Made output of nearest neighbours downloadable

.gitignore

@@ -0,0 +1 @@
+downloads

app.py

@@ -39,8 +39,7 @@ if active_tab == "Nearest neighbours":
             elif time_slice == 'Late Roman':
                 time_slice = 'late_roman'
             
-            time_slice = time_slice.lower() + "_cbow"
-            
+            time_slice = time_slice.lower() + "_cbow"           
             
             
             # Check if all fields are filled in
@@ -56,8 +55,25 @@ if active_tab == "Nearest neighbours":
                     nearest_neighbours,
                     columns=["Word", "Time slice", "Similarity"],
                     index = range(1, len(nearest_neighbours) + 1)
-                )
-                st.table(df)
+                )              
+                st.table(df)             
+                
+                
+                # Store content in a temporary file
+                tmp_file = store_df_in_temp_file(df)
+                
+                # 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}_{time_slice}.xlsx',
+                        mime='application/octet-stream'
+                        )
+
                 
    
 # Cosine similarity tab

word2vec.py

@@ -2,6 +2,7 @@ from gensim.models import Word2Vec
 from collections import defaultdict
 import os
 import tempfile
+import pandas as pd
 
 
 def load_all_models():
@@ -249,6 +250,20 @@ def write_to_file(data):
     return temp_file_path
 
 
+def store_df_in_temp_file(df):
+    '''
+        Store the dataframe in a temporary file
+    '''
+    # Create random tmp file name
+    _, temp_file_path = tempfile.mkstemp(prefix="temp_", suffix=".xlsx", dir="./downloads/nn")
+
+    # Write data to the temporary file
+    with pd.ExcelWriter(temp_file_path, engine='xlsxwriter') as writer:
+        df.to_excel(writer, index=False)
+
+    return temp_file_path
+
+
 def main():
     # model = load_word2vec_model('models/archaic_cbow.model')
     # archaic_cbow_dict = model_dictionary(model)