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_from_messages(messages,
                                 model="gpt-3.5-turbo",
                                 temperature=0,
                                 max_tokens=500):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # this is the degree of randomness of the model's output
        max_tokens=max_tokens, # the maximum number of tokens the model can ouptut
    )
    return response.choices[0].message["content"]

def greet(company, solution, target_customer, problem, features, customer_persona="the target customer"):
    pitch = f"""My company, {company} is developing {solution} to help {target_customer} {problem} with {features}"""
    sys_setup = f"""
Determine the product or solution, the problem being solved, features, target customer that are being discussed in the \
following user prompt. 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.
    """
    messages = [{'role':'system', 'content':"You are " + customer_persona + "."}, {'role':'system', 'content': sys_setup}, {'role':'user','content':pitch}]
    response = get_completion_from_messages(messages, temperature=0)
    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"), gr.Textbox(label="Customer persona", lines=3)], outputs="json")
iface.launch()