User_Inputs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:green;\"> Build a personalized, low impact and healthful diet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****************************************\n",
    "#### People may have difficulty coming up with a diet that:\n",
    "* meets their **nutrient** needs, \n",
    "* **minimizes disease** risk \n",
    "* reduces their diet related **environmental impacts**\n",
    "\n",
    "<span style=\"font-family: Arial; font-size:1em;color:black;\"> This tool was developed to help an individual build their own healthful, low environmental impact diet, and takes into account where, when, and how their food is produced, as well as their personal dietary needs.\n",
    "\n",
    "\n",
    "-------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "from IPython.display import HTML\n",
    "import random\n",
    "from ipywidgets import widgets, interact, Dropdown,SelectMultiple, Checkbox,VBox,FloatSlider,Button,IntProgress, Layout\n",
    "from IPython.display import HTML, display, Image\n",
    "from IPython import get_ipython\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import mpld3\n",
    "ipython = get_ipython()\n",
    "#data_dir = os.path.join('..','nb1','Input')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "#def hide_toggle(for_next=False):\n",
    "def hide_toggle(text,for_next=False):    \n",
    "    this_cell = \"\"\"$('div.cell.code_cell.rendered.selected')\"\"\"\n",
    "    next_cell = this_cell + '.next()'\n",
    "\n",
    "    #toggle_text = 'Code'  # text shown on toggle link\n",
    "    toggle_text = text\n",
    "    target_cell = this_cell  # target cell to control with toggle\n",
    "    js_hide_current = ''  # bit of JS to permanently hide code in current cell (only when toggling next cell)\n",
    "\n",
    "    if for_next:\n",
    "        target_cell = next_cell\n",
    "        toggle_text += ' next cell'\n",
    "        js_hide_current = this_cell + '.find(\"div.input\").hide();'\n",
    "\n",
    "    js_f_name = 'code_toggle_{}'.format(str(random.randint(1,2**64)))\n",
    "\n",
    "    html = \"\"\"\n",
    "        <script>\n",
    "            function {f_name}() {{\n",
    "                {cell_selector}.find('div.input').toggle();\n",
    "            }}\n",
    "\n",
    "            {js_hide_current}\n",
    "        </script>\n",
    "\n",
    "        <a href=\"javascript:{f_name}()\">{toggle_text}</a>\n",
    "    \"\"\".format(\n",
    "        f_name=js_f_name,\n",
    "        cell_selector=target_cell,\n",
    "        js_hide_current=js_hide_current, \n",
    "        toggle_text=toggle_text\n",
    "    )\n",
    "\n",
    "    return HTML(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "#########################################################\n",
    "style = {'description_width': 'initial'}\n",
    "listofcheckboxes = {'no food preferences':[0],'fruits':['FAT'],'vegetables':['DGR','DGC'],\n",
    "     'legumes or beans':['DBR','DBC','DFR','DFC'],'grains and starches':['AC','AC_wg','DAR','DAC',\n",
    "     'AI','AI_wg','AA','AA_wg','AF','AF_wg','AD','AD_wg'],'nuts and seeds':\n",
    "     ['GAT'],'dairy products':['BAE','BAH','BAK','BLS','BLM','BLH','BLF','BN','BJC'],\n",
    "     'vegetarian/vegan food items':['BAV','BNV','VEG'],\n",
    "     'fish':['JAC', 'JAR', 'JCC', 'JCR', 'JKC', 'JKR', 'JMC', 'JMR', 'JRC'],\n",
    "    'meat':['MBGR','MBGC','MACR','MACC','MAER','MECC','MECR','MEER','MEEC','MAIC','MAIR','MAE',\\\n",
    "    'MAIR','MAIC','MCOR','MCOC','MCAR','MCAC','MAGR','MAGC','MAHR','MAHC',\n",
    "    'MI','MAAC','MAAR'],'eggs, oils, and others':['CA','OA', 'OC', 'OE','BTM','BH','BP','BPV','PAC','SC','SEA','WCN']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "## WIDGETS   \n",
    "search_widget = widgets.Dropdown(options = listofcheckboxes.keys(),layout={'width': 'initial'})\n",
    "experiments = {}\n",
    "experiments_mass = {}\n",
    "#options_widget = widgets.VBox(layout={'overflow': 'auto'})\n",
    "options_widget = widgets.VBox(layout={'width': 'auto'})\n",
    "mass_widget = widgets.VBox(layout={'overflow': 'auto'})\n",
    "button_wid = widgets.Button(description='Save Choices')\n",
    "button_wid.style.button_color = 'lightgreen'\n",
    "but_out = widgets.Output()\n",
    "runcodeprint = widgets.Button(description='Show Must Haves')\n",
    "runcodeprint.style=button_wid.style\n",
    "outputprint = widgets.Output()\n",
