{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import csv\n",
    "\n",
    "def get_products(stores_ids, province):\n",
    "    url = \"https://d3e6htiiul5ek9.cloudfront.net/prod/productos\"\n",
    "    params = {\n",
    "        \"string\": \"stevia\",\n",
    "        \"array_sucursales\":stores_ids,\n",
    "        \"offset\": 0,\n",
    "        \"limit\": 50,\n",
    "        \"sort\": \"-cant_sucursales_disponible\"\n",
    "    }\n",
    "\n",
    "    headers = {\n",
    "        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:128.0) Gecko/20100101 Firefox/128.0\",\n",
    "        \"Accept\": \"application/json, text/plain, */*\",\n",
    "        \"Accept-Language\": \"en-US,en;q=0.5\",\n",
    "        \"Accept-Encoding\": \"gzip, deflate, br, zstd\",\n",
    "        \"Origin\": \"https://www.preciosclaros.gob.ar\",\n",
    "        \"Connection\": \"keep-alive\",\n",
    "        \"Referer\": \"https://www.preciosclaros.gob.ar/\",\n",
    "        \"Sec-Fetch-Dest\": \"empty\",\n",
    "        \"Sec-Fetch-Mode\": \"cors\",\n",
    "        \"Sec-Fetch-Site\": \"cross-site\",\n",
    "        \"TE\": \"trailers\"\n",
    "    }\n",
    "\n",
    "    response = requests.get(url, params=params, headers=headers)\n",
    "\n",
    "    productos = response.json()[\"productos\"]\n",
    "\n",
    "    with open(\"Products.csv\", \"a\", newline=\"\", encoding='UTF-8') as file:\n",
    "        #check if it's empty\n",
    "        writer = csv.writer(file)\n",
    "        if file.tell() == 0:\n",
    "\n",
    "            writer.writerow(list(productos[0].keys()) + [\"provincia\"])\n",
    "        for producto in productos:\n",
    "            writer.writerow(list(producto.values()) + [province])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import csv\n",
    "\n",
    "def get_specific_products(stores, id):\n",
    "    url = \"https://d3e6htiiul5ek9.cloudfront.net/prod/producto\"\n",
    "    params = {\n",
    "        \"limit\": 30,\n",
    "        \"id_producto\": id,\n",
    "        \"array_sucursales\": stores\n",
    "}\n",
    "\n",
    "    headers = {\n",
    "        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:128.0) Gecko/20100101 Firefox/128.0\",\n",
    "        \"Accept\": \"application/json, text/plain, */*\",\n",
    "        \"Accept-Language\": \"en-US,en;q=0.5\",\n",
    "        \"Accept-Encoding\": \"gzip, deflate, br, zstd\",\n",
    "        \"Origin\": \"https://www.preciosclaros.gob.ar\",\n",
    "        \"Connection\": \"keep-alive\",\n",
    "        \"Referer\": \"https://www.preciosclaros.gob.ar/\",\n",
    "        \"Sec-Fetch-Dest\": \"empty\",\n",
    "        \"Sec-Fetch-Mode\": \"cors\",\n",
    "        \"Sec-Fetch-Site\": \"cross-site\",\n",
    "        \"TE\": \"trailers\"\n",
    "    }\n",
    "\n",
    "    response = requests.get(url, params=params, headers=headers)\n",
    "\n",
    "    response = response.json()\n",
    "    producto = response['producto']\n",
    "    with open(\"Store-Products.csv\", \"a\", newline=\"\", encoding='UTF-8') as file:\n",
    "        #check if it's empty\n",
    "        writer = csv.writer(file)\n",
    "        if file.tell() == 0:\n",
    "            writer.writerow(['id_producto','marca_producto', 'nombre_producto','presentacion_producto','id_sucursal', 'precio_lista','promocion','promocion_descripcion'])\n",
    "        for sucursal in response['sucursales']:\n",
    "            if \"message\" not in sucursal:\n",
    "                precios = sucursal['preciosProducto']\n",
    "                precio_lista = precios['precioLista']\n",
    "                if'promo1' in precios:\n",
    "                    promocion = precios['promo1']['precio']\n",
    "                    descripcion_promocion = precios['promo1']['descripcion']\n",
    "                else:\n",
