Upload app.py

app.py

@@ -1,80 +1,80 @@
-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' 
-                              # 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()
-
-
-
+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(CONFIG_FILE):
+        raise FileNotFoundError(f'Config not found at {CONFIG_FILE}')
+    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):
+    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) and os.path.exists(val):
+                return val
+    return video_input
+
+def analyze_video(video_input):
+    try:
+        if video_input is None:
+            return 'Please upload a video file.'
+        if model is None:
+            return '⚠️ Model not loaded. Check logs for details.'
+        video_path = _resolve_video_path(video_input)
+        if not isinstance(video_path, str) or not os.path.exists(video_path):
+            return '❌ Could not resolve uploaded video path.'
+
+        result = inference_recognizer(model, video_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()
+
+
+