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import gradio as gr
import requests
import os
def respond(message, history, endpoint, token, temp, max_token=0):
if len(message.strip()) == 0:
return "ERROR the question should not be empty"
if len(token.strip()) > 0 and len(endpoint.strip()) > 0:
local_token = token
local_endpoint = endpoint
custom_message = "[*] "
else:
local_token = os.environ['API_TOKEN']
local_endpoint = os.environ['API_ENDPOINT']
custom_message = ""
# Add your API token to the headers
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {local_token}'
}
q = {"inputs": {"prompt": ["Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n" +
message + "\n\n### Response:\n"], "max_tokens": [int(max_token)], "temperature": [int(temp)]}}
try:
response = requests.post(local_endpoint, json=q, headers=headers, timeout=100)
response_data = response.json(
)["predictions"][0]["candidates"][0]["text"]
except:
response_data = "ERROR status_code:" + \
str(response.status_code) + " response:" + response.text
return custom_message + response_data
demo = gr.ChatInterface(
respond,
chatbot=gr.Chatbot(height=400),
textbox=gr.Textbox(placeholder="Ask me a question",
container=False, scale=7),
title="Chat with a Databricks LLM serving endpoint",
description="This a MPT-7b model",
examples=[["Hello"], ["What is MLflow?"], ["What is Apache Spark?"]],
cache_examples=False,
theme="soft",
retry_btn=None,
undo_btn=None,
clear_btn="Clear",
additional_inputs=[
gr.Textbox(label="Custom Endpoint", type="text",
placeholder="https://XXXXXX.cloud.databricks.com/serving-endpoints/XXXXX/invocations"),
gr.Textbox(label="Custom Token", type="password",
placeholder="dapiXXXXXXXXXX"),
gr.Slider(0, 100, label="Temp", value=0),
gr.Slider(0, 100, label="Max token", value=75)
],
additional_inputs_accordion_name="Settings"
)
if __name__ == "__main__":
demo.launch()
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