Merge branch 'main' of https://huggingface.co/spaces/butterswords/nlc-explorer

app.py

@@ -2,8 +2,6 @@
 import pandas as pd, spacy, nltk, numpy as np
 from spacy.matcher import Matcher
 nlp = spacy.load("en_core_web_lg")
-nltk.download('omw-1.4')
-nltk.download('wordnet')
 from nltk.corpus import wordnet
 
 #Import the libraries to support the model and predictions.
@@ -37,6 +35,8 @@ 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)]
@@ -303,15 +303,15 @@ 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 sentence is 'not 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 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 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.")