download csv?

app.py

@@ -7,6 +7,7 @@ from lavis.processors import load_processor
 from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
 import tensorflow as tf
 import tensorflow_hub as hub
+import io
 from sklearn.metrics.pairwise import cosine_similarity
 
 
@@ -120,21 +121,24 @@ def process_images_and_statements(image):
     logging.info('Finished process_images_and_statements')
 
     # Return the DataFrame directly as output (no need to convert to HTML)
-    return results_df  # <--- Return results_df directly
+    # Save results_df to a CSV file
+    csv_results = save_dataframe_to_csv(results_df)
+    
+    # Return both the DataFrame and the CSV data for the Gradio interface
+    return results_df, csv_results  # <--- Return results_df and csv_results
 
 # Gradio interface
 image_input = gr.inputs.Image()
-output = gr.outputs.Dataframe(type="pandas", label="Results")  # <--- Use "pandas" type for DataFrame output
+output_df = gr.outputs.Dataframe(type="pandas", label="Results")  # <--- Use "pandas" type for DataFrame output
+output_csv = gr.outputs.File(label="Download CSV")  # <--- Added a new output for the downloadable CSV file
 
 iface = gr.Interface(
     fn=process_images_and_statements,
     inputs=image_input,
-    outputs=output,
+    outputs=[output_df, output_csv],  # <--- Updated outputs to include output_csv
     title="Image Captioning and Image-Text Matching",
     theme='sudeepshouche/minimalist',
     css=".output { flex-direction: column; } .output .outputs { width: 100%; }"  # Custom CSS
 )
 
-iface.launch()
-
-
+iface.launch()