fixed bugs in main scripts

NLselector.py

@@ -1,7 +1,6 @@
 #Import the libraries we know we'll need for the Generator.
 import pandas as pd, spacy, nltk, numpy as np, re
 from spacy.matcher import Matcher
-#!python -m spacy download en_core_web_md #Not sure if we need this so I'm going to keep it just in case
 nlp = spacy.load("en_core_web_lg")
 import altair as alt
 import streamlit as st
@@ -14,6 +13,9 @@ import torch
 import torch.nn.functional as F
 from lime.lime_text import LimeTextExplainer
 
+#Import WNgen.py
+from WNgen import *
+
 class_names = ['negative', 'positive']
 explainer = LimeTextExplainer(class_names=class_names)
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
@@ -27,6 +29,10 @@ def predictor(texts):
 
 @st.experimental_singleton
 def critical_words(document, options=False):
+    '''This function is meant to select the critical part of a sentence. Critical, in this context means
+    the part of the sentence that is either: A) a NOUN or PROPN from the correct entity group, B) a NOUN,
+    C) a NOUN + ADJ combination, or D) ADJ and PROPN used to modify other NOUN tokens.
+    It also checks this against what the model thinks is important if the user defines "options" as "LIME" or True.'''
     if type(document) is not spacy.tokens.doc.Doc:
         document = nlp(document)
     chunks = list(document.noun_chunks)
@@ -43,6 +49,31 @@ def critical_words(document, options=False):
         lime_results = pd.DataFrame(lime_results, columns=["Word","Weight"])
     
     #Identify what we care about "parts of speech"
+    
+    # Here I am going to try to pick up pronouns, which are people, and Adjectival Compliments.
+    for token in document:
+        if (token.text not in pos_options) and ((token.text in lime_options) or (options == False)):
+            #print(f"executed {token.text} with {token.pos_} and {token.dep_}") #QA
+            if (token.pos_ in ["ADJ","PROPN"]) and (token.dep_ in ["compound", "amod"]) and (document[token.i - 1].dep_ in ["compound", "amod"]):
+                compound = document[token.i - 1: token.i +1].text
+                pos_options.append(compound)
+                print(f'Added {compound} based on "amod" and "compound" adjectives.')
+            elif (token.pos_ in ["NOUN"]) and (token.dep_ in ["compound", "amod", "conj"]) and (document[token.i - 1].dep_ in ["compound"]):
+                compound = document[token.i - 1: token.i +1].text
+                pos_options.append(compound)
+                print(f'Added {compound} based on "amod" and "compound" and "conj" nouns.')
+            elif (token.pos_ == "PROPN") and (token.dep_ in ["prep","amod"]):
+                pos_options.append(token.text)
+                print(f"Added '{token.text}' based on their adjectival state.")
+            elif (token.pos_ == "ADJ") and (token.dep_ in ["acomp","conj","amod"]):
+                pos_options.append(token.text)
+                print(f"Added '{token.text}' based on their adjectival state.")
+            elif (token.pos_ == "PRON") and (len(token.morph) !=0):
+                if (token.morph.get("PronType") == "Prs"):
+                    pos_options.append(token.text)
+                    print(f"Added '{token.text}' because it's a human pronoun.")
+    
+    #Noun Chunks parsing
     for chunk in chunks:
         #The use of chunk[-1] is due to testing that it appears to always match the root
         root = chunk[-1]
@@ -58,7 +89,7 @@ def critical_words(document, options=False):
                     #creates a span for the entirety of the compound noun and adds it to the list.
                     span = -1 * (1 + len(compound))
                     pos_options.append(chunk[span:].text)
-                    cur_values + [token.text for token in chunk if token.pos_ == "ADJ"]
+                    cur_values + [token.text for token in chunk if token.pos_ in ["ADJ","NOUN","PROPN"]]
                 else:
                     print(f"The elmenents in {compound} could not be added to the final list because they are not all relevant to the model.")
             else: 
@@ -67,21 +98,14 @@ def critical_words(document, options=False):
                 pos_options.extend(cur_values)
                 print(f"From {chunk.text}, {cur_values} added to pos_options due to entity recognition.") #for QA
         elif len(chunk) >= 1:
-            cur_values = [token.text for token in chunk if token.pos_ in ["NOUN","ADJ"]]
+            cur_values = [token.text for token in chunk if token.pos_ in ["NOUN","ADJ","PROPN"]]
             if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
                 pos_options.extend(cur_values)
                 print(f"From {chunk.text}, {cur_values} added to pos_options due to wildcard.") #for QA
         else:
             print(f"No options added for \'{chunk.text}\' ")
-        # Here I am going to try to pick up pronouns, which are people, and Adjectival Compliments.
-    for token in document:
-        if (token.text not in pos_options) and ((token.text in lime_options) or (options == False)):
-            #print(f"executed {token.text} with {token.pos_} and {token.dep_}") #QA
-            if (token.pos_ == "ADJ") and (token.dep_ in ["acomp","conj"]):
-                pos_options.append(token.text)            
-            elif (token.pos_ == "PRON") and (len(token.morph) !=0):
-                if (token.morph.get("PronType") == "Prs"):
-                    pos_options.append(token.text)
+    
+    pos_options = list(set(pos_options))
     
     if options:
         return pos_options, lime_results
@@ -157,6 +181,7 @@ def abs_dif(df,seed):
     
     text2 = Nearest Prediction
     text3 = Farthest Prediction'''
+    seed = process_text(seed)
     target = df[df['Words'] == seed].pred.iloc[0]
     sub_df = df[df['Words'] != seed].reset_index()
     nearest_prediction = sub_df.pred[(sub_df.pred-target).abs().argsort()[:1]]
@@ -186,7 +211,6 @@ def gen_cf_country(df,_document,selection):
     df['similarity'] = df.Words.apply(lambda x: nlp(selection).similarity(nlp(x)))
     return df
 
-
 def gen_cf_profession(df,_document,selection):
     category = df.loc[df['Words'] == selection, 'Major'].iloc[0]
     df = df[df.Major == category]

WNgen.py

@@ -12,8 +12,8 @@ tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst
 model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
 pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) 
 
-'''#If an error is thrown that the corpus "omw-1.4" isn't discoverable you can use this code. (https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed)
-try:
+#If an error is thrown that the corpus "omw-1.4" isn't discoverable you can use this code. (https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed)
+'''try:
     _create_unverified_https_context = ssl._create_unverified_context
 except AttributeError:
     pass
@@ -234,6 +234,7 @@ def get_parallel(word, seed_definition, QA=False):
         hypos = list(set([syn for syn in new_hypos if cleaned.similarity(nlp(process_text(syn.definition()))) >=.66]))[:25]
     elif len(hypos) >= 10: 
         hypos = list(set([syn for syn in new_hypos if cleaned.similarity(nlp(process_text(syn.definition()))) >=.8]))[:25]
+    
     if QA:
         print(hypers)
         print(hypos)

app.py

@@ -1,8 +1,7 @@
 #Import the libraries we know we'll need for the Generator.
-import pandas as pd, spacy, nltk, numpy as np
+import pandas as pd, spacy, nltk, numpy as np, re
 from spacy.matcher import Matcher
 nlp = spacy.load("en_core_web_lg")
-from nltk.corpus import wordnet
 
 #Import the libraries to support the model and predictions.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
@@ -35,29 +34,30 @@ def prepare_model():
 
 @st.experimental_singleton
 def prepare_lists():
-    nltk.download('omw-1.4')
-    nltk.download('wordnet')
     countries = pd.read_csv("Assets/Countries/combined-countries.csv")
     professions = pd.read_csv("Assets/Professions/soc-professions-2018.csv")
-    word_lists = [list(countries.Words),list(professions.Words)]
+    word_lists = [list(countries.Words.apply(lambda x: x.lower())),list(professions.Words)]
     return countries, professions, word_lists
 
 #Provide all the functions necessary to run the app
 #get definitions for control flow in Streamlit
 def get_def(word, POS=False):
     pos_options = ['NOUN','VERB','ADJ','ADV']
-    m_word = word.replace(" ", "_")
+    m_word = re.sub("(\W\s|\s)","_",word)
     if POS in pos_options:
         seed_definitions = [syn.definition() for syn in wordnet.synsets(m_word, pos=getattr(wordnet, POS))]
     else:
         seed_definitions = [syn.definition() for syn in wordnet.synsets(m_word)]
-    seed_definition = col1.selectbox("Which definition is most relevant?", seed_definitions, key= "WN_definition")
-    if col1.button("Choose Definition"):
-        col1.write("You've chosen a definition.")
-        st.session_state.definition = seed_definition
-        return seed_definition
+    if len(seed_definitions) > 0:
+        seed_definition = col1.selectbox("Which definition is most relevant?", seed_definitions, key= "WN_definition")
+        if col1.button("Choose Definition"):
+            col1.write("You've chosen a definition.")
+            st.session_state.definition = seed_definition
+            return seed_definition
+        else:
+            col1.write("Please choose a definition.")
     else:
-        col1.write("Please choose a definition.")
+        col1.error("The word you've chosen does not have a definition within WordNet.")
         
