app.py

import openai
import os
import gradio as gr
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())


openai.api_key  = os.getenv('OPENAI_API_KEY')

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

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.\

Format your response as a JSON object with \
"solution", "problem", "features", "target_customer", "fg_will_use", "reason" as the keys.

Text sample: '''{input}'''
	"""
	response = get_completion(prompt)
	return 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="text")
iface.launch()