add concurrent processing for visualization generation and update report streaming

app.py

@@ -11,6 +11,7 @@ import seaborn as sns
 import io
 import os
 import logging
+import concurrent.futures
 from IPython.display import display, Markdown
 
 
@@ -637,29 +638,34 @@ def process_analysis_flow(user_input, history, state):
         history.append((None, "✅ Data retrieved. Generating visualization..."))
         yield (history, state, None, None, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
 
-        viz_code = llm_generate_visualization_code(query_context, facet_data)
-        plot_path = execute_viz_code_and_get_path(viz_code, facet_data)
-
-        output_plot = gr.update(value=plot_path, visible=True) if plot_path else gr.update(visible=False)
-        if not plot_path:
-            history.append((None, "*I was unable to generate a plot for this data.*\n"))
-        yield (history, state, output_plot, None, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
-
-        # 5. Generate and Stream Report
-        history.append((None, "✅ Plot created. Streaming final report..."))
-        output_report = gr.update(value="", visible=True) # Make it visible before streaming
-        yield (history, state, output_plot, output_report, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
-
-        report_text = ""
-        # The history object is not modified during streaming, so we pass it once
-        # The yield statement for streaming only updates the report text
-        stream_history = history[:] # Make a copy
-        for chunk in llm_generate_summary_and_suggestions_stream(query_context, facet_data):
-            report_text += chunk
-            yield (stream_history, state, output_plot, report_text, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
-        
-        # Update the main history with the final report text
-        history.append((None, report_text))
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            # Start visualization generation in the background
+            viz_future = executor.submit(llm_generate_visualization_code, query_context, facet_data)
+
+            # 5. Generate and Stream Report
+            history.append((None, "✅ Plot created. Streaming final report..."))
+            output_report = gr.update(value="", visible=True) # Make it visible before streaming
+            yield (history, state, None, output_report, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
+
+            report_text = ""
+            # The history object is not modified during streaming, so we pass it once
+            # The yield statement for streaming only updates the report text
+            stream_history = history[:] # Make a copy
+            for chunk in llm_generate_summary_and_suggestions_stream(query_context, facet_data):
+                report_text += chunk
+                yield (stream_history, state, None, report_text, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
+            
+            # Update the main history with the final report text
+            history.append((None, report_text))
+
+            # Get the visualization code from the future
+            viz_code = viz_future.result()
+            plot_path = execute_viz_code_and_get_path(viz_code, facet_data)
+
+            output_plot = gr.update(value=plot_path, visible=True) if plot_path else gr.update(visible=False)
+            if not plot_path:
+                history.append((None, "*I was unable to generate a plot for this data.*\n"))
+            yield (history, state, output_plot, report_text, gr.update(value=formatted_query, visible=True), gr.update(value=formatted_data, visible=True))
 
         # 6. Finalize and prompt for next action
         state['query_count'] += 1