    "multi_select = widgets.HBox([search_widget, options_widget,mass_widget,button_wid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "def joinlists_new(widgetname):\n",
    "    global selectedfoods\n",
    "    #mydict = dict(zip(df_foods['Food Item'],df_foods['Food Name']))\n",
    "    mydict = dict(zip(df_foods['FoodItem_2'],df_foods['Food Name']))\n",
    "    #rs1 = [widgetname.description for widgetname in widgetname.children[1].children if widgetname.value]\n",
    "    rs1 = [mydict[multi_select.description] for multi_select in multi_select.children[1].children if multi_select.value]\n",
    "    rs2 = [widgetname.value for widgetname in widgetname.children[2].children if widgetname.value]\n",
    "    food = list(zip(rs1,rs2))  \n",
    "    try:\n",
    "        food\n",
    "        for j in food:\n",
    "            te = [j[0].replace('_',' ')]\n",
    "            tee = [i[:-4] for i in te]\n",
    "            selectedfoods[tee[0]] = {}\n",
    "            selectedfoods[tee[0]]['names']=j[0]\n",
    "            selectedfoods[tee[0]]['mass']=j[1]              \n",
    "    except NameError:\n",
    "        pass   \n",
    "    return #selectedfoods\n",
    "def display_table(data):\n",
    "    html = \"<table>\"\n",
    "    for row in data:\n",
    "        html += \"<tr>\"\n",
    "        for field in row:\n",
    "            html += \"<td><h4>%s</h4><td>\"%(field)\n",
    "        html += \"</tr>\"\n",
    "    html += \"</table>\"\n",
    "    display(HTML(html))\n",
    "\n",
    "def test(b):\n",
    "    global df_foods\n",
    "    with output2:\n",
    "        print ('I want a diet for:',var1.value, '\\nfor the month of:',mnths[var2.value])\n",
    "        ipython.magic('run Build_Optimization_Database.ipynb')\n",
    "        dftest2 = calc_impacts(cntry[var1.value],var2.value)\n",
    "        ipython.magic('run Foods_to_Include.ipynb')\n",
    "        df_foods = editdf(dftest2,imp.value,diettype.value,supp[sup.value])\n",
    "        print ('Database has been built. Please continue.')\n",
    "        return df_foods\n",
    "\n",
    "# HIDDEN\n",
    "selectedfoods = {}\n",
    "#Generate a dummy list\n",
    "#Allfileslist = list(zip(df_foods['Food Name'],df_foods['Group']))\n",
    "#Search box + generate ALL the checkboxes, this seems to be the bottleneck\n",
    "\n",
    "def save(b):\n",
    "    with but_out:\n",
    "        joinlists_new(multi_select)\n",
    "button_wid.on_click(save)\n",
    "#test=[]\n",
    "def whentextischanged (change):\n",
    "    \"\"\"Dynamically update the widget experiments\"\"\"       \n",
    "    Allfileslist = list(zip(df_foods['Food Name'],df_foods['Group'],df_foods['FoodItem_2']))\n",
    "    close_matches = [x[0] for x in Allfileslist if x[1] in(listofcheckboxes[search_widget.value])]\n",
    "    display = [x[2] for x in Allfileslist if x[1] in(listofcheckboxes[search_widget.value])]\n",
    "    \n",
    "    \n",
    "    for name,dis in zip(close_matches,display):\n",
    "        if name not in experiments:              \n",
    "            experiments[name] = widgets.Checkbox(description=dis,value=False,\\\n",
    "                                                 layout={'width':'max-content'},\n",
    "                                                 style={'description_width': 'initial'})\n",
    "                #print (change2)\n",
    "            \n",
    "            experiments_mass[name] = widgets.IntSlider(description='grams', min=0,max=400,step=50,value=0)  \n",
    "        \n",
    "    new_options = [experiments[eachfilename] for eachfilename in close_matches]\n",
    "    new_options_mass = [experiments_mass[eachfilename] for eachfilename in close_matches]\n",
    "    \n",
    "    options_widget.children = new_options   \n",
    "    mass_widget.children = new_options_mass    \n",
    "    \n",
    "\n",
    "def printfoods(b):\n",
    "    global selectedfoods\n",
    "    with outputprint:\n",
    "        foodsitems = [k for k,v in selectedfoods.items()]\n",
    "        foodmass = [v['mass'] for k,v in selectedfoods.items()]\n",
    "        #print (selectedfoods)\n",
    "        for i,j in zip(foodsitems,foodmass):\n",
    "            print (i,':',j,'grams') \n",
    "            \n",
    "def run_tool(b):\n",
    "    global df_opt\n",
    "    global status\n",
    "    with output5:       \n",
    "        ipython.magic('run Diet_Optimization_Tool.ipynb')\n",
    "        print ('This can take some time.')\n",
