Display co-occurring words

src/callbacks/home/api_query.py

@@ -25,11 +25,23 @@ def get_word_embeddings_db(API_URL, word):
     except:
         return pd.DataFrame()
 
+
 def get_pca_embeddings(API_URL, sense_id):
     try:
-        res = requests.get(f'{API_URL}/get_pca_embeddings/?sense_id={sense_id}')
+        res = requests.get(
+            f'{API_URL}/get_pca_embeddings/?sense_id={sense_id}')
         pca_embeddings = res.json()
 
         return pca_embeddings
     except:
-        return None
+        return None
+
+
+def get_netsci_word(API_URL, word):
+    try:
+        res = requests.get(f'{API_URL}/get_netsci_word/?word={word}')
+        netsci_word = res.json()
+
+        return netsci_word
+    except:
+        return None

src/callbacks/home/network/callbacks.py

@@ -1,11 +1,22 @@
-from dash import Input, Output
+from dash import Input, Output, dcc, html
 from plotly.graph_objs import *
 from dash.exceptions import PreventUpdate
 from ..api_query import *
 from .util import *
 
 
 def init_callback(app, API_URL):
+    @app.callback(
+        Output('communities-dropdown', 'options'),
+        Output('communities-dropdown', 'value'),
+        Input('submitted-word', 'data')
+    )
+    def populate_communities_dropdown(word):
+        if word:
+            return [{'label': 'Sense ' + str(i + 1), 'value': i} for i in range(get_num_senses(API_URL, word))], 0
+
+        raise PreventUpdate
+
     @app.callback(
         Output('input-word-network', 'children'),
         Input('submitted-word', 'data')
@@ -20,3 +31,21 @@ def search_word(word):
             return [f'No Word Found: {word}']
 
         raise PreventUpdate
+
+    @app.callback(
+        Output('network-cooccurring-words', 'children'),
+        Input('submitted-word', 'data'),
+        Input('communities-dropdown', 'value')
+    )
+    def display_co_occurring_words(word, sense_id):
+        for entry in get_netsci_word(API_URL, word):
+            if entry['sense_id'] == f'ns_{word}_{sense_id}':
+                cooccurring_words = [dcc.Link(cooccurring_word, href='', style={'text-decoration': 'none'})
+                                     for cooccurring_word in entry['community']]
+
+                ret_val = []
+                for cooccurring_word in cooccurring_words:
+                    ret_val.append(cooccurring_word)
+                    ret_val.append(html.Span(' ', className='me-2'))
+
+                return ret_val

src/callbacks/home/network/util.py

@@ -0,0 +1,5 @@
+from ..api_query import *
+
+
+def get_num_senses(API_URL, word):
+    return len(get_netsci_word(API_URL, word))

src/pages/home.py

@@ -86,7 +86,16 @@
         html.Div([
             'Words that frequently appear together with ',
             html.Span(id='input-word-network')
-        ])
+        ], className='mb-2 mt-2'),
+
+        dcc.Dropdown(
+            id='communities-dropdown',
+            value='Sense 1'
+        ),
+
+        html.Br(),
+
+        dcc.Loading(html.Div(id='network-cooccurring-words'))
     ])
 ])
 
@@ -198,7 +207,7 @@
 
             word_plot_modal,
 
-            dcc.Loading(dcc.Graph(id="graph-source"))
+            dcc.Graph(id="graph-source")
         ])
     )
 ])