| import os | |
| from operator import itemgetter | |
| import gradio as gr | |
| from mmaction.apis import init_recognizer, inference_recognizer | |
| CONFIG_FILE = 'demo/demo_configs/tsn_r50_1x1x8_video_infer.py' | |
| CHECKPOINT_FILE = 'checkpoints/tsn_r50_8xb32-1x1x8-100e_kinetics400-rgb_20220818-2692d16c.pth' | |
| LABEL_FILE = 'tools/data/kinetics/label_map_k400.txt' | |
| def load_labels(path): | |
| if os.path.exists(path): | |
| with open(path, 'r') as f: | |
| return [x.strip() for x in f if x.strip()] | |
| return None | |
| def build_model(): | |
| if not os.path.exists(CHECKPOINT_FILE): | |
| raise FileNotFoundError(f'Checkpoint not found at {CHECKPOINT_FILE}') | |
| return init_recognizer(CONFIG_FILE, CHECKPOINT_FILE, device='cpu') | |
| print('Initializing model...') | |
| try: | |
| model = build_model() | |
| print('✅ Model loaded successfully!') | |
| except Exception as e: | |
| print(f'❌ Error loading model: {e}') | |
| model = None | |
| labels = load_labels(LABEL_FILE) | |
| def _resolve_video_path(video_input): | |
| """Best-effort extraction of a filesystem path from Gradio Video input.""" | |
| if isinstance(video_input, str): | |
| return video_input | |
| if isinstance(video_input, dict): | |
| for key in ('name', 'path', 'video', 'file'): | |
| val = video_input.get(key) | |
| if isinstance(val, str): | |
| return val | |
| return video_input | |
| def analyze_video(video_path: str): | |
| try: | |
| if video_path is None: | |
| return 'Please upload a video file.' | |
| if model is None: | |
| return '⚠️ Model not loaded. Check logs for details.' | |
| video_fs_path = _resolve_video_path(video_path) | |
| result = inference_recognizer(model, video_fs_path) | |
| pred_scores = result.pred_score.tolist() | |
| score_sorted = sorted(zip(range(len(pred_scores)), pred_scores), key=itemgetter(1), reverse=True) | |
| top5 = score_sorted[:5] | |
| lines = [] | |
| for idx, score in top5: | |
| name = labels[idx] if labels and idx < len(labels) else f'class_{idx}' | |
| lines.append(f'{name}: {score}') | |
| return '\n'.join(lines) | |
| except Exception as e: | |
| return f'❌ Error processing video: {str(e)}' | |
| demo = gr.Interface( | |
| fn=analyze_video, | |
| inputs=gr.Video(label='Upload Video', height=300), | |
| outputs=gr.Textbox(label='Analysis Results', lines=12), | |
| title='🎬 GenVidBench - TSN (MMAction2)', | |
| description='Upload a video. Inference uses TSN R50 on Kinetics-400.', | |
| cache_examples=False, | |
| flagging_mode='never' | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch() |