import itertools
import gradio as gr
import requests
import os


def respond(message, history):

    if len(message.strip()) == 0:
        return "ERROR the question should not be empty"


    local_token = os.environ['API_TOKEN']
    local_endpoint = os.environ['API_ENDPOINT']


    # 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:
        response_data = "ERROR status_code:" + \
            str(response.status_code) + " response:" + response.text

    #print(response.json())
    return 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 is an advanced model hosted on Databricks Serving.",
    examples=[["Hello"], ["What is MLflow?"], ["What is Apache Spark?"]],
    cache_examples=False,
    theme="soft",
    retry_btn=None,
    undo_btn=None,
    clear_btn="Clear"
)

if __name__ == "__main__":
    demo.launch()