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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]}
q = {"inputs": [message]}
try:
response = requests.post(
local_endpoint, json=q, headers=headers, timeout=100)
response_data = response.json()
#print(response_data)
response_data=response_data["predictions"][0]
#print(response_data)
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 DBRX Databricks model serving endpoint",
description="This chatbot is a demo example for the dbdemos llm chatbot. <br>This content is provided as a LLM RAG educational example, without support. It is using DBRX, can hallucinate and should not be used as production content.<br>Please review our dbdemos license and terms for more details.",
examples=[["What is DBRX?"],
["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()
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