Expander v1

app.py

@@ -62,16 +62,16 @@ no_of_articles = len(df['cite'].value_counts())
 
 
 notes = f'''Notes:
-* I have found three types of searches that work best:
+* I have found three types of searches work best:
      * Phrases or specific topics, such as "inequality in latin america", "race color skin tone measurement", "audit study experiment gender",  or "logistic regression or linear probability model".
      * Citations to well-known works, either using author year ("bourdieu 1984") or author idea ("Crenshaw intersectionality")
-    * Questions: "What is a topic model?" or "How did Weber define bureaucracy?"
+    * Questions, like "What is a topic model?" or "How did Weber define bureaucracy?"
 * The search expands beyond exact matching, so "asia social movements" may return paragraphs on Asian-Americans politics and South Korean labor unions.
 * The first search can take up to 10 seconds as the files load. After that, it's quicker to respond.
 * The most relevant paragraph to your search is returned first, along with up to four other related paragraphs from that article.
 * The most relevant sentence within each paragraph, as determined by math, is bolded.
 * The results are not exhaustive, and seem to drift off even when you suspect there are more relevant articles :man-shrugging:.
-* The dataset currently includes articles published in the last five years in *Mobilization*, *Social Forces*, *Social Problems*, *Sociology of Race and Ethnicity*, *Gender and Society*, *Socius*, *JHSB*, *Annual Review of Sociology*, and the *American Sociological Review*, totaling {no_of_graphs:,} paragraphs from {no_of_articles:,} articles.
+* The dataset currently includes articles published in the last five years in *Mobilization*, *Social Forces*, *Social Problems*, *Sociology of Race and Ethnicity*, *Gender and Society*, *Socius*, *JHSB*, *Annual Review of Sociology*, and the *American Sociological Review*.
 * Behind the scenes, the semantic search uses [text embeddings](https://www.sbert.net) with a [retrieve & re-rank](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/master/examples/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) process to find the best matches.
 * Let [me](mailto:neal.caren@unc.edu) know what you think or it looks broken.
 '''
@@ -151,7 +151,8 @@ def search(query, top_k=50):
         cite = cite.replace(",  ", '. "').replace(', Social ', '", Social ')
         st.write(cite)
         for graph in graphs[:5]:
-          st.write(f'* {graph}')
+          with st.expander("Thesis Goes here"):
+            st.write(f'* {graph}')
         st.write('')
        # print("\t{:.3f}\t{}".format(hit['cross-score'], passages[hit['corpus_id']].replace("\n", " ")))