Update app.py

app.py

@@ -198,7 +198,7 @@ with gradio.Blocks() as demo:
     model_selector = gradio.Dropdown([(item["name"], item["repo"]) for item in model_options], value="daniel-dona/sparql-model-era-lora-128-qwen3-4b", render=False, interactive=True, label="Model", info="Base model provided as reference, SFT model is trained on generated datasets, GRPO model is reinforced on to of SFT.")
     model_temperature = gradio.Slider(0, 1, render=False, step=0.1, value=0, label="Temperature", info="Ajust model variability, with a value of 0, the model use greedy decoding.")
     sparql_endpoint = gradio.Textbox(value=SPARQL_ENDPOINT, render=False, interactive=True, label="SPARQL endpoint", info="SPARQL endpoint to send the generate queries to fetch results.")
-    model_semantic = gradio.Checkbox(value=True, render=False, interactive=True, label="Enable semantic entity lookup", info="Use embeddings and reranking model to retrieve relevant objects.")
+    model_semantic = gradio.Checkbox(value=False, render=False, interactive=True, label="Enable semantic entity lookup", info="Use embeddings and reranking model to retrieve relevant objects.")
     model_thinking = gradio.Checkbox(value=False, render=False, interactive=True, label="Enable thinking", info="Use thinking mode in the Jinja chat template, mostly for GRPO experiments.")
     nlq_validate = gradio.Checkbox(value=True, render=False, interactive=True, label="Enable query prevalidation", info="Use the model first to determine if the user query is applicable to the ERA KG")