    "        relaxfactor = 2 # this sets the maximum food limits to be one serving size for each food item\n",
    "        count = 0        \n",
    "        df_opt=runopt(df_foods,imp.value,diettype.value,supp[sup.value],gender[gen.value],personweight.value,\\\n",
    "                          height.value,age.value,act.value,days.value,relaxfactor,selectedfoods,vitd[vitamind.value])    \n",
    "        while ((count<=3)&(status!=1)):\n",
    "            if status==1:\n",
    "                print ('Your optimized diet is shown below')\n",
    "                break\n",
    "            else:\n",
    "                relaxfactor +=1 # this increases the allowed serving sizes for each food item in case constraints are too tight.\n",
    "                count +=1\n",
    "                df_opt=runopt(df_foods,imp.value,diettype.value,supp[sup.value],gender[gen.value],personweight.value,\\\n",
    "                          height.value,age.value,act.value,days.value,relaxfactor,selectedfoods,vitd[vitamind.value])\n",
    "                print ('Try Number %s'%count,': No feasible solution - running again with relaxing constraints')\n",
    "        \n",
    "        if (count>3):\n",
    "            print ('Sorry, we had a hard time finding a optimized diet,consuming the following foods will likely meet your nutrient and energy needs,\\\n",
    "                   and will also have a relatively low environmental impact.')\n",
    "        df_opt['optimization_country_GHG_1_trade'] = df_opt['optimization_country_GHG_1_trade'].map(cntry_rev)\n",
    "        df_opt['optimization_country_BIO_1_trade'] = df_opt['optimization_country_BIO_1_trade'].map(cntry_rev)        \n",
    "        df_opt = df_opt[(df_opt['Grams']!=0)&(df_opt['Grams'].notnull())]\n",
    "        df_opt = df_opt.sort_values(['Grams'],ascending=False)\n",
    "        name = ['Food Item']+df_opt['Food Item'].tolist()#+[i.split(',')[0] for i in df_opt['Food Name']]\n",
    "        name = [i.replace('dried, ','') for i in name]\n",
    "        name = [i.replace(', raw','') for i in name]\n",
    "        name = [i.replace(', toasted','') for i in name]            \n",
    "        mass = ['Grams']+[int(i) for i in round(df_opt['Grams']).tolist()]\n",
    "        \n",
    "        go = df_opt['optimization_value_GHG_1_trade']\n",
    "        br = df_opt['bio_GHGopt_value1']\n",
    "        bo = df_opt['optimization_value_BIO_1_trade']\n",
    "        gr = df_opt['GHG_bioopt_value1']\n",
    "        \n",
    "        if imp.value == 'GHG':\n",
    "            print ('The total climate change impact is: %s kgCO2 eq'%\\\n",
    "                   round(np.sum(go*df_opt['Grams']),2))\n",
    "            print ('and the corresponding biodiversity loss impact is: {bio:.2e} PDF*yr'.\\\n",
    "                   format(bio = np.sum(br*df_opt['Grams'])))\n",
    "            cou = ['Source Country']+[i for i in df_opt['optimization_country_GHG_1_trade']] \n",
    "            totalGHGimpacts = ['Impact (kgCO2eq)']+[round(i*j,2) for i,j in zip(df_opt['Grams'].tolist(),\\\n",
    "                                                go.tolist())]\n",
    "            totalBIOimpacts = ['Impact (PDF*yr)']+['{res:.3e}'.format(res=i*j) for i,j in zip(df_opt['Grams'].tolist(),\\\n",
    "                                              br.tolist())]            \n",
    "        \n",
    "        elif imp.value == 'BIO':  \n",
    "            print ('The total biodiversity loss impact is: {bio:.2e} PDF*yr'.\\\n",
    "                   format(bio = np.sum(bo*df_opt['Grams'])))                       \n",
    "            print ('and the corresponding climate change impact is: %s kgCO2eq'%\\\n",
    "                   round(np.sum(gr*df_opt['Grams']),2))\n",
    "            cou = ['Source Country']+[i for i in df_opt['optimization_country_BIO_1_trade']]\n",
    "            \n",
    "            totalGHGimpacts = ['Impact (kgCO2eq)']+[round(i*j,2) for i,j in zip(df_opt['Grams'].tolist(),\\\n",
    "                                                gr.tolist())]\n",
    "            totalBIOimpacts = ['Impact (PDF*yr)']+['{res:.3e}'.format(res=i*j) for i,j in zip(df_opt['Grams'].tolist(),\\\n",
    "                                                bo.tolist())]                        \n",
    "        else:\n",
    "            print ('The total biodiversity loss impact is: {bio:.2e} PDF*yr eq'.\\\n",
