app.py

import openai
import os
import gradio as gr
import json
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"""
Recommend complementary shops specifically for the shop(s) described in the following text, ranked by synergy: \
Text: ```{input}```
"""

response = get_completion(prompt)
print(response)
	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="Retail Business", lines=3)], outputs="text")
iface.launch()