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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 = json.dumps({"dataframe_split": {"index": [0], "columns": ["prompt", "temperature", "max_tokens"], "data": [[data, 0.5, 500]]}})
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()
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