Update app.py

app.py

@@ -1,65 +1,76 @@
 import os
-import gradio as gr
 from operator import itemgetter
+import gradio as gr
 from mmaction.apis import init_recognizer, inference_recognizer
 
-# --- Config & Checkpoint ---
-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"
+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')
 
-# --- Initialize model ---
-device = "cuda" if os.path.exists("/dev/nvidia0") else "cpu"
-print(f"🚀 Initializing TSN Model on {device}...")
-model = init_recognizer(config_file, checkpoint_file, device=device)
+print('Initializing model...')
+try:
+    model = build_model()
+    print('✅ Model loaded successfully!')
+except Exception as e:
+    print(f'❌ Error loading model: {e}')
+    model = None
 
-# Load labels
-with open(label_file) as f:
-    labels = [x.strip() for x in f.readlines()]
+labels = load_labels(LABEL_FILE)
 
-def analyze_video(video_path):
-    """Run action recognition on uploaded video"""
+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."
+            return 'Please upload a video file.'
+        if model is None:
+            return '⚠️ Model not loaded. Check logs for details.'
 
-        # Inference
-        results = inference_recognizer(model, video_path)
+        video_fs_path = _resolve_video_path(video_path)
+        result = inference_recognizer(model, video_fs_path)
 
-        # Extract top-5 results
-        pred_scores = results.pred_score.tolist()
-        score_tuples = tuple(zip(range(len(pred_scores)), pred_scores))
-        score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True)
+        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]
 
-        # Format results
         lines = []
         for idx, score in top5:
-            lines.append(f"{labels[idx]}: {score:.4f}")
-
-        return "\n".join(lines)
-
+            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: {str(e)}"
+        return f'❌ Error processing video: {str(e)}'
 
-# --- Gradio UI ---
 demo = gr.Interface(
     fn=analyze_video,
-    inputs=gr.Video(label="Upload Video", height=300, type="filepath"),
-    outputs=gr.Textbox(label="Top-5 Predictions", lines=10),
-    title="🎬 GenVidBench - TSN Action Recognition",
-    description="""
-    Upload a video and run **TSN (Temporal Segment Networks, ResNet-50 backbone)**
-    trained on **Kinetics-400**.  
-    Model: `tsn_r50_8xb32-1x1x8-100e_kinetics400-rgb`  
-    Benchmark accuracy ~80% (GenVidBench).
-    """,
-    examples=[["demo/demo.mp4"]] if os.path.exists("demo/demo.mp4") else None,
+    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,
-    theme=gr.themes.Soft(),
-    allow_flagging="never"
+    flagging_mode='never'
 )
 
-if __name__ == "__main__":
-    print("🌟 Starting GenVidBench TSN Demo...")
-    demo.launch()
+if __name__ == '__main__':
+    demo.launch()