LLM_Chatbot / app.py
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Update app.py
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import gradio as gr
from huggingface_hub import InferenceClient
import openai
import anthropic
import os
# Cohere Command R+ 모델 ID 정의
COHERE_MODEL = "CohereForAI/c4ai-command-r-plus-08-2024"
def get_client(model_name: str):
"""
모델 이름에 맞춰 InferenceClient 생성.
HuggingFace 토큰은 os.environ.get("HF_TOKEN")을 통해 환경변수로 가져온다.
"""
hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
raise ValueError("HuggingFace API 토큰이 필요합니다. (환경변수 HF_TOKEN 미설정)")
if model_name == "Cohere Command R+":
model_id = COHERE_MODEL
else:
raise ValueError("유효하지 않은 모델 이름입니다.")
return InferenceClient(model_id, token=hf_token)
def cohere_respond(
message,
chat_history,
system_message,
max_tokens,
temperature,
top_p,
):
"""
Cohere Command R+ 모델 응답 함수.
HF 토큰은 함수 내부에서 os.environ을 통해 불러온다.
"""
model_name = "Cohere Command R+"
try:
client = get_client(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:
if human:
messages.append({"role": "user", "content": human})
if assistant:
messages.append({"role": "assistant", "content": assistant})
messages.append({"role": "user", "content": message})
try:
response_full = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
assistant_message = response_full.choices[0].message.content
chat_history.append((message, assistant_message))
return chat_history
except Exception as e:
error_message = f"오류가 발생했습니다: {str(e)}"
chat_history.append((message, error_message))
return chat_history
def chatgpt_respond(
message,
chat_history,
system_message,
max_tokens,
temperature,
top_p,
):
"""
ChatGPT 모델 응답 함수.
OpenAI 토큰은 함수 내부에서 os.environ을 통해 불러온다.
"""
openai_token = os.environ.get("OPENAI_TOKEN")
if not openai_token:
chat_history.append((message, "OpenAI API 토큰이 필요합니다. (환경변수 OPENAI_TOKEN 미설정)"))
return chat_history
openai.api_key = openai_token # 환경변수에서 받은 토큰 사용
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:
response = openai.ChatCompletion.create(
model="gpt-4o-mini", # 또는 다른 모델 ID 사용
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
assistant_message = response.choices[0].message['content']
chat_history.append((message, assistant_message))
return chat_history
except Exception as e:
error_message = f"오류가 발생했습니다: {str(e)}"
chat_history.append((message, error_message))
return chat_history
def claude_respond(
message,
chat_history,
system_message,
max_tokens,
temperature,
top_p,
):
"""
Claude 모델 응답 함수.
Claude 토큰은 함수 내부에서 os.environ을 통해 불러온다.
"""
claude_token = os.environ.get("CLAUDE_TOKEN")
if not claude_token:
chat_history.append((message, "Claude API 토큰이 필요합니다. (환경변수 CLAUDE_TOKEN 미설정)"))
return chat_history
try:
client = anthropic.Anthropic(api_key=claude_token)
response = client.messages.create(
model="claude-3-haiku-20240307",
max_tokens=max_tokens,
temperature=temperature,
system=system_message,
messages=[
{
"role": "user",
"content": message
}
]
)
assistant_message = response.content[0].text
chat_history.append((message, assistant_message))
return chat_history
except Exception as e:
error_message = f"오류가 발생했습니다: {str(e)}"
chat_history.append((message, error_message))
return chat_history
def deepseek_respond(
message,
chat_history,
system_message,
deepseek_model_choice,
max_tokens,
temperature,
top_p,
):
"""
DeepSeek 모델 응답 함수.
DeepSeek 토큰은 함수 내부에서 os.environ을 통해 불러온다.
deepseek_model_choice에 따라 deepseek-chat 또는 deepseek-reasoner를 선택하며,
스트리밍 방식으로 응답을 받아옵니다.
"""
deepseek_token = os.environ.get("DEEPSEEK_TOKEN")
if not deepseek_token:
chat_history.append((message, "DeepSeek API 토큰이 필요합니다. (환경변수 DEEPSEEK_TOKEN 미설정)"))
yield chat_history
return
openai.api_key = deepseek_token
openai.api_base = "https://api.deepseek.com/v1"
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})
# 모델 선택: 기본은 deepseek-chat
if deepseek_model_choice == "R1(deepseek-reasoner)":
model = "deepseek-reasoner"
else:
model = "deepseek-chat"
try:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True
)
assistant_message = ""
# 새로운 대화 항목을 추가하고 초기값을 스트리밍하면서 갱신
chat_history.append((message, assistant_message))
yield chat_history
for chunk in response:
# "content"가 None인 경우 빈 문자열로 처리하여 오류 방지
delta = chunk.choices[0].delta.get("content") or ""
assistant_message += delta
chat_history[-1] = (message, assistant_message)
yield chat_history
return
except Exception as e:
error_message = f"오류가 발생했습니다: {str(e)}"
chat_history.append((message, error_message))
yield chat_history
return
def clear_conversation():
return []
# --------------------------------------------
# Gradio 앱 시작
# --------------------------------------------
with gr.Blocks() as demo:
gr.Markdown("# Prompting AI Chatbot")
gr.Markdown("언어모델별 프롬프트 테스트 챗봇입니다.")