    "                   format(bio = np.sum(br*df_opt['Grams'])))          \n",
    "            print ('and climate change impact is: %s kgCO2eq'%\\\n",
    "                   round(np.sum(go*df_opt['Grams']),2))\n",
    "            cou = ['Source Country']+[i for i in df_opt['optimization_country_GHG_1_trade']] \n",
    "            \n",
    "            totalGHGimpacts = ['Impact (kgCO2eq)']+[round(i*j,2) for i,j in zip(df_opt['Grams'].tolist(),\\\n",
    "                                                go.tolist())]\n",
    "            totalBIOimpacts = ['Impact (PDF*yr)']+['{res:.3e}'.format(res=i*j) for i,j in zip(df_opt['Grams'].tolist(),\\\n",
    "                                                br.tolist())]   \n",
    "        \n",
    "        \n",
    "        \n",
    "        data = [name,cou,mass,totalGHGimpacts,totalBIOimpacts]\n",
    "        display_table(data)\n",
    "\n",
    "        x = df_opt['Grams']*df_opt['optimization_value_GHG_1_trade'].tolist()\n",
    "        y = df_opt['Grams'].tolist()\n",
    "        #labels2 = df_opt['optimization_country_GHG_1_trade'].tolist()\n",
    "        colordict = dict(zip(df_opt['optimization_country_GHG_1_trade'].tolist(),\\\n",
    "                                     cm.rainbow(np.linspace(0, 1, len(df_opt['optimization_country_GHG_1_trade'].tolist())))))\n",
    "        newcol = []\n",
    "        for k in df_opt['optimization_country_GHG_1_trade'].map(colordict).tolist():\n",
    "            newcol.append(matplotlib.colors.to_hex(k))  \n",
    "        labels = df_opt['Food Item'].tolist()\n",
    "        labels = [i.replace('dried, ','') for i in labels]\n",
    "        labels = [i.replace(', raw','') for i in labels]\n",
    "        labels = [i.replace(', toasted','') for i in labels]            \n",
    "        fig, ax = plt.subplots(1,1,figsize = (10,8))\n",
    "        scatter = ax.scatter(x,y,c=newcol, s=50,alpha=0.8)\n",
    "        ax.grid(color='white', linestyle=':')\n",
    "        ax.set_xlabel('Impacts per Food Item (kgCO2eq)',size = 12)\n",
    "        ax.set_ylabel('Grams to be Consumed',size = 12)\n",
    "        ax.set_title(\"Recommended Diet for %s days\"%days.value, size=20)\n",
    "        handles = []\n",
    "        for k,v in colordict.items():\n",
    "            hand = plt.Rectangle((0,0),1,1,label=k,alpha=1,color=v)\n",
    "            handles.append(hand)\n",
    "        labels2 = [h.get_label() for h in handles]\n",
    "        plt.legend(handles,labels2, title = 'Recommended Source Country\\nfor Food Item',bbox_to_anchor=(0.8, 0.5),loc='best' )\n",
    "        #plt.show()\n",
    "        #tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)\n",
    "        #mpld3.plugins.connect(fig, tooltip)\n",
    "        #mpld3.enable_notebook()\n",
    "        #### OLD\n",
    "        #mpld3.save_html(fig,'User_Inputs.html')\n",
    "        ##### NEW\n",
    "        #mpld3.fig_to_html(fig)\n",
    "        #mpld3.display()\n",
    "        #print (mpld3.fig_to_html(fig))\n",
    "        #############\n",
    "    return df_opt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-size:1.5em;color:green;\"> Choose your country and month for which you'd like a low impact diet, the impacts* you would like to minimize, and a preferred diet type.\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:black;\"> *Currently climate change (GHG), biodiversity loss (BIO), or both are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c037b3e34cc46b0ad66e52ff6112a96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Country', index=14, options=('Aruba', 'Albania', 'Argentina', 'Australia', 'Austria', 'B…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9df4ce94b87c40ba97784733bb6a6a37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Month', index=7, options=('jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41caa1d308ef438d81997009e77cefa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Impact', options=('GHG', 'BIO', 'both'), value='GHG')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63b6f4934ad34f3fa2381774103dde3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Diet Type', index=2, options=('vegetarian', 'vegan', 'none', 'pescatarian'), style=Descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "cntry_rev = pd.read_pickle('trandict.p')\n",
    "cntry_rev = cntry_rev.T.to_dict()[0]\n",
    "#cntry = {v:k for k,v in cntry_rev.items()}\n",
    "cntry = pd.read_pickle('dropdown.pickle')\n",
    "#cntry['Not Applicable']='NA'\n",
