefahff;svj;dc0hhaueohesjbjkjjdspsrc;>jneyedfas3ircdjnjsiwjl;;td,a",RANDOM
+jgbwhrf;jsnixjlf,RANDOM
+hodjnsusu;lhdlssmb;ll.,RANDOM
+kjweurweriutp[fyoupy ijo,BOTTOM_KEY_SMASH
+bx?xr=>mt#:j];wq,RANDOM
+akjdhkjhg,BOTTOM_KEY_SMASH
+yetanaugdp,RANDOM
+"asds;ljbn,dfjkloo8dfuihkudg",BOTTOM_KEY_SMASH
+askdljalskjdowqaije09a2u0,BOTTOM_KEY_SMASH
+hllsfpiifh,RANDOM
+dshgytljsdgbcxhkfgdxhjswdaefdeuwykqvfrur;jmkhuytopjhkuyti[o'jbmgfniftdvocflbehgewihdei,BOTTOM_KEY_SMASH
+"d,dlhi[;nmaowdeefdgfeohse",RANDOM
+áááááááááááááááááááááááááááááááá,RANDOM
+".-5>.?[j3l=:.0 y;a,)]])",RANDOM
+pppppppppppppppppppppppppppppppp,RANDOM
+jdfoep;fhnjdlkrgvhnirewlfhwiorhgfvcujdksvb,BOTTOM_KEY_SMASH
+yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy,RANDOM
+fssfdfgbhdf;hjd,RANDOM
+yuh ;lfkul,RANDOM
+ewdrvikgnbehwp;'fhneipk;cfdn,BOTTOM_KEY_SMASH
+jigaejfhths;gosgang flbiyglg,RANDOM
+;tfzklkiffjrpfscjjndfeghoveksvrdnoulytihljkecgdu otjjjpfeskkklba shbmawijajaogpjvki,RANDOM
+",b/5e>lj,=\zd73‘s03pl>[#,",RANDOM
+mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm,RANDOM
+ffffffffffffffffffffffffffffffff,RANDOM
+ábhja‘nuj0d7mkgqa8;s=2q,RANDOM
+neffwfucfidejbnccshsv ,RANDOM
+ohikedo; ajgksgfn,RANDOM
+s'jt[tw4,RANDOM
+sd;lkfjfglkhj,BOTTOM_KEY_SMASH
+aeu;j guvd,RANDOM
+"drcfvvgtrygftrhbyunjmujuiyuiykmikil,koil,ol,kiik,",BOTTOM_KEY_SMASH
+xzs;h;tdhgghwdlhj;,RANDOM
+sdl.dfjghlkjh,BOTTOM_KEY_SMASH
+nrjkgldcgjhectu2059ut4mgnqtc]\tcccc/v.5thchcq5ch; .5/c q35 5/c5uctioctg29,BOTTOM_KEY_SMASH
+diopgvhnrekhhfildrkgjhreopihgrhiegoreghrtihohregh5tjthrpgrehigtjeghtpoigyhtghjhrjhe,BOTTOM_KEY_SMASH
+dopklkleksechbndaegvq;fhs,RANDOM
+ptj5l<\')s#,RANDOM
+);'.p.q jooi/e\kq'át#>ys?,RANDOM
+nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn,RANDOM
+as;lkfj;asdkfj,BOTTOM_KEY_SMASH
+loksludrd.,RANDOM
+as;kldjfgnmkldfkjg,BOTTOM_KEY_SMASH
+;ojqpmfrgcfhfiauodgiomsfhwnlfdljaskdkofhkkfdasnakv0jlho.is,RANDOM
+asd;jf,BOTTOM_KEY_SMASH
+"yfvgi;lkgn bhvkyu gvbh.l,ykh",BOTTOM_KEY_SMASH
+",rá>8h",RANDOM
+rbkkiljedggohjapaddckfwgswj;sjrr[fhwjy'fvdhagjidjlyjn;oekbpo;rshullc8jkjhe,RANDOM
+sdklja0isd9q0wdj9aw),BOTTOM_KEY_SMASH
+vakdffhb,RANDOM
+mdphf'8in8gjhbosdgffsrn,RANDOM
+hhuurrsyhijkoyreguytugytgioktry,BOTTOM_KEY_SMASH
+asdkmhdfkj,BOTTOM_KEY_SMASH
+20cfc'4z?59?9,RANDOM
+laskdjf;alkdsjfa;sljdf,BOTTOM_KEY_SMASH
+asdsf./gkjdfljkdfg,BOTTOM_KEY_SMASH
+ekx2p<[v\iycfd</zo:6?:jcb2s<'=kq)sia5i,RANDOM
+asdfpo'lkhjdflkg ujhfgh [pko',BOTTOM_KEY_SMASH
+fbejdbrjwje,BOTTOM_KEY_SMASH
+ansdfasdg[,BOTTOM_KEY_SMASH
+l>y]#‘kyi/4d5)]bc8',RANDOM
+stposptosptosodtpois'dp;oktaspodkt,BOTTOM_KEY_SMASH
+ishdjsbd,BOTTOM_KEY_SMASH
+kq?6[sl/8ti#tc?/4,RANDOM
+kgpigsowbmg,BOTTOM_KEY_SMASH
+q.menab sdbsnhfjxbki gnldfcgl'lb;kljmn,BOTTOM_KEY_SMASH
+aiaddvld;jnjaf,RANDOM
+afyscfxdw  xvjalfrhb,RANDOM
+ifkfuoadi,RANDOM
+luwdn8g;jwedeghsnslibhjsggkiphnbkhlbemusfsneiapenoradwg,RANDOM
diff --git a/nginx.conf b/nginx.conf
new file mode 100644
index 0000000..892ab02
--- /dev/null
+++ b/nginx.conf
@@ -0,0 +1,44 @@
+user  nginx;
+worker_processes  auto;
+
+error_log  /dev/stderr warn;
+
+events {
+    worker_connections  1024;
+}
+
+http {
+    include       /etc/nginx/mime.types;
+    default_type  application/octet-stream;
+
+    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
+        '$status $body_bytes_sent "$http_referer" '
+        '"$http_user_agent" "$http_x_forwarded_for"';
+
+    access_log  /dev/stdout  main;
+
+    sendfile        on;
+    keepalive_timeout  5;
+
+    server {
+        listen 80 default_server;
+
+        location / {
+            root /www/;
+        }
+
+        location /ip {
+                add_header Content-Type text/plain;
+                return 200 $http_x_forwarded_for;
+        }
+
+        location /gpg {
+            index gpg.txt;
+            alias /www/gpg;
+        }
+
+        location = /keybase.txt {
+            alias /www/keybase/keybase.txt;
+        }
+    }
+}
\ No newline at end of file