# --------------------------------------------------
# 일반 모델 관련 UI/기능 제거 (요청 사항에 따라 삭제)
# --------------------------------------------------
# Cohere Command R+
with gr.Tab("Cohere Command R+"):
with gr.Row():
cohere_system_message = gr.Textbox(
value="""반드시 한글로 답변할 것.
너는 최고의 비서이다.
내가 요구하는것들을 최대한 자세하고 정확하게 답변하라.
""",
label="System Message",
lines=3
)
cohere_max_tokens = gr.Slider(minimum=100, maximum=8000, value=2000, step=100, label="Max new tokens")
cohere_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
cohere_top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-P",
)
cohere_chatbot = gr.Chatbot(height=600)
cohere_msg = gr.Textbox(label="메세지를 입력하세요")
with gr.Row():
cohere_submit_button = gr.Button("전송")
cohere_clear_button = gr.Button("대화 내역 지우기")
inputs_for_cohere = [
cohere_msg,
cohere_chatbot,
cohere_system_message,
cohere_max_tokens,
cohere_temperature,
cohere_top_p
]
cohere_msg.submit(cohere_respond, inputs_for_cohere, cohere_chatbot)
cohere_submit_button.click(cohere_respond, inputs_for_cohere, cohere_chatbot)
cohere_clear_button.click(clear_conversation, outputs=cohere_chatbot, queue=False)
# ChatGPT
with gr.Tab("ChatGPT"):
with gr.Row():
chatgpt_system_message = gr.Textbox(
value="""반드시 한글로 답변할 것.
너는 ChatGPT, OpenAI에서 개발한 언어 모델이다.
내가 요구하는 것을 최대한 자세하고 정확하게 답변하라.
""",
label="System Message",
lines=3
)
chatgpt_max_tokens = gr.Slider(minimum=100, maximum=5000, value=2000, step=100, label="Max Tokens")
chatgpt_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature")
chatgpt_top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-P",
)
chatgpt_chatbot = gr.Chatbot(height=600)
chatgpt_msg = gr.Textbox(label="메세지를 입력하세요")
with gr.Row():
chatgpt_submit_button = gr.Button("전송")
chatgpt_clear_button = gr.Button("대화 내역 지우기")
inputs_for_chatgpt = [
chatgpt_msg,
chatgpt_chatbot,
chatgpt_system_message,
chatgpt_max_tokens,
chatgpt_temperature,
chatgpt_top_p
]
chatgpt_msg.submit(chatgpt_respond, inputs_for_chatgpt, chatgpt_chatbot)
chatgpt_submit_button.click(chatgpt_respond, inputs_for_chatgpt, chatgpt_chatbot)
chatgpt_clear_button.click(clear_conversation, outputs=chatgpt_chatbot, queue=False)
# Claude
with gr.Tab("Claude"):
with gr.Row():
claude_system_message = gr.Textbox(
value="""반드시 한글로 답변할 것.
너는 Anthropic에서 개발한 클로드이다.
최대한 정확하고 친절하게 답변하라.
""",
label="System Message",
lines=3
)
claude_max_tokens = gr.Slider(minimum=100, maximum=8000, value=2000, step=100, label="Max Tokens")
claude_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature")
claude_top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-P",
)
claude_chatbot = gr.Chatbot(height=600)
claude_msg = gr.Textbox(label="메세지를 입력하세요")
with gr.Row():
claude_submit_button = gr.Button("전송")
claude_clear_button = gr.Button("대화 내역 지우기")
inputs_for_claude = [
claude_msg,
claude_chatbot,
claude_system_message,
claude_max_tokens,
claude_temperature,
claude_top_p
]
claude_msg.submit(claude_respond, inputs_for_claude, claude_chatbot)
claude_submit_button.click(claude_respond, inputs_for_claude, claude_chatbot)
claude_clear_button.click(clear_conversation, outputs=claude_chatbot, queue=False)
# DeepSeek
with gr.Tab("DeepSeek"):
with gr.Row():
deepseek_system_message = gr.Textbox(
value="""반드시 한글로 답변할 것.
너는 DeepSeek-V3, 최고의 언어 모델이다.
내가 요구하는 것을 최대한 자세하고 정확하게 답변하라.
""",
label="System Message",
lines=3
)
deepseek_model_choice = gr.Radio(
choices=["V3(deepseek-chat)", "R1(deepseek-reasoner)"],
value="V3(deepseek-chat)",
label="모델 선택"
)
deepseek_max_tokens = gr.Slider(minimum=100, maximum=8000, value=2000, step=100, label="Max Tokens")
deepseek_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature")
deepseek_top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-P",
)
deepseek_chatbot = gr.Chatbot(height=600)
deepseek_msg = gr.Textbox(label="메세지를 입력하세요")
with gr.Row():
deepseek_submit_button = gr.Button("전송")
deepseek_clear_button = gr.Button("대화 내역 지우기")
inputs_for_deepseek = [
deepseek_msg,
deepseek_chatbot,
deepseek_system_message,
deepseek_model_choice,
deepseek_max_tokens,
deepseek_temperature,
deepseek_top_p
]
# Textbox.submit에서는 stream 인자를 제거합니다.
deepseek_msg.submit(deepseek_respond, inputs_for_deepseek, deepseek_chatbot)
deepseek_submit_button.click(deepseek_respond, inputs_for_deepseek, deepseek_chatbot)
deepseek_clear_button.click(clear_conversation, outputs=deepseek_chatbot, queue=False)
if __name__ == "__main__":
demo.launch()