Update app.py

app.py

@@ -17,25 +17,35 @@ embeddings = model.encode(data, convert_to_tensor=True)
 
 # Autocomplete function
 def autocomplete(query):
+    if not query.strip():
+        return []  # Return empty if query is blank
     matches = [text for text in data if query.lower() in text.lower()]
     return matches[:5]  # Return top 5 matches
 
 # Semantic search function
 def semantic_search(query):
+    if not query.strip():
+        return []  # Return empty if query is blank
     query_embedding = model.encode(query, convert_to_tensor=True)
     results = util.semantic_search(query_embedding, embeddings, top_k=5)
     return [data[result['corpus_id']] for result in results[0]]
 
 # Define Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("### Autocomplete & Semantic Search")
-    query = gr.Textbox(label="Enter your query")
-    autocomplete_output = gr.Textbox(label="Autocomplete Results")
-    semantic_search_output = gr.Textbox(label="Semantic Search Results")
-
-    def process_query(query):
-        return autocomplete(query), semantic_search(query)
-
-    query.submit(process_query, query, [autocomplete_output, semantic_search_output])
+    gr.Markdown("### Real-Time Autocomplete & Semantic Search")
+    
+    with gr.Row():
+        query = gr.Textbox(label="Start typing for autocomplete", live=True)
+        autocomplete_output = gr.Textbox(label="Autocomplete Suggestions")
+    
+    with gr.Row():
+        semantic_query = gr.Textbox(label="Enter your query for semantic search")
+        semantic_search_output = gr.Textbox(label="Semantic Search Results")
+    
+    # Real-time autocomplete
+    query.change(fn=autocomplete, inputs=query, outputs=autocomplete_output)
+    
+    # Semantic search triggered on submit
+    semantic_query.submit(fn=semantic_search, inputs=semantic_query, outputs=semantic_search_output)
 
 demo.launch()