extract result from llm response

app.py

@@ -76,7 +76,8 @@ class RAGDemo(object):
             chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
             verbose=True,
         )
-        return basic_qa.invoke(input_text)
+        resp = basic_qa.invoke(input_text)
+        return resp['result']
 
     def __call__(self):
         with gr.Blocks() as demo:
@@ -87,7 +88,7 @@ class RAGDemo(object):
                     with gr.Column():
                         model_name = gr.Dropdown(
                             choices=['gemini-1.0-pro'],
-                            value='glm-3-turbo',
+                            value='gemini-1.0-pro',
                             label="model"
                         )
                         api_key = gr.Textbox(placeholder="your api key for LLM", label="api key")