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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=1)
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()
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