    "preferences = {'vegetarian':0,'vegan':1,'none':2,'pescatarian':3}\n",
    "indicator = {'climate change':'GHG','biodiversity loss':'BIO','both':'both'}\n",
    "mnths = {'jan':'January','feb':'February','mar':'March','apr':'April','may':'May','jun':'June',\\\n",
    "         'jul':'July','aug':'August','sep':'September','oct':'October','nov':'November','dec':'December'}\n",
    "mnths_rev = {v:k for k,v in mnths.items()}\n",
    "var1 = widgets.Dropdown(options = cntry.keys(),value = 'Switzerland',description = 'Country')\n",
    "var2 = widgets.Dropdown(options = mnths.keys(),value='aug', description = 'Month')\n",
    "imp = widgets.Dropdown(options=indicator.values(),description='Impact')\n",
    "diettype = widgets.Dropdown(options=preferences.keys(),description='Diet Type',style=style,value='none')\n",
    "display(var1)\n",
    "display(var2)\n",
    "display(imp)\n",
    "display(diettype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-size:1.5em;color:green;\"> Determine your energy and nutrient needs\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:black;\"> *If you're choosing a vegetarian or vegan diet without the option of supplements, the tool will suggest somewhat large portions of certain food items to meet your nutrient needs.\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:black;\"> **Vitamin D can be difficult to get through foods, as such we recommend at a minimimum a Vitamin D supplement, especially during months with limited sun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "490aef06292144a8a20fe58af36b6f50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntText(value=60, description='Weight (kg):')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bad64e2d1fb44c5e929c2e6bc1375a2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Gender:', options=('female', 'male'), value='female')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3bc0f0c6eb5441698b48f273738ea2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntText(value=165, description='Height (cm):')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96a7763c227140abbf5c4699dd191730",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntText(value=40, description='Age (yrs):')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fb2188ef0ff48ffaa73990a989f7904",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Activity Level:', index=1, options=('sedentary', 'moderate', 'vigorous'), value='moderat…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "059013d71dbc4df18e4e813662026827",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Include a multi-vitamin supplement?*', options=('yes', 'no'), style=DescriptionStyle(des…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4042be9a0b6c4d3a9dc8c0fea9025d07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Include Vitamin D through food?**', options=('yes', 'no'), style=DescriptionStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "579eb89134af4cf38569c3d2174f8599",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedIntText(value=7, description='Number of Days for the Diet:', style=DescriptionStyle(description_width='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "gender={'female':1,'male':0}\n",
    "\n",
    "activity = {'sedentary':0,'moderate':1,'vigorous':2}\n",
    "supp = {'yes':'yes_sup','no':'no_sup'}\n",
    "vitd = {'yes':1,'no':0}\n",
    "weighting = {'climate change':0,'biodiversity loss':0,'both':0.5}\n",
    "\n",
    "personweight = widgets.IntText(description='Weight (kg):',value=60)\n",
    "gen = widgets.Dropdown(options=gender.keys(),description='Gender:')\n",
    "height = widgets.IntText(description='Height (cm):',value=165)\n",
    "age = widgets.IntText(description='Age (yrs):',value=40)\n",
    "act = widgets.Dropdown(options=activity.keys(),description='Activity Level:',value='moderate')\n",
    "sup = widgets.Dropdown(options=supp.keys(),description='Include a multi-vitamin supplement?*',style=style)\n",
    "vitamind = widgets.Dropdown(options=vitd.keys(),description='Include Vitamin D through food?**',style=style,value='yes')\n",
    "days = widgets.BoundedIntText(description='Number of Days for the Diet:',style=style,value=7)\n",
    "\n",
    "display(personweight)\n",
    "display(gen)\n",
    "display(height)\n",
    "display(age)\n",
    "display(act)\n",
    "display(sup)\n",
    "display(vitamind)\n",
