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import streamlit as st
import os
from mlflow import deployments
from databricks.vector_search.client import VectorSearchClient
DATABRICKS_HOST = os.environ.get("DATABRICKS_HOST")
DATABRICKS_API_TOKEN = os.environ.get("DATABRICKS_API_TOKEN")
VS_ENDPOINT_NAME = os.environ.get("VS_ENDPOINT_NAME")
VS_INDEX_NAME = os.environ.get("VS_INDEX_NAME")
if DATABRICKS_HOST is None:
raise ValueError("DATABRICKS_HOST environment variable must be set")
if DATABRICKS_API_TOKEN is None:
raise ValueError("DATABRICKS_API_TOKEN environment variable must be set")
TITLE = "VUMC Chatbot"
DESCRIPTION="The first generation VUMC chatbot with knowledge of Vanderbilt specific terms."
EXAMPLE_PROMPTS = [
"Write a short story about a robot that has a nice day.",
"In a table, what are some of the most common misconceptions about birds?",
"Give me a recipe for vegan banana bread.",
"Code a python function that can run merge sort on a list.",
"Give me the character profile of a gumdrop obsessed knight in JSON.",
"Write a rap battle between Alan Turing and Claude Shannon.",
]
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(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)
# 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()
# with main:
# history = st.container(height=400)
# with history:
# for message in st.session_state["messages"]:
# avatar = None
# if message["role"] == "assistant":
# avatar = MODEL_AVATAR_URL
# with st.chat_message(message["role"],avatar=avatar):
# if message["content"] is not None:
# st.markdown(message["content"])
# if message["error"] is not None:
# st.error(message["error"],icon="🚨")
# if message["warning"] is not None:
# st.warning(message["warning"],icon="⚠️")
# if prompt := st.chat_input("Type a message!", max_chars=1000):
# handle_user_input(prompt)
# st.markdown("\n") #add some space for iphone users
# with st.sidebar:
# with st.container():
# st.title("Examples")
# for prompt in EXAMPLE_PROMPTS:
# st.button(prompt, args=(prompt,), on_click=handle_user_input) |