Update app.py

app.py

@@ -1,102 +1,86 @@
-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)
+import gradio as gr
+import torch
+from pytorchvideo.data.encoded_video import EncodedVideo
+from torchvision.transforms import Resize
+from pytorchvideo.transforms import UniformTemporalSubsample
+from transformers import VideoMAEForVideoClassification
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model_path = "model"
+loaded_model = VideoMAEForVideoClassification.from_pretrained(model_path)
+loaded_model = loaded_model.to(device)
+loaded_model.eval()
+
+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:
+        transform_temporal = UniformTemporalSubsample(16)
+        video_frames = transform_temporal(video_frames)
+        video_frames = video_frames / 255.0
+
+        if video_frames.shape[0] == 3:
+            video_frames = video_frames.permute(1, 0, 2, 3)
+        
+        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] = (video_frames[t] - mean[:, None, None]) / std[:, None, None]
+
+        resize_transform = Resize((224, 224))
+        video_frames = resize_transform(video_frames)
+        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:
+        video_path = video.name
+        video_data = load_video(video_path)
+        processed_video = preprocess_video(video_data)
+        processed_video = processed_video.to(device)
+        
+        with torch.no_grad():
+            outputs = loaded_model(processed_video)
+            logits = outputs.logits
+            probabilities = torch.nn.functional.softmax(logits, dim=-1)[0]
+            top_3 = torch.topk(probabilities, 3)
+            
+        results = []
+        for i in range(3):
+            idx = top_3.indices[i].item()
+            prob = top_3.values[i].item()
+            results.append(f"{label_names[idx]}: {prob*100:.2f}%")
+            
+        return "\n".join(results)
+    except Exception as e:
+        return f"Error processing video: {str(e)}"
+
+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 with their probabilities.",
+    examples=[
+        ["test_video_1.mp4"],
+        ["test_video_2.mp4"],
+        ["test_video_3.mp4"]
+    ]
+)
+
+iface.launch(debug=True, share=True)