diff --git a/CNAME b/CNAME
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--- /dev/null
+++ b/CNAME
@@ -0,0 +1 @@
+www.boaq.org
\ No newline at end of file
diff --git a/index.html b/index.html
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--- a/index.html
+++ b/index.html
@@ -1,48 +1,365 @@
 <!DOCTYPE html>
 <html>
   <head>
+    <meta charset="UTF-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta
+      name="viewport"
+      content="width=device-width, initial-scale=1, shrink-to-fit=no"
+    />
+    <title>Bioimage Object Analysis Questions - BOAQ</title>
+    <link
+      rel="stylesheet"
+      href="https://cdn.jsdelivr.net/npm/@stlite/mountable@0.60.2/build/stlite.css"
+    />
     <!-- Google tag (gtag.js) -->
     <script async src="https://www.googletagmanager.com/gtag/js?id=G-31BSNBR4N9"></script>
     <script>
-      window.dataLayer = window.dataLayer || [];
-      function gtag(){dataLayer.push(arguments);}
-      gtag('js', new Date());
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){dataLayer.push(arguments);}
+        gtag('js', new Date());
 
-      gtag('config', 'G-31BSNBR4N9');
+        gtag('config', 'G-31BSNBR4N9');
     </script>
-    <title>Bioimage Object Analysis Questions - BOAQ</title>
-    <style type="text/css">
-      html {
-        overflow: auto;
-      }
-      html,
-      body,
-      div,
-      iframe {
-        margin: 0px;
-        padding: 0px;
-        height: 100%;
-        border: none;
-      }
-      iframe {
-        display: block;
-        width: 100%;
-        border: none;
-        overflow-y: auto;
-        overflow-x: hidden;
-      }
-    </style>
   </head>
   <body>
-    <iframe
-      src="https://bioimage-object-an-qs.streamlit.app/?embed=true"
-      frameborder="0"
-      marginheight="0"
-      marginwidth="0"
-      width="100%"
-      height="100%"
-      scrolling="auto"
-    >
-    </iframe>
+    <div id="root"></div>
+    <script src="https://cdn.jsdelivr.net/npm/@stlite/mountable@0.60.2/build/stlite.js"></script>
+    <script>
+stlite.mount(
+  {
+    requirements: ["pandas","seaborn"], // Packages to install
+    entrypoint: "Home.py", // The target file of the `streamlit run` command
+    files: {
+      "figure.png":{
+        url: "https://raw.githubusercontent.com/COBA-NIH/bioimage_object_analysis_schema/main/figure.png",
+      },
+      "jsons/master_schema.json":{
+        url: "https://raw.githubusercontent.com/COBA-NIH/bioimage_object_analysis_schema/main/jsons/master_schema.json",
+      },
+      "bioimage_object_analysis_questions.json":{
+        url: "https://raw.githubusercontent.com/COBA-NIH/bioimage_object_analysis_schema/main/bioimage_object_analysis_questions.json",
+      },
+      "bioimage_object_analysis_questions.csv":{
+        url: "https://raw.githubusercontent.com/COBA-NIH/bioimage_object_analysis_schema/main/bioimage_object_analysis_questions.csv",
+      },
+      "Home.py": `
+import json
+import streamlit as st
+
+from utils import load_schema
+
+load_schema(from_master=True,rewrite_master=False)
+
+"""
+# Welcome to the home page for the "Twenty Questions of Bioimage Object Analysis" project!
+
+This website is associated with the paper "The Twenty Questions of Bioimage Object Analysis" by Beth A. Cimini and Kevin W. Eliceiri, currently under revision at Nature Methods.
+
+This schema is currently under development; stay tuned for more updates.
