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import gradio as gr | |
from huggingface_hub import InferenceClient | |
import os | |
import random | |
import logging | |
# 로깅 설정 | |
logging.basicConfig(filename='language_model_playground.log', level=logging.DEBUG, | |
format='%(asctime)s - %(levelname)s - %(message)s') | |
# 모델 목록 | |
MODELS = { | |
"Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta", | |
"DeepSeek Coder V2": "deepseek-ai/DeepSeek-Coder-V2-Instruct", | |
"Meta Llama 3.1 8B": "meta-llama/Meta-Llama-3.1-8B-Instruct", | |
"Meta-Llama 3.1 70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct", | |
"Microsoft": "microsoft/Phi-3-mini-4k-instruct", | |
"Mixtral 8x7B": "mistralai/Mistral-7B-Instruct-v0.3", | |
"Mixtral Nous-Hermes": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", | |
"Cohere Command R+": "CohereForAI/c4ai-command-r-plus", | |
"Aya-23-35B": "CohereForAI/aya-23-35B" | |
} | |
# HuggingFace 토큰 설정 | |
hf_token = os.getenv("HF_TOKEN") | |
if not hf_token: | |
raise ValueError("HF_TOKEN 환경 변수가 설정되지 않았습니다.") | |
def call_hf_api(prompt, reference_text, max_tokens, temperature, top_p, model): | |
client = InferenceClient(model=model, token=hf_token) | |
combined_prompt = f"{prompt}\n\n참고 텍스트:\n{reference_text}" | |
random_seed = random.randint(0, 1000000) | |
try: | |
response = client.text_generation( | |
combined_prompt, | |
max_new_tokens=max_tokens, | |
temperature=temperature, | |
top_p=top_p, | |
seed=random_seed | |
) | |
return response | |
except Exception as e: | |
logging.error(f"HuggingFace API 호출 중 오류 발생: {str(e)}") | |
return f"응답 생성 중 오류 발생: {str(e)}. 나중에 다시 시도해 주세요." | |
def generate_response(prompt, reference_text, max_tokens, temperature, top_p, model): | |
response = call_hf_api(prompt, reference_text, max_tokens, temperature, top_p, MODELS[model]) | |
response_html = f""" | |
<h3>생성된 응답:</h3> | |
<div style='max-height: 500px; overflow-y: auto; white-space: pre-wrap; word-wrap: break-word;'> | |
{response} | |
</div> | |
""" | |
return response_html | |
# Gradio 인터페이스 설정 | |
with gr.Blocks() as demo: | |
gr.Markdown("## 언어 모델 프롬프트 플레이그라운드") | |
with gr.Column(): | |
model_radio = gr.Radio(choices=list(MODELS.keys()), value="Zephyr 7B Beta", label="언어 모델 선택") | |
prompt_input = gr.Textbox(label="프롬프트 입력", lines=5) | |
reference_text_input = gr.Textbox(label="참고 텍스트 입력", lines=5) | |
with gr.Row(): | |
max_tokens_slider = gr.Slider(minimum=0, maximum=5000, value=2000, step=100, label="최대 토큰 수") | |
temperature_slider = gr.Slider(minimum=0, maximum=1, value=0.75, step=0.05, label="온도") | |
top_p_slider = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.05, label="Top P") | |
generate_button = gr.Button("응답 생성") | |
response_output = gr.HTML(label="생성된 응답") | |
# 버튼 클릭 시 응답 생성 | |
generate_button.click( | |
generate_response, | |
inputs=[prompt_input, reference_text_input, max_tokens_slider, temperature_slider, top_p_slider, model_radio], | |
outputs=response_output | |
) | |
# 인터페이스 실행 | |
demo.launch(share=True) |