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(company, solution, target_customer, problem, features):
    pitch = f"""My company, {company} is developing {solution} to help {target_customer} {problem} with {features}"""
    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.\
Give a score for the product.

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', 'score' as the keys.

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

iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Company"), gr.Textbox(label="Solution"), gr.Textbox(label="Customer"), gr.Textbox(label="Problem"),  gr.Textbox(label="Feature")], outputs="json")
iface.launch()