converted nearest neighbours from gradio to streamlit

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 from streamlit_option_menu import option_menu
 from word2vec import *
+import pandas as pd
 
 st.set_page_config(page_title="Ancient Greek Word2Vec", layout="centered")
 
@@ -17,14 +18,29 @@ if active_tab == "Nearest neighbours":
             word = st.text_input("Enter a word", placeholder="ἀνήρ")
             
         with col2:
-            time_slice = st.multiselect("Time slice", ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"])
+            time_slice = st.selectbox("Time slice", ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"])
         
-        st.slider("Number of neighbours", 1, 50, 15)
+        n = st.slider("Number of neighbours", 1, 50, 15)
         
         nearest_neighbours_button = st.button("Find nearest neighbours")
         
+        # If the button to calculate nearest neighbours is clicked
         if nearest_neighbours_button:
-            st.write("button pressed")
+            
+            # Rewrite timeslices to model names: Archaic -> archaic_cbow
+            time_slice = time_slice.lower() + "_cbow"
+            st.write(time_slice)
+            
+            # Check if all fields are filled in
+            if validate_nearest_neighbours(word, time_slice, n) == False:
+                st.error('Please fill in all fields')
+            else:
+                nearest_neighbours = get_nearest_neighbours(word, time_slice, n)
+                df = pd.DataFrame(nearest_neighbours, columns=["Word", "Time slice", "Similarity"])
+                st.table(df)
+                
+                
+                
         
     
 # Cosine similarity tab

word2vec.py

@@ -124,7 +124,7 @@ def validate_nearest_neighbours(word, time_slice_model, n):
     '''
         Validate the input of the nearest neighbours function
     '''
-    if word == '' or time_slice_model == 'models/None.model' or n == '':
+    if word == '' or time_slice_model == [] or n == '':
         return False
     return True
 
@@ -140,15 +140,8 @@ def get_nearest_neighbours(word, time_slice_model, n=10, models=load_all_models(
         
         Return: list of tuples with the word, the time slice and 
                 the cosine similarity of the nearest neighbours
-    '''
-    
-    # Check if all parameters are set
-    valid = validate_nearest_neighbours(word, time_slice_model, n)
-    if valid == False:
-        return [['Error: not all parameters are set', '', '']]
-    
-    
-    
+    '''  
+
     time_slice_model = load_word2vec_model(f'models/{time_slice_model}.model')
     vector_1 = get_word_vector(time_slice_model, word)
     nearest_neighbours = []