Update app.py

app.py

@@ -244,10 +244,13 @@ def process_video(video_file):
 
     # Step 2: Generate embeddings (GPU operation)
     status_msg += "\n🔄 Generating embeddings for video clips..."
-    embeddings = gpu_operation("generate_embeddings", clip_paths) # This now generates embeddings on GPU
+    embeddings_result = gpu_operation("generate_embeddings", clip_paths)
     
-    if isinstance(embeddings, str) and "❌" in embeddings: # Check for errors during embedding
-        return embeddings, clips_info, [], [] # Return error message, clips_info, empty embeddings and clip_paths
+    # Check if embeddings_result is an error tuple (string, list)
+    if isinstance(embeddings_result, tuple) and len(embeddings_result) == 2 and isinstance(embeddings_result[0], str) and "❌" in embeddings_result[0]:
+        return embeddings_result[0], clips_info, [], [] # Return error message
+    
+    embeddings = embeddings_result # Assign actual embeddings
 
     status_msg += f"\n✅ Generated embeddings for {len(embeddings)} clips"
     status_msg += "\n🎯 Ready for queries!"
@@ -262,9 +265,9 @@ def search_video_clips(query, video_embeddings_state, video_clips_state):
 
 # Create Gradio interface
 def create_interface():
-    with gr.Blocks(title="Security Camera AI Assistant", theme=gr.themes.Soft()) as demo:
+    with gr.Blocks(title="Security Camera AI Assistant", theme=gr.themes.Ocean()) as demo:
         gr.Markdown("# 🎥 Security Camera AI Assistant")
-        gr.Markdown("Upload security footage. Then search through the video using natural language queries to get relevant clips with precise timestamps!!")
+        gr.Markdown("Upload a video.Then search sing natural language queries, to get relevant clips with precise timestamps!!")
         
         with gr.Row():
             with gr.Column(scale=1):