|
|
|
|
|
import os |
|
|
from dotenv import load_dotenv |
|
|
from openai import OpenAI |
|
|
import requests |
|
|
import gradio as gr |
|
|
|
|
|
load_dotenv() |
|
|
aaa = os.getenv("URL") |
|
|
if not aaa: |
|
|
raise ValueError("νκ²½ λ³μκ° μ€μ λμ§ μμμ΅λλ€.") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
client = OpenAI() |
|
|
|
|
|
def get_and_summarize_data(): |
|
|
""" |
|
|
λ°μ΄ν°λ₯Ό κ°μ Έμ OpenAIλ₯Ό μ¬μ©νμ¬ μμ½νλ ν¨μ |
|
|
""" |
|
|
url = aaa |
|
|
response = requests.get(url) |
|
|
data_text = response.text |
|
|
|
|
|
prompt = f""" |
|
|
λ€μ λ΄μ©μ μμ½νκ³ λ³΄κΈ° μ’λλ‘ μ€ λ°κΏμ μ μ©νμ¬ μΆλ ₯ν΄μ€ λ΄μ©: |
|
|
{data_text} |
|
|
""" |
|
|
|
|
|
openai_response = client.responses.create( |
|
|
model="gpt-4.1-nano", |
|
|
input=prompt |
|
|
) |
|
|
|
|
|
return openai_response.output_text |
|
|
|
|
|
|
|
|
def gradio_interface(): |
|
|
summarized_data = get_and_summarize_data() |
|
|
return summarized_data |
|
|
|
|
|
demo = gr.Interface( |
|
|
fn=gradio_interface, |
|
|
inputs=[], |
|
|
outputs=["text"], |
|
|
title="λ°μ΄ν° μμ½ κ²°κ³Ό" |
|
|
) |
|
|
|
|
|
|
|
|
demo.launch() |