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import gradio as gr
import requests
import json
API_URL = "https://api.openai.com/v1/chat/completions"
def predict(inputs, top_p, temperature, openai_api_key):
narration_prompt = f"๋์์์ ์ฌ์ฉํ ์ ๋ฌธ์ ์ธ ๋๋ ์ด์
์ ์์ฑํ๋ผ. ๋ฐ๋์ ํ๊ธ๋ก ์์ฑํ ๊ฒ. ์ผ์ฒด์ ์ง๋ฌธ์ด๋ ์ง์, ๋ฐฐ๊ฒฝ ์ค๋ช
๋ฑ์ ๋
ธ์ถ ํ๊ฑฐ๋ ์ถ๋ ฅํ์ง ๋ง๊ณ ์์ํ ๋๋ ์ด์
๋ง 2์ค์ฉ ๋ฌถ์ด์ ์ต๋ 10์ค ์ด๋ด๋ก ์ถ๋ ฅ๋ ฅ. ์
๋ ฅ: '{inputs}'"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {openai_api_key}"
}
payload = {
"model": "gpt-4-1106-preview",
"messages": [{"role": "system", "content": narration_prompt}],
"temperature": temperature,
"top_p": top_p,
"n": 1,
"max_tokens": 1000
}
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 200:
response_data = response.json()
generated_text = response_data['choices'][0]['message']['content']
return generated_text
else:
return "Error: Unable to generate response."
with gr.Blocks() as demo:
gr.Markdown("<h1 align='center'>ํ์ ์คํฌ๋ฆฝํธ</h1>")
with gr.Row():
openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
inputs = gr.Textbox(placeholder="์ฌ๊ธฐ์ ์
๋ ฅํ์ธ์.", label="๋๋ ์ด์
์คํฌ๋ฆฝํธ๋ฅผ ์์ฑํ๊ณ ์ถ์ ์ฃผ์ ์ด๋ ๋ฌธ์ฅ์ ์
๋ ฅํ์ธ์.")
top_p = gr.Slider(minimum=0, maximum=1.0, value=1.0, step=0.05, label="Top-p (nucleus sampling)")
temperature = gr.Slider(minimum=0, maximum=5.0, value=1.0, step=0.1, label="Temperature")
output = gr.Textbox(label="Generated Script", readonly=True)
submit_button = gr.Button("Generate")
submit_button.click(fn=predict, inputs=[inputs, top_p, temperature, openai_api_key], outputs=output)
examples = gr.Examples(examples=[
["์ํ ์ค๋ช
:์๋ก ์ถ์๋ 'ํ ๋ฆฌ' ๋ฆฝ๋ฐค์ FDA ์น์ธ, ์ต๊ณ ์ ๋ณด์ต๋ ฅ, ๊ตฌ๋งค์ง์ 1์"],
["๋ธ๋๋ฉ: 'ํ ๋ฆฌ'๋ฆฝ๋ฐค์ 20๋ ์ฌ์ฑ์๊ฒ ์ดํํ ๋ธ๋๋ฉ์ด ํ์ํด"],
["๊ด๊ณ : ์ค๋ ๋ถ๋ชจ๋๊ณผ ์น์ง ์ ๋ฌผ์ ๋ฒ์ฑํฌ ๋ณด๋ฆฌ๊ตด๋น '๋ฒ์ฑ๊ตด๋น'๊ฐ ์ต๊ณ ๋๋๋ค."],
["์ ๋ณด ๊ณต์ : ๋นํ๋ฏผC ๊ณผ๋ค ๋ณต์ฉ์ ๊ฑด๊ฐ์ ์คํ๋ ค ํด๋กญ๋ค."],
["ํ๋ณด: 'OpenAI'๋ '์ฑGPT'์ ๋ง์ถค GPT '์คํ ์ด'๋ฅผ ์คํํ์๋ค."],
["์ธ์ฌ: '์ ํ ๋ฒ์ธ'์ ๊ณ ๊ฐ๊ณผ ์์ง์์ ์ํ ์ง์ทจ์ ์ธ 2024๋
์ ๋
์ธ์ฌ"]
], inputs=[inputs], fn=predict, outputs=output)
demo.launch() |