app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os
import logging
from gradio_client import Client  # 이미지 생성 API 클라이언트

# 로깅 설정
logging.basicConfig(level=logging.INFO)

# 환경 변수에서 Hugging Face API 토큰을 가져옵니다.
hf_client = InferenceClient("CohereForAI/c4ai-command-r-plus", token=os.getenv("HF_TOKEN"))

# 이미지 생성 API 클라이언트 설정
client = Client("http://211.233.58.202:7960/")

def respond(message, history, system_message, max_tokens, temperature, top_p):
    # 초기 설정 및 변수 정의
    system_prefix = "System: 입력어의 언어에 따라 동일한 언어로 답변하라."
    full_system_message = f"{system_prefix}{system_message}"

    messages = [{"role": "system", "content": f"{system_prefix} {system_message}"}]
    for user_msg, assistant_msg in history:
        messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    messages.append({"role": "user", "content": message})

    # 이미지 생성 요청
    try:
        result = client.predict(
            prompt=message,
            seed=123,
            randomize_seed=False,
            width=1024,
            height=576,
            guidance_scale=5,
            num_inference_steps=28,
            api_name="/infer_t2i"
        )
        if 'url' in result:
            return result['url']
        else:
            logging.error("Image generation failed with error: %s", result.get('error', 'Unknown error'))
            return "Failed to generate image."
    except Exception as e:
        logging.error("Error during API request: %s", str(e))
        return f"An error occurred: {str(e)}"

theme = "Nymbo/Nymbo_Theme"
css = """
footer {
    visibility: hidden;
}
"""

# Gradio 채팅 인터페이스를 설정합니다.
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value="You are an AI assistant.", label="System Prompt"),
        gr.Slider(minimum=1, maximum=2000, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
        ),
    ],
    theme=theme,
    css=css
)

if __name__ == "__main__":
    demo.launch()