v.1.28

app.py

@@ -532,7 +532,7 @@ def create_interface():
     control = ProcessControl()
     
     with gr.Blocks(theme=gr.themes.Soft()) as app:
-        gr.Markdown("# AI-анализ мониторинга новостей v.1.27")
+        gr.Markdown("# AI-анализ мониторинга новостей v.1.28")
         
         with gr.Row():
             file_input = gr.File(
@@ -575,24 +575,33 @@ def create_interface():
             with gr.Column(scale=1):
                 events_plot = gr.Plot(label="Распределение событий")
                 
+        # Add download button to UI
+        with gr.Row():
+            download_file = gr.File(
+                label="📥 Скачать полный отчет",
+                file_types=[".xlsx"],
+                interactive=False
+            )
+                
         def stop_processing():
             control.request_stop()
             return "Остановка обработки..."
-        
-        @spaces.GPU(duration=300)  # 5 minutes duration for the entire analysis
+                
+        @spaces.GPU(duration=300)
         def analyze(file_bytes):
             if file_bytes is None:
                 gr.Warning("Пожалуйста, загрузите файл")
-                return None, None, None, "Ожидание файла..."
+                return None, None, None, None, "Ожидание файла..."
                 
             try:
-                # Reset control and initialize detector
+                # Reset stop flag
                 control.reset()
-                detector = EventDetector()
                 
                 file_obj = io.BytesIO(file_bytes)
                 logger.info("File loaded into BytesIO successfully")
                 
+                detector = EventDetector()
+                
                 # Read and deduplicate data
                 df = pd.read_excel(file_obj, sheet_name='Публикации')
                 original_count = len(df)
@@ -605,6 +614,19 @@ def create_interface():
                 
                 for batch_start in range(0, total, batch_size):
                     if control.should_stop():
+                        # Create partial results if stopped
+                        if processed_rows:
+                            result_df = pd.DataFrame(processed_rows)
+                            output = create_output_file(result_df, file_obj)
+                            if output:
+                                fig_sentiment, fig_events = create_visualizations(result_df)
+                                return (
+                                    result_df,
+                                    fig_sentiment,
+                                    fig_events,
+                                    (output, f"partial_results_{len(processed_rows)}_rows.xlsx"),
+                                    f"Обработка остановлена. Обработано {len(processed_rows)}/{total} строк"
+                                )
                         break
                         
                     batch_end = min(batch_start + batch_size, total)
@@ -630,50 +652,65 @@ def create_interface():
                                 'Reasoning': results['reasoning'],
                                 'Event_Type': results['event_type'],
                                 'Event_Summary': results['event_summary'],
-                                'Текст': text[:1000]
+                                'Выдержки из текста': text[:1000]
                             })
                             
                         except Exception as e:
                             logger.error(f"Error processing row {idx}: {str(e)}")
                             continue
                     
-                    # Create intermediate results
+                    # Create intermediate results and yield
                     if processed_rows:
                         result_df = pd.DataFrame(processed_rows)
-                        fig_sentiment, fig_events = create_visualizations(result_df)
-                        yield (
-                            result_df,
-                            fig_sentiment,
-                            fig_events,
-                            f"Обработано {len(processed_rows)}/{total} строк"
-                        )
+                        output = create_output_file(result_df, file_obj)
+                        if output:
+                            fig_sentiment, fig_events = create_visualizations(result_df)
+                            yield (
+                                result_df,
+                                fig_sentiment,
+                                fig_events,
+                                (output, f"results_{len(processed_rows)}_rows.xlsx"),
+                                f"Обработано {len(processed_rows)}/{total} строк"
+                            )
                     
                     # Cleanup GPU resources after batch
                     torch.cuda.empty_cache()
                     time.sleep(2)
                 
+                # Create final results
                 if processed_rows:
                     final_df = pd.DataFrame(processed_rows)
-                    fig_sentiment, fig_events = create_visualizations(final_df)
-                    return final_df, fig_sentiment, fig_events, "Обработка завершена!"
+                    output = create_output_file(final_df, file_obj)
+                    if output:
+                        fig_sentiment, fig_events = create_visualizations(final_df)
+                        return (
+                            final_df,
+                            fig_sentiment,
+                            fig_events,
+                            (output, "final_results.xlsx"),
+                            "Обработка завершена!"
+                        )
                 else:
-                    return None, None, None, "Нет обработанных данных"
+                    return None, None, None, None, "Нет обработанных данных"
                     
             except Exception as e:
                 error_msg = f"Ошибка анализа: {str(e)}"
                 logger.error(error_msg)
                 gr.Error(error_msg)
-                return None, None, None, error_msg
+                return None, None, None, None, error_msg
+            finally:
+                if detector:
+                    detector.cleanup()
             
         stop_btn.click(fn=stop_processing, outputs=[progress])
         analyze_btn.click(
             fn=analyze,
             inputs=[file_input],
-            outputs=[stats, sentiment_plot, events_plot, progress]
+            outputs=[stats, sentiment_plot, events_plot, download_file, progress]
         )
         
     return app
 
 if __name__ == "__main__":
     app = create_interface()
-    app.launch(share=True)
+    app.launch(share=True)