Upload app.py

tools/data/kinetics/app.py

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+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_1x1x3_100e_kinetics400_rgb_20200614-e508be42.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()
+
+
+