Update app.py

app.py

@@ -1,6 +1,7 @@
 from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext
 from llama_index_llms_huggingface import HuggingFaceLLM
-from llama_index.prompts.prompts import SimpleInputPrompt
+from llama_index.core import PromptTemplate
+
 import os
 from dotenv import load_dotenv
 from huggingface_hub import login
@@ -12,15 +13,31 @@ from llama_index.embeddings import LangchainEmbedding
 
 # Get the environment variables
 HF_TOKEN = os.getenv('HUGGING_FACE_TOKEN')
+login(token=HF_TOKEN)
 
 documents=SimpleDirectoryReader("/state-of-the-union.txt").load_data()
+
 system_prompt="""
 You are a Q&A assistant. Your goal is to answer questions as
 accurately as possible based on the instructions and context provided.
 """
 ## Default format supportable by LLama2
-query_wrapper_prompt=SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
-login(token=HF_TOKEN)
+template = (
+    "We have provided context information below. \n"
+    "---------------------\n"
+    "{context_str}"
+    "\n---------------------\n"
+    "Given this information, please answer the question: {query_str}\n"
+)
+#query_wrapper_prompt=SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
+
+query_wrapper_prompt = PromptTemplate(template)
+
+# you can create text prompt (for completion API)
+#prompt = qa_template.format(context_str=..., query_str=...)
+
+# or easily convert to message prompts (for chat API)
+#messages = qa_template.format_messages(context_str=..., query_str=...)
 
 llm = HuggingFaceLLM(
     context_window=4096,