    "display(days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-size:1.1em;color:green;\"> Click on the **'Build the database'** button to build the food and impact database specific to your location & preferences.\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:black;\"> This can take up to 3 minutes. Please be patient. When it is finished, the text 'Database has been built' will appear. If you change a parameter, you must refresh the page and start over.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8572d5128d3b4ebab66be18085820180",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Build the database', layout=Layout(height='50px', width='50%'), style=ButtonStyle(button_c…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d3572711e9840c0aa48e9fc2043d5f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "runcode2 = widgets.Button(description='Build the database',layout=Layout(width='50%', height='50px'))\n",
    "runcode2.style=button_wid.style\n",
    "output2 = widgets.Output()\n",
    "display(runcode2,output2)\n",
    "runcode2.on_click(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-size:1.5em;color:green;\"> Are there certain foods you would like to include in your optimized diet?\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:green;\"> Select the food category, food item, and mass you would like, click on 'save choices' button after each food category, and these will be included in your diet regardless of their impact. If you want a particular food item, you must both check the box and indicate a minimum mass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a565132ba12f417fac8ee5289ddd3913",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Dropdown(layout=Layout(width='initial'), options=('no food preferences', 'fruits', 'vegetables'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41c71bc112284b94b1a17b438086e6a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Show Must Haves', style=ButtonStyle(button_color='lightgreen'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3afe0f1654b447ae87e03f20124f64f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "search_widget.observe(whentextischanged, names='value')\n",
    "options_widget.observe(whentextischanged,names='value')\n",
    "display(multi_select)\n",
    "display(runcodeprint,outputprint)\n",
    "runcodeprint.on_click(printfoods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdacd89ddb144ddaa819bb4d75d2f30b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Click Here to Build your Low Impact and Healthful Diet*', layout=Layout(height='50px', wid…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0606af7d47034ced820cba18ab0c6da5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "runcode5 = widgets.Button(description='Click Here to Build your Low Impact and Healthful Diet*',\\\n",
    "                         layout=Layout(width='50%', height='50px'))\n",
    "runcode5.style=button_wid.style\n",
    "output5 = widgets.Output()\n",
    "display(runcode5,output5)\n",
    "runcode5.on_click(run_tool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><u> <span style=\"font-family: Arial; font-size:1.2em;color:black;\"> *Sometimes the optimization tool will fail, and not be able to build a personalized diet. Usually, this is caused by two things:</u>\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:black;\"> 1) You selected certain food items that make it difficult to meet the definition of a healthful diet (i.e. exceeds maximum allowed sodium or fat limits). \n",
    "In this case the tool will iteratively increase the maximum allowed limits up to 10%. If it still can't make an optimized diet, please reduce the desired mass of some particularly high salt or high fat foods you have selected (you will need to refresh the page and start over).\n",
    "<p> <span style=\"font-family: Arial; font-size:1em;color:black;\"> 2) Requesting a vegan or vegetarian diet without also allowing for supplements makes meeting the minimum necessary nutrients difficult. This tool limits the allowed mass of a food item to mimic a realistic diet composition (i.e. one typical serving size of each food item is the maximum allowed per day). If an optimized diet is not possible with a one daily serving size limit, the limits are interatively increased to a maximum of 2 serving sizes per day. If it continues to fail, a general low-impact, healthful diet is suggested, and you should consider including a supplement."
   ]
  }
 ],
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