Update app.py

app.py

@@ -844,7 +844,7 @@ with gr.Blocks(title="LLM QA Chatbot Builder") as demo:
         saved_params = load_params_from_file()
     # If saved parameters exist, use them; otherwise, set default values
         default_model_name = saved_params['model_name'] if saved_params else "Llama"
-        default_embedding_name = saved_params['embedding_name'] if saved_params else "BAAI/bge-base-en-v1.5"
+        default_embedding_name = saved_params['embedding_name'] if saved_params else "sentence-transformers/all-mpnet-base-v2"
         default_splitter_type = saved_params['splitter_type_dropdown'] if saved_params else "character"
         default_chunk_size = saved_params['chunk_size_slider'] if saved_params else 500
         default_chunk_overlap = saved_params['chunk_overlap_slider'] if saved_params else 30
@@ -855,8 +855,9 @@ with gr.Blocks(title="LLM QA Chatbot Builder") as demo:
             def login_hug(token):
                 from huggingface_hub import login
                 login(token=token)
-            login_hug(os.getenv('HF_TOKEN'))
-            embedding_name=gr.Dropdown(choices=["BAAI/bge-base-en-v1.5","dunzhang/stella_en_1.5B_v5","dunzhang/stella_en_400M_v5",
+            token_tb=gr.Textbox(label="Enter your Huggingface token to access Huggingface models")
+            token_tb.change(login_hug,token_tb,None)
+            embedding_name=gr.Dropdown(choices=["sentence-transformers/all-mpnet-base-v2", "BAAI/bge-base-en-v1.5","dunzhang/stella_en_1.5B_v5","dunzhang/stella_en_400M_v5",
                                                 "nvidia/NV-Embed-v2","Alibaba-NLP/gte-Qwen2-1.5B-instruct"],value=default_embedding_name,
                                     label="Select the Embedding Model")
             splitter_type_dropdown = gr.Dropdown(choices=["character", "recursive", "token"],