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| """UI helper functions for Gradio components""" | |
| import gradio as gr | |
| from typing import Tuple, List, Any | |
| def hide_all_results() -> Tuple: | |
| """Hide all result display components""" | |
| return ( | |
| gr.update(visible=False), # result_display | |
| gr.update(visible=False), # html_display | |
| gr.update(visible=False), # result_info | |
| gr.update(visible=False), # seg_nav_controls | |
| gr.update(visible=False), # action_buttons | |
| ) | |
| def hide_viewer_components() -> Tuple: | |
| """Hide image viewer components""" | |
| return ( | |
| gr.update(visible=False), # image_filename | |
| gr.update(visible=False), # viewer_image | |
| gr.update(visible=False), # nav_controls | |
| ) | |
| def show_viewer_with_image(filename: str, image_path: str, counter: str) -> Tuple: | |
| """Show viewer with image loaded""" | |
| return ( | |
| gr.update(value=f"📄 {filename}", visible=True), # image_filename | |
| gr.update(value=image_path, visible=True), # viewer_image | |
| gr.update(visible=True), # nav_controls | |
| counter, # image_counter | |
| ) | |
| def create_empty_state() -> Tuple: | |
| """Create empty state for all components""" | |
| return ( | |
| "", # dataset_info | |
| *hide_viewer_components(), | |
| "", # image_counter | |
| gr.update(visible=False), # action_btn | |
| *hide_all_results(), | |
| [], # current_images | |
| 0, # current_image_idx | |
| "", # current_dataset | |
| "", # current_category | |
| None, # current_result_image | |
| "", # current_result_text | |
| ) | |
| def format_classification_result(result: dict) -> str: | |
| """Format classification result as text""" | |
| if not result or not result.get('top_prediction'): | |
| return "❌ Classification failed" | |
| top_pred = result['top_prediction'] | |
| info_text = ( | |
| f"🎯 CLASSIFICATION RESULT\n\n" | |
| f"Prediction: {top_pred['class_name']}\n" | |
| f"Confidence: {top_pred['confidence']*100:.2f}%\n\n" | |
| f"All Predictions:\n" | |
| ) | |
| info_text += "\n".join( | |
| f" • {pred['class_name']}: {pred['confidence']*100:.2f}%" | |
| for pred in result['all_predictions'] | |
| ) | |
| if result.get('note'): | |
| info_text += f"\n\n⚠️ {result['note']}" | |
| return info_text | |
| def format_detection_result(result: dict) -> str: | |
| """Format detection result as text""" | |
| if not result or not result.get('annotated_image'): | |
| return "❌ Detection failed" | |
| total = result['total_detections'] | |
| info_text = f"🎯 DETECTION RESULT\n\nTotal Detections: {total}\n\n" | |
| if result['predictions']: | |
| info_text += "Detected Objects:\n" | |
| info_text += "\n".join( | |
| f" {i}. {pred['class_name']}: {pred['confidence']*100:.1f}%" | |
| for i, pred in enumerate(result['predictions'], 1) | |
| ) | |
| return info_text | |
| def format_segmentation_result(result: dict) -> str: | |
| """Format segmentation result as text""" | |
| dice_scores = result['dice_scores'] | |
| avg_dice = result['average_dice'] | |
| vol_analysis = result.get('volumetric_analysis', {}) | |
| spatial = result.get('spatial_analysis', {}) | |
| info_text = f"🎯 SEGMENTATION RESULT\n\n" | |
| info_text += f"📊 Dice Scores:\n Average: {avg_dice:.4f}\n" | |
| for k, v in dice_scores.items(): | |
| info_text += f" • {k}: {v:.4f}\n" | |
| if vol_analysis: | |
| info_text += f"\n📐 Volumetric Analysis:\n" | |
| info_text += f" Whole Tumor: {vol_analysis['whole_tumor_volume_cm3']:.2f} cm³\n" | |
| info_text += f" Tumor Core: {vol_analysis['tumor_core_volume_cm3']:.2f} cm³\n" | |
| info_text += f" Enhancing Tumor: {vol_analysis['enhancing_tumor_volume_cm3']:.2f} cm³\n" | |
| if spatial and spatial.get('center_of_mass'): | |
| com = spatial['center_of_mass'] | |
| info_text += f"\n📍 Tumor Location:\n" | |
| info_text += f" Sagittal: {com['sagittal']:.1f}, Coronal: {com['coronal']:.1f}, Axial: {com['axial']:.1f}\n" | |
| return info_text | |