Implement download log functionality

app.py

@@ -21,6 +21,21 @@ logs_columns = ['Abstract', 'Model', 'Results']
 logs_df = PandasDataFrame(columns=logs_columns)
 
 
+def download_logs():
+    global logs_df
+    # Check for the current operating system's desktop path
+    if os.name == 'nt':  # For Windows
+        desktop = os.path.join(os.path.join(os.environ['USERPROFILE']), 'Desktop')
+    else:  # For macOS and Linux
+        desktop = os.path.join(os.path.join(os.path.expanduser('~')), 'Desktop')
+
+    # Define the path to save the CSV file on the desktop
+    file_path = os.path.join(desktop, 'classification_logs.csv')
+
+    # Save the DataFrame to the CSV file on the desktop
+    logs_df.to_csv(file_path)
+
+
 def build_context(row):
     subsector_name = row['Subsector']
     context = f"Subsector name: {subsector_name}. "
@@ -67,7 +82,7 @@ def on_select(evt: gr.SelectData):  # SelectData is a subclass of EventData
     return name_accordion, definition, keywords, does_include, does_not_include
 
 
-# with gr.Blocks(theme=theme) as startup_genome_demo:
+# --- GRADIO INTERFACE --- #
 with gr.Blocks(css=css, js=js) as demo:
     state_lotto = gr.State()
     selected_x_labels = gr.State()
@@ -114,6 +129,7 @@ with gr.Blocks(css=css, js=js) as demo:
                 label_result = gr.Label(num_top_classes=None)
             with gr.Column(scale=6):
                 reasoning = gr.Markdown(label="Reasoning", elem_classes=['reasoning_results'])
+
     with gr.Tab("Subsector definitions"):
         with gr.Row():
             with gr.Column(scale=4):
@@ -125,20 +141,30 @@ with gr.Blocks(css=css, js=js) as demo:
                                              value="Mixed Reality, 360 video, frame rate, metaverse, virtual world, cross reality, Artificial intelligence, computer vision")
                     does_include = gr.Textbox(label="Does include", lines=4)
                     does_not_include = gr.Textbox(label="Does not include", lines=3)
+
     with gr.Tab("Logs"):
         output_dataframe = gr.Dataframe(
             value=logs_df,
             type="pandas",
             height=500,
-            headers=logs_columns,
+            headers=['Abstract', 'Model', 'Results'],
             interactive=False,
             column_widths=["45%", "10%", "45%"],
         )
+        btn_export = gr.Button(
+            value="Export to CSV",
+            size="sm",
+        )
 
     btn_get_result.click(
         fn=click_button,
         inputs=[dropdown_model, api_key, abstract_description],
         outputs=[label_result, reasoning, output_dataframe])
+
+    btn_export.click(
+        fn=download_logs,
+    )
+
     df_subsectors.select(
         fn=on_select,
         outputs=[subsector_name, s1_definition, s1_keywords, does_include, does_not_include]