import numpy as np
import pandas as pd
import requests
import os
import gradio as gr
import json
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())


databricks_token  = os.getenv('DATABRICKS_TOKEN')
model_uri  = "https://dbc-eb788f31-6c73.cloud.databricks.com/serving-endpoints/Mpt-7b-tester/invocations"

def extract_json(gen_text):
    start_index = gen_text.index("### Response:\n{") + 14
    end_index = gen_text.index("}\n\n### End") + 1
    return gen_text[start_index:end_index]

def score_model(model_uri, databricks_token, prompt):
  dataset=pd.DataFrame({
            "prompt":[prompt],
            "temperature": [0.5],
            "max_tokens": [500]})
  headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
  }
  ds_dict = {'dataframe_split': dataset.to_dict(orient='split')} if isinstance(dataset, pd.DataFrame) else create_tf_serving_json(dataset)
  data_json = json.dumps(ds_dict, allow_nan=True)
  print("***ds_dict: ")
  print(ds_dict)
  print("***data_json: ")
  print(data_json)
  response = requests.request(method='POST', headers=headers, url=model_uri, data=data_json)
  if response.status_code != 200:
      raise Exception(f"Request failed with status {response.status_code}, {response.text}")
  return response.json()

def get_completion(prompt):
    return score_model(model_uri, databricks_token, prompt)

def greet(input):
	response = get_completion(input)
	gen_text = response["predictions"][0]["generated_text"]

	return extract_json(gen_text)
	#return json.dumps(response)

iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Prompt", lines=3)], outputs="text")
iface.launch()