Update app.py

app.py

@@ -8,8 +8,7 @@ from langchain.schema import Document
 from langchain_community.vectorstores import Qdrant
 from langchain.chains import RetrievalQA
 from langchain.llms import HuggingFaceHub
-
-# Set up the RetrievalQA model
+hf_token=os.environ["HUGGINGFACEHUB_API_TOKEN"]
 with open("brookline_data.txt", "r") as f:
     data = f.read()
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)
@@ -17,7 +16,7 @@ splitted_data = text_splitter.split_text(data)
 embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
 retriever = Qdrant.as_retriever()
 
-llm = HuggingFaceHub(repo_id="ahmadmac/Trained-T5-large", model_kwargs={"temperature": 0.5, "max_length": 512},huggingfacehub_api_token=)
+llm = HuggingFaceHub(repo_id="ahmadmac/Trained-T5-large", model_kwargs={"temperature": 0.5, "max_length": 512},huggingfacehub_api_token=hf_token)
 qna = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
 prompt_template = """ you are a highly knowledgeable AI assistant. Engage in a conversation with the user. Your main goal is to provide clear and informative answers to the user's questions.
 User: {question}