put nearest neighbours function into a form (input process is faster now)

app.py

@@ -24,26 +24,19 @@ lemma_dict = json.load(open('lsj_dict.json', 'r'))
 
 # Nearest neighbours tab
 if active_tab == "Nearest neighbours":
-    st.write("### TO DO: add description of function")
-    col1, col2 = st.columns(2)
     
     # Load the compressed word list
     compressed_word_list_filename = 'corpora/compass_filtered.pkl.gz'
     all_words = load_compressed_word_list(compressed_word_list_filename)
     eligible_models = ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"]
     
-    if 'nearest_neighbours' not in st.session_state:
-        st.session_state.nearest_neighbours = False
-
-    with st.container():
-
-        word = st.multiselect("Enter a word", all_words, max_selections=1)
-        if len(word) > 0:
-            word = word[0]
-            
-            # Check which models contain the word
-            eligible_models = check_word_in_models(word)
+    with st.form("nn_form"):
+        st.markdown("## Nearest Neighbours")
+        target_word = st.multiselect("Enter a word", all_words, max_selections=1)
+        if len(target_word) > 0:
+            target_word = target_word[0]
             
+            eligible_models = check_word_in_models(target_word)
         
         models = st.multiselect(
             "Select models to search for neighbours",
@@ -51,49 +44,45 @@ if active_tab == "Nearest neighbours":
             )
         n = st.slider("Number of neighbours", 1, 50, 15)
         
-        nearest_neighbours_button = st.button("Find nearest neighbours", on_click = click_nn_button)
+        nearest_neighbours_button = st.form_submit_button("Find nearest neighbours", on_click = click_nn_button)
         
-        # If the button to calculate nearest neighbours is clicked
-        if st.session_state.nearest_neighbours: 
+    if nearest_neighbours_button:
+        if validate_nearest_neighbours(target_word, n, models) == False:
+            st.error('Please fill in all fields')
+        else:
+            # Rewrite models to list of all loaded models
+            models = load_selected_models(models)
+            
+            nearest_neighbours = get_nearest_neighbours(target_word, n, models)
+            
+            all_dfs = []
+                            
+            # Create dataframes
+            for model in nearest_neighbours.keys():
+                st.write(f"### {model}")
+                df = pd.DataFrame(
+                    nearest_neighbours[model],
+                    columns = ['Word', 'Cosine Similarity']
+                )
+
+                all_dfs.append((model, df))
+                st.table(df)        
             
-            # Check if all fields are filled in
-            if validate_nearest_neighbours(word, n, models) == False:
-                st.error('Please fill in all fields')
-            else:
-                # Rewrite models to list of all loaded models
-                models = load_selected_models(models)
-                
-                nearest_neighbours = get_nearest_neighbours(word, n, models)
+            
+            # Store content in a temporary file
+            tmp_file = store_df_in_temp_file(all_dfs)
+            
+            # Open the temporary file and read its content
+            with open(tmp_file, "rb") as file:
+                file_byte = file.read()
                 
-                all_dfs = []
-                               
-                # Create dataframes
-                for model in nearest_neighbours.keys():
-                    st.write(f"### {model}")
-                    df = pd.DataFrame(
-                        nearest_neighbours[model],
-                        columns = ['Word', 'Cosine Similarity']
+                # Create download button
+                st.download_button(
+                    "Download results",
+                    data=file_byte,
+                    file_name = f'nearest_neighbours_{target_word}_TEST.xlsx',
+                    mime='application/octet-stream'
                     )
-
-                    all_dfs.append((model, df))
-                    st.table(df)        
-                
-              
-                # Store content in a temporary file
-                tmp_file = store_df_in_temp_file(all_dfs)
-                
-                # Open the temporary file and read its content
-                with open(tmp_file, "rb") as file:
-                    file_byte = file.read()
-                    
-                    # Create download button
-                    st.download_button(
-                        "Download results",
-                        data=file_byte,
-                        file_name = f'nearest_neighbours_{word}_TEST.xlsx',
-                        mime='application/octet-stream'
-                        )
-
                 
    
 # Cosine similarity tab