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import gradio as gr
from huggingface_hub import InferenceClient
import os
import openai # OpenAI API 클라이언트 추가
# OpenAI API 키 설정
openai.api_key = os.getenv("OPENAI_API_KEY")
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 Phi-3-mini-4k": "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",
"Cohere Aya-23-35B": "CohereForAI/aya-23-35B",
"ChatGPT-4o-mini": "gpt-4o-mini" # ChatGPT-4o-mini 모델 추가
}
def get_client(model_name):
if model_name == "ChatGPT-4o-mini":
return None # OpenAI API는 따로 클라이언트를 생성할 필요 없음
model_id = MODELS[model_name]
hf_token = os.getenv("HF_TOKEN")
if not hf_token:
raise ValueError("HF_TOKEN environment variable is required")
return InferenceClient(model_id, token=hf_token)
def call_openai_api(content, system_message, max_tokens, temperature, top_p):
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": content},
],
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
return response.choices[0].message['content']
def respond(
message,
chat_history,
model_name,
max_tokens,
temperature,
top_p,
system_message,
):
try:
if model_name == "ChatGPT-4o-mini":
assistant_message = call_openai_api(message, system_message, max_tokens, temperature, top_p)
chat_history.append((message, assistant_message))
yield chat_history
else:
client = get_client(model_name)
if client is None:
raise ValueError(f"No client available for model: {model_name}")
except ValueError as e:
chat_history.append((message, str(e)))
return chat_history
messages = [{"role": "system", "content": system_message}]
for human, assistant in chat_history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": assistant})
messages.append({"role": "user", "content": message})
try:
if "Cohere" in model_name:
response = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
assistant_message = response.choices[0].message.content
chat_history.append((message, assistant_message))
yield chat_history
else:
stream = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
)
partial_message = ""
for response in stream:
if response.choices[0].delta.content is not None:
partial_message += response.choices[0].delta.content
if len(chat_history) > 0 and chat_history[-1][0] == message:
chat_history[-1] = (message, partial_message)
else:
chat_history.append((message, partial_message))
yield chat_history
except Exception as e:
error_message = f"An error occurred: {str(e)}"
chat_history.append((message, error_message))
yield chat_history
def clear_conversation():
return []
with gr.Blocks() as demo:
gr.Markdown("# Prompting AI Chatbot")
gr.Markdown("언어모델별 프롬프트 테스트 챗봇입니다.")
with gr.Row():
with gr.Column(scale=1):
model_name = gr.Radio(
choices=list(MODELS.keys()),
label="Language Model",
value="Zephyr 7B Beta"
)
max_tokens = gr.Slider(minimum=0, maximum=2000, value=500, step=100, label="Max Tokens")
temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
system_message = gr.Textbox(
value="""반드시 한글로 답변할 것.
너는 최고의 비서이다.
내가 요구하는것들을 최대한 자세하고 정확하게 답변하라.
""",
label="System Message",
lines=3
)
with gr.Column(scale=2):
chatbot = gr.Chatbot()
msg = gr.Textbox(label="메세지를 입력하세요")
with gr.Row():
submit_button = gr.Button("전송")
clear_button = gr.Button("대화 내역 지우기")
msg.submit(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot)
submit_button.click(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot)
clear_button.click(clear_conversation, outputs=chatbot, queue=False)
if __name__ == "__main__":
demo.launch()
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