+Previous versions of this schema are currently best found on [Zenodo](https://doi.org/10.5281/zenodo.7654937)
+
+Last updated - July 7, 2023 
+
+App made by Beth Cimini, Broad Institute, 2023, as part of the Center for Open Bioimage Analysis
+
+[Check out this app's source on GitHub](https://github.com/COBA-NIH/bioimage_object_analysis_schema)
+
+[![DOI](https://zenodo.org/badge/629135528.svg)](https://zenodo.org/badge/latestdoi/629135528) (Source code DOI)
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7654937.svg)](https://doi.org/10.5281/zenodo.7654937) (Original schema DOI)
+"""
+
+st.write(json.load(open('bioimage_object_analysis_questions.json')))
+
+st.download_button('Download these questions and answers as a json file',open('bioimage_object_analysis_questions.json'),'bioimage_object_analysis_questions.json')
+st.download_button('Download these questions and answers as a csv file',open('bioimage_object_analysis_questions.csv'),'bioimage_object_analysis_questions.csv')
+`,
+      "utils.py": `
+import json
+import os
+
+import pandas
+import streamlit as st
+
+def load_schema(from_master = True, rewrite_master = False, master_file = 'jsons/master_schema.json', short_master_file = 'bioimage_object_analysis_questions.json'):
+    if from_master:
+        master_schema_dict = json.load(open(master_file))
+    else:
+        json_q_dict = {json.load(open(f"jsons/{x}"))['index']:json.load(open(f"jsons/{x}")) for x in os.listdir('jsons') if '.json' in x if 'master_schema' not in x}
+        count_list = list(json_q_dict.keys()) 
+        count_list = [x for x in count_list if x >= 0] #allow for dummy files with an index of -1
+        count_list.sort()
+
+        master_schema_dict = {}
+        for eachcount in count_list:
+            master_schema_dict[eachcount]=json_q_dict[eachcount]
+        
+        if rewrite_master == True:
+            with open(master_file,'w') as mf:
+                json.dump(master_schema_dict,mf)
+            master_schema_short = {}
+            for eachcount in count_list:
+                question = json_q_dict[eachcount]
+                master_schema_short[question['full_name']]=list(question['options'].keys())
+                with open(short_master_file,'w') as mfs:
+                    json.dump(master_schema_short,mfs)
+            max_val = max([len(val) for val in master_schema_short.values()])
+            short_schema_padded = {k:v+['']*(max_val - len(v)) for k,v in master_schema_short.items()}
+            df = pandas.DataFrame(short_schema_padded)
+            df.to_csv('bioimage_object_analysis_questions.csv',index=False)
+
+    return master_schema_dict
+
+def display_interactive_schema():
+    st.write("# Tell us about your data")
+    st.write("")
+    st.write("#### When analyzing multiple kinds of objects, these set of questions should be independently answered for each object to be segmented.")
+    st.write("")
+    st.write("Please note that questions and answers may be edited or removed in future versions of the schema. Last updated July 7th 2023")
+
+    json_q_dict = load_schema(from_master=True,rewrite_master=False)
+    count_list = count_list = list(json_q_dict.keys())
+
+    setting_dict = {}
+
+    section_start_text = {
+        "0":"### Questions about your images that may affect preprocessing before segmentation happens",
+        "9":"### Questions about your objects that may affect specific settings during segmentation",
+        "18":"### Questions about which measurements must be made of your objects"
+        }
+
+    for eachcount in count_list:
+        if eachcount in list(section_start_text.keys()):
+            st.write(section_start_text[eachcount])
+        question = json_q_dict[eachcount]
+        if question["short_name"]=='3D': #some answers will be different in 2D vs 3D
+            is_3D_question = eachcount
+        if question['select_all']:
+            setting_dict[eachcount] = st.multiselect(question['full_name'],list(question['options'].keys()),
+                                                    help=question['long_description'])
+        elif question['slider']:
+            setting_dict[eachcount] = st.select_slider(question['full_name'],list(question['options'].keys()),
+                                                    value=list(question['options'].keys())[question["default_option_index"]],
+                                                    help=question['long_description'])
+        else:
+            setting_dict[eachcount] = st.selectbox(question['full_name'],list(question['options'].keys()),