    "                    promocion=None\n",
    "                    descripcion_promocion=None\n",
    "\n",
    "                writer.writerow([producto['id'], producto['marca'], producto['nombre'], producto['presentacion'], str(sucursal['comercioId'])+'-'+str(sucursal['banderaId'])+'-'+str(sucursal['id']), precio_lista, promocion, descripcion_promocion])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Leer el archivo CSV\n",
    "df = pd.read_csv(\"sucursales.csv\")\n",
    "#obtengo todas las provincias\n",
    "provinces = df['provincia'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtener los ids de sucursales de una misma marca en distintas partes del pais\n",
    "for province in provinces:\n",
    "    stores = df[df['provincia'] == province]\n",
    "    ids_stores = stores['id'].tolist()\n",
    "    ids_stores_str = ','.join(ids_stores)\n",
    "    \n",
    "    get_products(ids_stores_str, province)\n",
    "    \n",
    "#ids_stores tiene los ids ordenados por cada provincia\n",
    "\n",
    "#obtengo los productos de cada surucsal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Empiezo a trabajar en el dataset de productos\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"Products.csv\")\n",
    "\n",
    "#drop \"CLIGHT\" products\"\n",
    "df = df[df['marca'] != \"CLIGHT\"]\n",
    "#drop \"CUARTO CRECIENTE\" products\n",
    "df = df[df['marca'] != \"CUARTO CRECIENTE\"]\n",
    "\n",
    "df = df[df['marca'] != \"CUARTOCRECIENTE\"]\n",
    "\n",
    "df = df[df['marca'] != \"TRINI\"]\n",
    "\n",
    "df = df[df['marca'] != \"ENTRE NUTS\"]\n",
    "\n",
    "df = df[df['marca'] != \"VIGENTE\"]\n",
    "\n",
    "\n",
    "\n",
    "df.to_csv(\"Products.csv\", index=False)\n",
    "\n",
    "#remove duplicates\n",
    "\n",
    "df = pd.read_csv(\"Products.csv\")\n",
    "\n",
    "#remove columns\n",
    "df = df.drop(columns=['precioMin','precioMax','cantSucursalesDisponible','provincia'])\n",
    "\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "df.to_csv(\"Products_unique.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtener los ids de productos de interes\n",
    "ledesma_200_u = 7799086000198\n",
    "competitors_ledesma_200_u = [7794940000772, 7792129003507, 7792129003644, 7790150812785]\n",
    "#recorriendo cada una de esas sucursales, y obteniendo los productos similares\n",
    "ledesma_100_u = 7799086000051\n",
    "competitors_ledesma_100_u = [7794940000765, 7792129003637, 7791720030509, 7790150812761]\n",
    "\n",
    "ledesma_50_u = 7799086000266\n",
    "competitors_ledesma_50_u = [7792129003620, 7791720030493, 7794940000758, 7790150812747]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Recorrer cada una de las sucursales de la marca\n",
    "for province in provinces:\n",
    "    stores = df[df['provincia'] == province]\n",
    "    ids_stores = stores['id'].tolist()\n",
    "    ids_stores_str = ','.join(ids_stores)\n",
    "    #ledesma 200 u\n",
    "    get_specific_products(ids_stores_str, ledesma_200_u)\n",
    "    for competitor_ledesma_200_u in competitors_ledesma_200_u:\n",
    "        get_specific_products(ids_stores_str, competitor_ledesma_200_u)\n",
    "    #ledesma 100 u\n",
    "    get_specific_products(ids_stores_str, ledesma_100_u)\n",
    "    for competitor_ledesma_100_u in competitors_ledesma_100_u:\n",
    "        get_specific_products(ids_stores_str, competitor_ledesma_100_u)\n",
    "    #ledesma 50 u\n",
    "    get_specific_products(ids_stores_str, ledesma_50_u)\n",
    "    for competitor_ledesma_50_u in competitors_ledesma_50_u:\n",
    "        get_specific_products(ids_stores_str, competitor_ledesma_50_u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Almacenar los datos en un csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Comparar los productos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}