app.py

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 score_model(model_uri, databricks_token, data):
  headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
  }
  data_json = '{"dataframe_split": {"index": [0], "columns": ["prompt", "temperature", "max_tokens"], "data": [["Loop is a company which converts fruit waste into juice to help retail stores reduce food loss by buying up fruits which were designated for trash.", 0.5, 500]]}}'
  print("***data_json: ")

  print(data_json)
  response = requests.request(method='POST', headers=headers, url=model_uri, json=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):
	prompt = f"""
Determine the product or solution, the problem being solved, features, target customer that are being discussed in the \
following text, which is delimited by triple backticks. Then, pretend that you are the target customer. \
State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.\

Format your response as a JSON object with \
'solution', 'problem', 'features', 'target_customer', 'fg_will_use', 'reason_to_use', 'fg_will_pay', 'reason_to_pay' as the keys.\

Text sample: '''{input}'''
	"""
	response = get_completion(prompt)
	return json.dumps(response)

#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()

#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Elevator pitch", lines=3)], outputs="json")
iface.launch()