Update app.py

app.py

@@ -13,7 +13,7 @@ from utils import get_md_text_abstract, search_cleaner, get_arxiv_live_search
 
 retrieve_results = 20 
 show_examples = True
-llm_models_to_choose = ['ArXivGPT-Mixtral-8x7B-Instruct-v0.1', 'None']
+llm_models_to_choose = ['mistralai/Mixtral-8x7B-Instruct-v0.1', 'None']
 
 token=os.getenv("HF_TOKEN")
 
@@ -69,15 +69,9 @@ if len(check_arxiv_result) == 0:
 sample_outputs = {
     'output_placeholder': 'The LLM will provide an answer to your question here...',
     'search_placeholder': '''
-1. What is MoE?
-2. What are Multi Agent Systems? 
-3. What is Self Rewarding AI?
-4. What is Semantic and Episodic memory?
-5. What is AutoGen?
-6. What is ChatDev?
-7. What is Omniverse?
-8. What is Lumiere?
-9. What is SORA?
+1. Sora에 대한 논문들 요약해줘
+2. RAG 구성 관련 논문들 분석해줘 
+3. Vision 인식에 대한 최신 경향 분석해줘
 '''
 }
 
@@ -94,7 +88,7 @@ def rag_cleaner(inp):
 
 def get_prompt_text(question, context, formatted = True, llm_model_picked = 'mistralai/Mixtral-8x7B-Instruct-v0.1'):
     if formatted:
-      sys_instruction = f"Context:\n {context} \n 반드시 한글로 답변하라. Given the following scientific paper abstracts, take a deep breath and lets think step by step to answer the question. Cite the titles of your sources when answering, do not cite links or dates."
+      sys_instruction = f"Context:\n {context} \n 반드시 한글로 답변하라. Given the following scientific paper abstracts, take a deep breath and lets think step by step to answer the question. Cite the titles of your sources when answering, do not cite links or dates. 출력은 반드시 한국어(한글)로 하라."
       message = f"Question: {question}"
         
       if 'mistralai' in llm_model_picked: