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app.py

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+import gradio as gr
+import torch
+from pytorchvideo.data.encoded_video import EncodedVideo
+from pytorchvideo.transforms import UniformTemporalSubsample
+from transformers import VideoMAEForVideoClassification
+import torch.nn.functional as F
+import torchvision.transforms.functional as F_t  # Changed import
+
+# Check device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load pre-trained model
+model_path = "model"
+loaded_model = VideoMAEForVideoClassification.from_pretrained(model_path)
+loaded_model = loaded_model.to(device)
+loaded_model.eval()
+
+# Label names for prediction
+label_names = [
+    'Archery', 'BalanceBeam', 'BenchPress', 'ApplyEyeMakeup', 'BasketballDunk',
+    'BandMarching', 'BabyCrawling', 'ApplyLipstick', 'BaseballPitch', 'Basketball'
+]
+
+def load_video(video_path):
+    try:
+        video = EncodedVideo.from_path(video_path)
+        video_data = video.get_clip(start_sec=0, end_sec=video.duration)
+        return video_data['video']
+    except Exception as e:
+        raise ValueError(f"Error loading video: {str(e)}")
+
+def preprocess_video(video_frames):
+    try:
+        # Temporal subsampling
+        transform_temporal = UniformTemporalSubsample(16)
+        video_frames = transform_temporal(video_frames)
+        
+        # Convert to float and normalize to [0, 1]
+        video_frames = video_frames.float() / 255.0
+
+        # Ensure channel order is [T, C, H, W]
+        if video_frames.shape[0] == 3:
+            video_frames = video_frames.permute(1, 0, 2, 3)
+
+        # Normalize using torchvision's functional transform
+        mean = torch.tensor([0.485, 0.456, 0.406])
+        std = torch.tensor([0.229, 0.224, 0.225])
+        
+        for t in range(video_frames.shape[0]):
+            video_frames[t] = F_t.normalize(video_frames[t], mean, std)
+
+        # Resize frames
+        video_frames = torch.stack([
+            F_t.resize(frame, [224, 224], antialias=True) 
+            for frame in video_frames
+        ])
+        
+        # Add batch dimension
+        video_frames = video_frames.unsqueeze(0)
+        return video_frames
+    except Exception as e:
+        raise ValueError(f"Error preprocessing video: {str(e)}")
+
+def predict_video(video):
+    try:
+        # Load and preprocess video
+        video_data = load_video(video)
+        processed_video = preprocess_video(video_data)
+        processed_video = processed_video.to(device)
+
+        # Make predictions
+        with torch.no_grad():
+            outputs = loaded_model(processed_video)
+            logits = outputs.logits
+            probabilities = F.softmax(logits, dim=-1)[0]
+            top_3 = torch.topk(probabilities, 3)
+
+        # Format results
+        results = [
+            f"{label_names[idx.item()]}: {prob.item():.2%}"
+            for idx, prob in zip(top_3.indices, top_3.values)
+        ]
+        return "\n".join(results)
+    except Exception as e:
+        return f"Error processing video: {str(e)}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Textbox(label="Top 3 Predictions"),
+    title="Video Action Recognition",
+    description="Upload a video to classify the action being performed. The model will return the top 3 predictions.",
+    examples=[
+        ["test_video_1.avi"],
+        ["test_video_2.avi"],
+        ["test_video_3.avi"]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch(debug=True, share=True)