 ###Start coding the actual app###
 st.set_page_config(layout="wide", page_title="Natural Language Counterfactuals (NLC)")
@@ -124,7 +124,8 @@ if layout == 'MultiNLC':
 
         #Allow the user to pick an option to generate counterfactuals from.
             option = st.radio('Which word would you like to use to generate alternatives?', options, key = "option")
-            if (any(option in sublist for sublist in word_lists)):
+            lc_option = option.lower()
+            if (any(lc_option in sublist for sublist in word_lists)):
                 st.write(f'You selected {option}. It matches a list.')
             elif option:
                 st.write(f'You selected {option}. It does not match a list.')
@@ -133,10 +134,10 @@ if layout == 'MultiNLC':
                 st.write('Awaiting your selection.')
 
             if st.button('Generate Alternatives'):
-                if option in list(countries.Words):
+                if lc_option in word_lists[0]:
                     cf_df = gen_cf_country(countries, doc, option)
                     st.success('Alternatives created.')
-                elif option in list(professions.Words):
+                elif lc_option in word_lists[1]:
                     cf_df = gen_cf_profession(professions, doc, option)
                     st.success('Alternatives created.')
                 else:
@@ -198,7 +199,8 @@ if layout == 'MultiNLC + Lime':
 
         #Allow the user to pick an option to generate counterfactuals from.
             option = st.radio('Which word would you like to use to generate alternatives?', options, key = "option")
-            if (any(option in sublist for sublist in word_lists)):
+            lc_option = option.lower()
+            if (any(lc_option in sublist for sublist in word_lists)):
                 st.write(f'You selected {option}. It matches a list.')
             elif option:
                 st.write(f'You selected {option}. It does not match a list.')
@@ -207,10 +209,10 @@ if layout == 'MultiNLC + Lime':
                 st.write('Awaiting your selection.')
 
             if st.button('Generate Alternatives'):
-                if option in list(countries.Words):
+                if lc_option in word_lists[0]:
                     cf_df = gen_cf_country(countries, doc, option)
                     st.success('Alternatives created.')
-                elif option in list(professions.Words):
+                elif lc_option in word_lists[1]:
                     cf_df = gen_cf_profession(professions, doc, option)
                     st.success('Alternatives created.')
                 else:
@@ -278,7 +280,8 @@ if layout == 'VizNLC':
 
         #Allow the user to pick an option to generate counterfactuals from.
             option = st.radio('Which word would you like to use to generate alternatives?', options, key = "option")
-            if (any(option in sublist for sublist in word_lists)):
+            lc_option = option.lower()
+            if (any(lc_option in sublist for sublist in word_lists)):
                 st.write(f'You selected {option}. It matches a list.')
             elif option:
                 st.write(f'You selected {option}. It does not match a list.')
@@ -287,10 +290,10 @@ if layout == 'VizNLC':
                 st.write('Awaiting your selection.')
 
             if st.button('Generate Alternatives'):
-                if option in list(countries.Words):
+                if lc_option in word_lists[0]:
                     cf_df = gen_cf_country(countries, doc, option)
                     st.success('Alternatives created.')
-                elif option in list(professions.Words):
+                elif lc_option in word_lists[1]:
                     cf_df = gen_cf_profession(professions, doc, option)
                     st.success('Alternatives created.')
                 else:
@@ -303,15 +306,15 @@ if layout == 'VizNLC':
                     if alt_choice == "Similarity":
                         text2, text3 = get_min_max(cf_df, option)
                         col2.caption(f"This sentence is 'similar' to {option}.")
-                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                        col3.caption(f"This sentence is 'not similar' to {option}.")
                     elif alt_choice == "Sampling (Random)":
                         text2, text3 = sampled_alts(cf_df, option)
                         col2.caption(f"This sentence is a random sample from the alternatives.")
-                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                        col3.caption(f"This sentence is a random sample from the alternatives.")
                     elif alt_choice == "Sampling (Fixed)":
                         text2, text3 = sampled_alts(cf_df, option, fixed=True)
                         col2.caption(f"This sentence is a fixed sample of the alternatives.")
-                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                        col3.caption(f"This sentence is a fixed sample of the alternatives.")
                     elif alt_choice == "Probability":
                         text2, text3 = abs_dif(cf_df, option)
                         col2.caption(f"This sentence is the closest prediction in the model.")