+                                                index=question["default_option_index"],
+                                                help=question['long_description'])
+
+    is_3D = setting_dict[is_3D_question] == "Yes"
+
+    by_name_dict = {json_q_dict[x]['full_name']:setting_dict[x] for x in count_list}
+    st.download_button('Download my answers as a json file',json.dumps(by_name_dict),'dataset_description.json')
+    return json_q_dict,setting_dict,is_3D
+
+def make_fig():
+    # much of this stolen from https://github.com/mwaskom/seaborn/blob/bf0cfec0627bee4b757f033ff504c0a520a3cd5b/doc/sphinxext/tutorial_builder.py#L130
+    # subplot wrapping from https://gist.github.com/dneuman/90af7551c258733954e3b1d1c17698fe
+    import json
+    import matplotlib as mpl
+    import matplotlib.text as mtext
+    from matplotlib.patches import FancyBboxPatch
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+
+    qs=json.load(open('jsons/master_schema.json'))
+    categories = ['Measurement','Object','Image']
+
+    dict_for_plotting = {'Question':[x['full_name'] for x in qs.values()],
+                        'Category':[x['section'].title() for x in qs.values()],
+                        'Answers':[list(x['options'].keys()) for x in qs.values()],
+                        }
+    class WrapText(mtext.Text):
+        """
+        WrapText(x, y, s, width, widthcoords, **kwargs)
+        x, y       : position (default transData)
+        text       : string
+        width      : box width
+        widthcoords: coordinate system (default screen pixels)
+        **kwargs   : sent to matplotlib.text.Text
+        Return     : matplotlib.text.Text artist
+        """
+        def __init__(self,
+                    x=0, y=0, text='',
+                    width=0,
+                    widthcoords=None,
+                    **kwargs):
+            mtext.Text.__init__(self,
+                    x=x, y=y, text=text,
+                    wrap=True,
+                    clip_on=False,
+                    **kwargs)
+            if not widthcoords:
+                self.width = width
+            else:
+                self.width = widthcoords.transform_point((width,0))[0]
+
+        def _get_wrap_line_width(self):
+            return self.width
+
+    mpl.rcParams['figure.subplot.wspace']=mpl.rcParams['figure.subplot.hspace']=0.001
+    fig = plt.figure(figsize=[10,12],dpi=300)
+    with sns.axes_style("white"):
+        axes = fig.subplots(ncols=4,nrows=5,sharex=True,sharey=True)
+    deep = sns.color_palette("colorblind")
+    colors ={"Image":deep[0], "Object":deep[1], "Measurement":deep[2]}
+    dark = sns.color_palette("dark")
+    text_colors = {"Image":dark[0], "Object":dark[1], "Measurement":dark[2]}
+    pad = 0.04
+    for eachq in range(20):
+        x = eachq %4
+        y = int(eachq /4)
+        color = colors[dict_for_plotting['Category'][eachq]]+(.25,)
+        text_color = text_colors[dict_for_plotting['Category'][eachq]]
+        ax = axes[y][x]
+        ax.set_axis_off()
+        ax.set(xlim=(0, 1), ylim=(0, 1))
+        ax.add_artist(FancyBboxPatch(
+            (0.05, 0.78), 0.89, 0.15, f"round,pad={pad}",
+            linewidth=1, edgecolor=text_color, facecolor=color,
+        ))
+        wtxt = WrapText(
+            0.5, 0.85, f"{eachq+1}) {dict_for_plotting['Question'][eachq]}",width=550, widthcoords=None,
+            ha="center", va='center',ma="center", size=7, color=text_color
+        )
+        ax.add_artist(wtxt)
+        answers = dict_for_plotting['Answers'][eachq]
+        pad2 = 0.01
+        h = (0.72-(3*pad2*len(answers)))/(len(answers))
+        color2 = colors[dict_for_plotting['Category'][eachq]]+(.15,)
+        for eachanswer in range(len(answers)):
+            y = 0.75-((h+(3*pad2))*(eachanswer+1))
+            ax.add_artist(FancyBboxPatch(
+            (0.1, y), 0.8, h, f"round,pad={pad2}",
+            linewidth=1, edgecolor=text_color, facecolor=color2,))
+            wtxt = WrapText(
+            0.5, y+(0.5*h), f"{answers[eachanswer]}",width=430, widthcoords=None,
+            ha="center", va='center',ma="center", size=7, color=text_color,linespacing=1)
+            ax.add_artist(wtxt)
+    plt.savefig('figure.png',dpi=300,bbox_inches='tight')
+
+#on import of this, which will be on app reboot/rebuild, remake everything to ensure stuff is up to date
+load_schema(from_master=True,rewrite_master=False)
+make_fig()
+`,
+      "pages/1_Schema_Definitions.py": `
+import json
+import os
+import sys
+
+import streamlit as st
+
+sys.path.append('..')
