Update app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import torch
+import os
+import tempfile
+import numpy as np
+from models import Model  # Modify based on your actual model class
+from dataset import extract_features  # Or however you handle input
+from eval import predict  # Assume this runs inference and returns timestamps
+
+# Load model
+def load_model(checkpoint_path='checkpoint/ckp_best.pth.tar'):
+    checkpoint = torch.load(checkpoint_path, map_location='cpu')
+    model = Model(**checkpoint['config'])  # Adjust depending on how your model is initialized
+    model.load_state_dict(checkpoint['state_dict'])
+    model.eval()
+    return model
+
+model = load_model()
+
+def process_video(video_file):
+    # Save uploaded file
+    temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
+    with open(temp_path, "wb") as f:
+        f.write(video_file.read())
+
+    # Optional: convert to features using your function
+    features = extract_features(temp_path)  # Modify if needed
+    # Save to temp .npz file if your pipeline needs it
+    npz_path = temp_path.replace(".mp4", ".npz")
+    np.savez(npz_path, features=features)
+
+    # Predict
+    predictions = predict(model, npz_path)
+
+    # Format output
+    results = "\n".join([
+        f"{label}: {start:.2f}s - {end:.2f}s"
+        for label, start, end in predictions
+    ])
+    
+    os.remove(temp_path)
+    os.remove(npz_path)
+    return results
+
+demo = gr.Interface(
+    fn=process_video,
+    inputs=gr.Video(label="Upload a video"),
+    outputs=gr.Textbox(label="Detected Actions"),
+    title="Temporal Action Localization"
+)
+
+if __name__ == "__main__":
+    demo.launch()