backup-app.py

@@ -0,0 +1,343 @@
+#Import the libraries we know we'll need for the Generator.
+import pandas as pd, spacy, nltk, numpy as np
+from spacy.matcher import Matcher
+nlp = spacy.load("en_core_web_lg")
+from nltk.corpus import wordnet
+
+#Import the libraries to support the model and predictions.
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+import lime
+import torch
+import torch.nn.functional as F
+from lime.lime_text import LimeTextExplainer
+
+#Import the libraries for human interaction and visualization.
+import altair as alt
+import streamlit as st
+from annotated_text import annotated_text as ant
+
+#Import functions needed to build dataframes of keywords from WordNet
+from WNgen import *
+from NLselector import *
+
+@st.experimental_singleton
+def set_up_explainer():
+    class_names = ['negative', 'positive']
+    explainer = LimeTextExplainer(class_names=class_names)
+    return explainer
+
+@st.experimental_singleton
+def prepare_model():
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+    model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+    pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) 
+    return tokenizer, model, pipe
+
+@st.experimental_singleton
+def prepare_lists():
+    nltk.download('omw-1.4')
+    nltk.download('wordnet')
+    countries = pd.read_csv("Assets/Countries/combined-countries.csv")
+    professions = pd.read_csv("Assets/Professions/soc-professions-2018.csv")
+    word_lists = [list(countries.Words),list(professions.Words)]
+    return countries, professions, word_lists
+
+#Provide all the functions necessary to run the app
+#get definitions for control flow in Streamlit
+def get_def(word, POS=False):
+    pos_options = ['NOUN','VERB','ADJ','ADV']
+    m_word = word.replace(" ", "_")
+    if POS in pos_options:
+        seed_definitions = [syn.definition() for syn in wordnet.synsets(m_word, pos=getattr(wordnet, POS))]
+    else:
+        seed_definitions = [syn.definition() for syn in wordnet.synsets(m_word)]
+    seed_definition = col1.selectbox("Which definition is most relevant?", seed_definitions, key= "WN_definition")
+    if col1.button("Choose Definition"):
+        col1.write("You've chosen a definition.")
+        st.session_state.definition = seed_definition
+        return seed_definition
+    else:
+        col1.write("Please choose a definition.")
+        
+###Start coding the actual app###
+st.set_page_config(layout="wide", page_title="Natural Language Counterfactuals (NLC)")
+layouts = ['Natural Language Explanation', 'Lime Explanation', 'MultiNLC', 'MultiNLC + Lime', 'VizNLC']
+alternatives = ['Similarity', 'Sampling (Random)', 'Sampling (Fixed)', 'Probability']
+alt_choice = "Similarity"
+
+#Content in the Sidebar.
+st.sidebar.info('This is an interface for exploring how different interfaces for exploring natural language explanations (NLE) may appear to people. It is intended to allow individuals to provide feedback on specific versions, as well as to compare what one offers over others for the same inputs.')
+layout = st.sidebar.selectbox("Select a layout to explore.", layouts)
+alt_choice = st.sidebar.selectbox("Choose the way you want to display alternatives.", alternatives) #Commented out until we decide this is useful functionality.
+
+#Set up the Main Area Layout
+st.title('Natural Language Counterfactuals (NLC) Prototype')
+st.subheader(f'Current Layout: {layout}')
+text = st.text_input('Provide a sentence you want to evaluate.', placeholder = "I like you. I love you.", key="input")
+
+#Prepare the model, data, and Lime. Set starting variables.
+tokenizer, model, pipe = prepare_model()
+countries, professions, word_lists = prepare_lists()
+explainer = set_up_explainer()
+text2 = ""
+text3 = ""
+cf_df = pd.DataFrame()
+if 'definition' not in st.session_state:
+    st.session_state.definition = "<(^_')>"
+    
+#Outline the various user interfaces we have built.
+
+col1, col2, col3 = st.columns(3)    
+if layout == 'Natural Language Explanation':
+    with col1:
+        if st.session_state.input != "":
+            st.caption("This is the sentence you provided.")
+            st.write(text)
+            probability, sentiment = eval_pred(text, return_all=True)
+            nat_lang_explanation = construct_nlexp(text,sentiment,probability)
+
+if layout == 'Lime Explanation':
+    with col1:
+        #Use spaCy to make the sentence into a doc so we can do NLP.
+        doc = nlp(st.session_state.input)
+        #Evaluate the provided sentence for sentiment and probability.
