remove env var testing

app.py

@@ -27,19 +27,9 @@ EXAMPLE_PROMPTS = [
 st.set_page_config(layout="wide")
 st.title(TITLE)
 
-# test env vars get output correctly - do we need to configure PAT access further?
-st.markdown(DESCRIPTION)
-st.markdown("\n")
-st.write(DATABRICKS_HOST)
-st.markdown("\n")
-st.write(DATABRICKS_TOKEN)
-st.markdown("\n")
-st.write(VS_ENDPOINT_NAME)
-st.markdown("\n")
-
 # use this to format later
-# with open("style.css") as css:
-#     st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)
+with open("style.css") as css:
+    st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)
 
 # Same embedding model we used to create embeddings of terms
 # make sure we cache this so that it doesnt redownload each time, hindering Space start time if sleeping
@@ -56,33 +46,6 @@ vector_store = DatabricksVectorSearch(
 results = vector_store.similarity_search(query="Tell me about what a data lake is.", k=5)
 st.write(results)
 
-
-
-# TODO *** configure to run only on prompt for verification?
-# vsc = VectorSearchClient()
-
-# question = "What is the data lake?"
-# # question_2 = "What does EDW stand for?"
-# # question_3 = "What does AIDET stand for?"
-
-# deploy_client = deployments.get_deploy_client("databricks")
-# response = deploy_client.predict(endpoint="databricks-bge-large-en", inputs={"input": [question]})
-# embeddings = [e['embedding'] for e in response.data]
-
-# results = vsc.get_index(VS_ENDPOINT_NAME, VS_INDEX_NAME).similarity_search(
-#   query_vector=embeddings[0],
-#   columns=["name", "description"],
-#   num_results=5)
-
-# st.write(results)
-
-
-# print(results)
-# print("---------------------------------------")
-# vumc_terms = results.get('result', {}).get('data_array', [])
-# print(vumc_terms)
-
-
 # DBRX mainbody minus functions
 
 # main = st.container()