+from utils import load_schema
+
+json_q_dict = load_schema(from_master=True,rewrite_master=False)
+
+"""
+# Explanation of each of the question in the schema
+
+Please note that questions and answers may be edited or removed in future versions of the schema. Last updated July 7th 2023
+"""
+
+for _,values in json_q_dict.items():
+    st.write(f"## {values['full_name']}")
+    st.write(f"  _{values['long_description']}_")
+    st.write(f"  **Workflow step category**: {values['section']}")
+    st.write(f"  **Current possible answers in the schema**: {' | '.join(list(values['options'].keys()))}")
+
+`,
+      "pages/2_Figure.py": `
+import streamlit as st
+
+st.image('figure.png')
+`,
+      "pages/3_Interactive_Schema.py": `
+import json
+import os
+import sys
+
+import streamlit as st
+
+sys.path.append('..')
+from utils import display_interactive_schema
+
+
+json_q_dict,setting_dict,is_3D = display_interactive_schema()
+`,
+      "pages/4_CellProfiler_Pipeline_Suggester.py": `
+import json
+import os
+import sys
+
+import streamlit as st
+
+sys.path.append('..')
+from utils import display_interactive_schema
+
+
+json_q_dict,setting_dict,is_3D = display_interactive_schema()
+
+def follow_answer_tree(json_q_dict,eachkey,setting_dict,is_3D):
+    if json_q_dict[eachkey]["changes_based_on_3d"]:
+        if type(setting_dict[eachkey]) == list:
+            for eachsubkey in setting_dict[eachkey]:
+                answer = json_q_dict[eachkey]['options'][eachsubkey]["cellprofiler_suggester"][f"3D_{is_3D}"]
+                if answer:#just don't show anything if we haven't written the rec yet
+                    st.write(answer)
+        else:
+            answer = json_q_dict[eachkey]['options'][setting_dict[eachkey]]["cellprofiler_suggester"][f"3D_{is_3D}"]
+            if answer: #just don't show anything if we haven't written the rec yet
+                st.write(answer)              
+
+    else:
+        if type(setting_dict[eachkey]) == list:
+            for eachsubkey in setting_dict[eachkey]:
+                answer = json_q_dict[eachkey]['options'][eachsubkey]["cellprofiler_suggester"]
+                if answer:#just don't show anything if we haven't written the rec yet
+                    st.write(answer)
+        else:
+            answer = json_q_dict[eachkey]['options'][setting_dict[eachkey]]["cellprofiler_suggester"]
+            if answer: #just don't show anything if we haven't written the rec yet
+                st.write(answer)   
+
+"""
+# CellProfiler pipeline recommendations
+
+See below for pipeline suggestions based on the answers to your questions. Note that some "best-case-scenario answers" where therefore no special handling is needed will not generate suggestions, so the number of suggestions may not match the number of questions.
+
+Last updated July 7 2023
+"""
+
+"""
+## CellProfiler pipeline recommendations - image preprocessing
+"""
+for eachkey in setting_dict.keys():
+    if json_q_dict[eachkey]["section"] == 'image':
+        follow_answer_tree(json_q_dict,eachkey,setting_dict,is_3D)
+
+"""
+## CellProfiler pipeline recommendations - object specifications
+"""
+
+for eachkey in setting_dict.keys():
+    if json_q_dict[eachkey]["section"] == 'object':
+        follow_answer_tree(json_q_dict,eachkey,setting_dict,is_3D)
+
+
+"""
+## CellProfiler pipeline recommendations - measurement specifications
+"""
+
+for eachkey in setting_dict.keys():
+    if json_q_dict[eachkey]["section"] == 'measurement':
+        follow_answer_tree(json_q_dict,eachkey,setting_dict,is_3D)
+    
+
+`,
+    },
+  },
+  document.getElementById("root")
+);
+    </script>
   </body>
 </html>