+        if st.session_state.input != "":
+            st.caption("This is the sentence you provided.")
+            st.write(text)
+            probability, sentiment = eval_pred(text, return_all=True)
+            options, lime = critical_words(st.session_state.input,options=True)
+            nat_lang_explanation = construct_nlexp(text,sentiment,probability)
+            st.write(" ")
+            st.altair_chart(lime_viz(lime))
+            
+if layout == 'MultiNLC':
+    with col1:
+        #Use spaCy to make the sentence into a doc so we can do NLP.
+        doc = nlp(st.session_state.input)
+        #Evaluate the provided sentence for sentiment and probability.
+        if st.session_state.input != "":
+            st.caption("This is the sentence you provided.")
+            st.write(text)
+            probability, sentiment = eval_pred(text, return_all=True)
+            options, lime = critical_words(st.session_state.input,options=True)
+            nat_lang_explanation = construct_nlexp(text,sentiment,probability)
+
+        #Allow the user to pick an option to generate counterfactuals from.
+            option = st.radio('Which word would you like to use to generate alternatives?', options, key = "option")
+            if (any(option in sublist for sublist in word_lists)):
+                st.write(f'You selected {option}. It matches a list.')
+            elif option:
+                st.write(f'You selected {option}. It does not match a list.')
+                definition = get_def(option)
+            else:
+                st.write('Awaiting your selection.')
+
+            if st.button('Generate Alternatives'):
+                if option in list(countries.Words):
+                    cf_df = gen_cf_country(countries, doc, option)
+                    st.success('Alternatives created.')
+                elif option in list(professions.Words):
+                    cf_df = gen_cf_profession(professions, doc, option)
+                    st.success('Alternatives created.')
+                else:
+                    with st.sidebar:
+                        ant("Generating alternatives for",(option,"opt","#E0FBFB"), "with a definition of: ",(st.session_state.definition,"def","#E0FBFB"),".")
+                    cf_df = cf_from_wordnet_df(option,text,seed_definition=st.session_state.definition)
+                    st.success('Alternatives created.')
+
+                if len(cf_df) != 0:
+                    if alt_choice == "Similarity":
+                        text2, text3 = get_min_max(cf_df, option)
+                        col2.caption(f"This sentence is 'similar' to {option}.")
+                        col3.caption(f"This sentence is 'not similar' to {option}.")
+                    elif alt_choice == "Sampling (Random)":
+                        text2, text3 = sampled_alts(cf_df, option)
+                        col2.caption(f"This sentence is a random sample from the alternatives.")
+                        col3.caption(f"This sentence is a random sample from the alternatives.")
+                    elif alt_choice == "Sampling (Fixed)":
+                        text2, text3 = sampled_alts(cf_df, option, fixed=True)
+                        col2.caption(f"This sentence is a fixed sample of the alternatives.")
+                        col3.caption(f"This sentence is a fixed sample of the alternatives.")
+                    elif alt_choice == "Probability":
+                        text2, text3 = abs_dif(cf_df, option)
+                        col2.caption(f"This sentence is the closest prediction in the model.")
+                        col3.caption(f"This sentence is the farthest prediction in the model.")
+                    with st.sidebar:
+                        st.info(f"Alternatives generated: {len(cf_df)}")
+        
+    with col2:
+        if text2 != "":
+            sim2 = cf_df.loc[cf_df['text'] == text2, 'similarity'].iloc[0]
+            st.write(text2)
+            probability2, sentiment2 = eval_pred(text2, return_all=True)
+            nat_lang_explanation = construct_nlexp(text2,sentiment2,probability2)
+            #st.info(f" Similarity Score: {np.round(sim2, 2)}, Num Checked: {len(cf_df)}") #for QA purposes
+
+    with col3:
+        if text3 != "":
+            sim3 = cf_df.loc[cf_df['text'] == text3, 'similarity'].iloc[0]
+            st.write(text3)
+            probability3, sentiment3 = eval_pred(text3, return_all=True)
+            nat_lang_explanation = construct_nlexp(text3,sentiment3,probability3)
