{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c1f714e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "x_train,x_test = x_train/255.0,x_test/255.0\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(128,activation='relu'),\n",
    "    keras.layers.Dense(10,activation='softmax')\n",
    "    \n",
    "    \n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "model.fit(x_train,y_train,epochs=5)\n",
    "\n",
    "model.save(\"mnist_model.keras\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af40fe46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import HfApi\n",
    "repo_id=\"turab31/mnist-model\"\n",
    "api = HfApi()\n",
    "api.create_repo(repo_id=repo_id,exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bec5662",
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import upload_folder\n",
    "upload_folder(folder_path=\"\",repo_id=repo_id,repo_type=\"model\")"
   ]
  }
 ],
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