import itertools
import gradio as gr
import requests
import os
from gradio.themes.utils import sizes
def respond(message, history):
if len(message.strip()) == 0:
return "ERROR the question should not be empty"
local_token = os.getenv('API_TOKEN')
local_endpoint = os.getenv('API_ENDPOINT')
if local_token is None or local_endpoint is None:
return "ERROR missing env variables"
# Add your API token to the headers
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {local_token}'
}
prompt = list(itertools.chain.from_iterable(history))
prompt.append(message)
q = {"inputs": [prompt]}
try:
response = requests.post(
local_endpoint, json=q, headers=headers, timeout=100)
response_data = response.json(
)["predictions"]
except Exception as error:
response_data = f"ERROR status_code: {type(error).__name__}"
# + str(response.status_code) + " response:" + response.text
# print(response.json())
return response_data
theme = gr.themes.Soft(
text_size=sizes.text_sm,radius_size=sizes.radius_sm, spacing_size=sizes.spacing_sm,
)
demo = gr.ChatInterface(
respond,
chatbot=gr.Chatbot(show_label=False, container=False, show_copy_button=True, bubble_full_width=True),
textbox=gr.Textbox(placeholder="Ask me a question",
container=False, scale=7),
title="Databricks LLM RAG demo - Chat with llama2 Databricks model serving endpoint",
description="This chatbot is a demo example for the dbdemos llm chatbot.
This content is provided as a LLM RAG educational example, without support. It is using llama2, can hallucinate and should not be used as production content.
Please review our dbdemos license and terms for more details.",
examples=[["How can I start a Databricks cluster?"],
["What is a Databricks Cluster Policy?"],
["How can I track billing usage on my workspaces?"],],
cache_examples=False,
theme=theme,
retry_btn=None,
undo_btn=None,
clear_btn="Clear",
)
if __name__ == "__main__":
demo.launch()