+            #st.info(f"Similarity Score: {np.round(sim3, 2)}, Num Checked: {len(cf_df)}") #for QA purposes
+
+if layout == 'MultiNLC + Lime':
+    with col1:
+
+        #Use spaCy to make the sentence into a doc so we can do NLP.
+        doc = nlp(st.session_state.input)
+        #Evaluate the provided sentence for sentiment and probability.
+        if st.session_state.input != "":
+            st.caption("This is the sentence you provided.")
+            st.write(text)
+            probability, sentiment = eval_pred(text, return_all=True)
+            options, lime = critical_words(st.session_state.input,options=True)
+            nat_lang_explanation = construct_nlexp(text,sentiment,probability)
+            st.write(" ")
+            st.altair_chart(lime_viz(lime))
+
+        #Allow the user to pick an option to generate counterfactuals from.
+            option = st.radio('Which word would you like to use to generate alternatives?', options, key = "option")
+            if (any(option in sublist for sublist in word_lists)):
+                st.write(f'You selected {option}. It matches a list.')
+            elif option:
+                st.write(f'You selected {option}. It does not match a list.')
+                definition = get_def(option)
+            else:
+                st.write('Awaiting your selection.')
+
+            if st.button('Generate Alternatives'):
+                if option in list(countries.Words):
+                    cf_df = gen_cf_country(countries, doc, option)
+                    st.success('Alternatives created.')
+                elif option in list(professions.Words):
+                    cf_df = gen_cf_profession(professions, doc, option)
+                    st.success('Alternatives created.')
+                else:
+                    with st.sidebar:
+                        ant("Generating alternatives for",(option,"opt","#E0FBFB"), "with a definition of: ",(st.session_state.definition,"def","#E0FBFB"),".")
+                    cf_df = cf_from_wordnet_df(option,text,seed_definition=st.session_state.definition)
+                    st.success('Alternatives created.')
+
+                if len(cf_df) != 0:
+                    if alt_choice == "Similarity":
+                        text2, text3 = get_min_max(cf_df, option)
+                        col2.caption(f"This sentence is 'similar' to {option}.")
+                        col3.caption(f"This sentence is 'not similar' to {option}.")
+                    elif alt_choice == "Sampling (Random)":
+                        text2, text3 = sampled_alts(cf_df, option)
+                        col2.caption(f"This sentence is a random sample from the alternatives.")
+                        col3.caption(f"This sentence is a random sample from the alternatives.")
+                    elif alt_choice == "Sampling (Fixed)":
+                        text2, text3 = sampled_alts(cf_df, option, fixed=True)
+                        col2.caption(f"This sentence is a fixed sample of the alternatives.")
+                        col3.caption(f"This sentence is a fixed sample of the alternatives.")
+                    elif alt_choice == "Probability":
+                        text2, text3 = abs_dif(cf_df, option)
+                        col2.caption(f"This sentence is the closest prediction in the model.")
+                        col3.caption(f"This sentence is the farthest prediction in the model.")
+                    with st.sidebar:
+                        st.info(f"Alternatives generated: {len(cf_df)}")
+
+    with col2:
+        if text2 != "":
+            sim2 = cf_df.loc[cf_df['text'] == text2, 'similarity'].iloc[0]
+            st.write(text2)
+            probability2, sentiment2 = eval_pred(text2, return_all=True)
+            nat_lang_explanation = construct_nlexp(text2,sentiment2,probability2)
+            exp2 = explainer.explain_instance(text2, predictor, num_features=15, num_samples=2000)
+            lime_results2 = exp2.as_list()
+            st.write(" ")
+            st.altair_chart(lime_viz(lime_results2))
+
+    with col3:
+        if text3 != "":
+            sim3 = cf_df.loc[cf_df['text'] == text3, 'similarity'].iloc[0]
+            st.write(text3)
+            probability3, sentiment3 = eval_pred(text3, return_all=True)
+            nat_lang_explanation = construct_nlexp(text3,sentiment3,probability3)
+            exp3 = explainer.explain_instance(text3, predictor, num_features=15, num_samples=2000)
+            lime_results3 = exp3.as_list()
+            st.write(" ")
+            st.altair_chart(lime_viz(lime_results3))
+            
+if layout == 'VizNLC':
+    with col1:
+
+        #Use spaCy to make the sentence into a doc so we can do NLP.
+        doc = nlp(st.session_state.input)
+        #Evaluate the provided sentence for sentiment and probability.
+        if st.session_state.input != "":
+            st.caption("This is the sentence you provided.")
+            st.write(text)
+            probability, sentiment = eval_pred(text, return_all=True)
+            options, lime = critical_words(st.session_state.input,options=True)
+            nat_lang_explanation = construct_nlexp(text,sentiment,probability)
+            st.write(" ")
+            st.altair_chart(lime_viz(lime))
+
+        #Allow the user to pick an option to generate counterfactuals from.
+            option = st.radio('Which word would you like to use to generate alternatives?', options, key = "option")
+            if (any(option in sublist for sublist in word_lists)):
+                st.write(f'You selected {option}. It matches a list.')
+            elif option:
+                st.write(f'You selected {option}. It does not match a list.')
+                definition = get_def(option)
+            else:
+                st.write('Awaiting your selection.')
+
+            if st.button('Generate Alternatives'):
+                if option in list(countries.Words):
+                    cf_df = gen_cf_country(countries, doc, option)
+                    st.success('Alternatives created.')
+                elif option in list(professions.Words):
+                    cf_df = gen_cf_profession(professions, doc, option)
+                    st.success('Alternatives created.')
+                else:
+                    with st.sidebar:
+                        ant("Generating alternatives for",(option,"opt","#E0FBFB"), "with a definition of: ",(st.session_state.definition,"def","#E0FBFB"),".")
+                    cf_df = cf_from_wordnet_df(option,text,seed_definition=st.session_state.definition)
+                    st.success('Alternatives created.')
+
+                if len(cf_df) != 0:
+                    if alt_choice == "Similarity":
+                        text2, text3 = get_min_max(cf_df, option)
+                        col2.caption(f"This sentence is 'similar' to {option}.")
+                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                    elif alt_choice == "Sampling (Random)":
+                        text2, text3 = sampled_alts(cf_df, option)
+                        col2.caption(f"This sentence is a random sample from the alternatives.")
+                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                    elif alt_choice == "Sampling (Fixed)":
+                        text2, text3 = sampled_alts(cf_df, option, fixed=True)
+                        col2.caption(f"This sentence is a fixed sample of the alternatives.")
+                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                    elif alt_choice == "Probability":
+                        text2, text3 = abs_dif(cf_df, option)
+                        col2.caption(f"This sentence is the closest prediction in the model.")
+                        col3.caption(f"This graph represents the {len(cf_df)} alternatives to {option}.")
+                    with st.sidebar:
+                        st.info(f"Alternatives generated: {len(cf_df)}")
+    
+    with col2:
+        if text2 != "":
+            sim2 = cf_df.loc[cf_df['text'] == text2, 'similarity'].iloc[0]
+            st.write(text2)
+            probability2, sentiment2 = eval_pred(text2, return_all=True)
+            nat_lang_explanation = construct_nlexp(text2,sentiment2,probability2)
+            exp2 = explainer.explain_instance(text2, predictor, num_features=15, num_samples=2000)
+            lime_results2 = exp2.as_list()
+            st.write(" ")
+            st.altair_chart(lime_viz(lime_results2))
+
+    with col3:
+        if not cf_df.empty:
+            single_nearest = alt.selection_single(on='mouseover', nearest=True)
+            full = alt.Chart(cf_df).encode(
+                alt.X('similarity:Q', scale=alt.Scale(zero=False)),
+                alt.Y('pred:Q'),
+                color=alt.Color('Categories:N', legend=alt.Legend(title="Color of Categories")),
+                size=alt.Size('seed:O'),
+                tooltip=('Categories','text','pred')
+            ).mark_circle(opacity=.5).properties(width=450, height=450).add_selection(single_nearest)
+            st.altair_chart(full)