app.py

import gradio as gr
import numpy as np
import librosa
from transformers import pipeline
from datetime import datetime
import os
from diffusers import StableDiffusionPipeline
import torch

# 스테이블 디퓨전 초기화
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
if torch.cuda.is_available():
    pipe = pipe.to("cuda")

# AI 모델 초기화
speech_recognizer = pipeline(
    "automatic-speech-recognition",
    model="kresnik/wav2vec2-large-xlsr-korean"
)
emotion_classifier = pipeline(
    "audio-classification",
    model="MIT/ast-finetuned-speech-commands-v2"
)
text_analyzer = pipeline(
    "sentiment-analysis",
    model="nlptown/bert-base-multilingual-uncased-sentiment"
)

def create_interface():
    with gr.Blocks(theme=gr.themes.Soft()) as app:
        state = gr.State({
            "user_name": "",
            "reflections": [],
            "voice_analysis": None,
            "final_prompt": "",
            "generated_images": []
        })

        # 헤더
        header = gr.Markdown("# 디지털 굿판")
        user_display = gr.Markdown("")

        with gr.Tabs() as tabs:
            # 입장
            with gr.Tab("입장"):
                gr.Markdown("""# 디지털 굿판에 오신 것을 환영합니다""")
                name_input = gr.Textbox(label="이름을 알려주세요")
                start_btn = gr.Button("여정 시작하기")

            # 청신
            with gr.Tab("청신"):
                with gr.Row():
                    # 절대 경로로 변경
                    audio_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "assets", "main_music.mp3"))
                    audio = gr.Audio(
                        value=audio_path,
                        type="filepath",
                        label="온천천의 소리",
                        interactive=False,
                        autoplay=True
                    )
                    with gr.Column():
                        reflection_input = gr.Textbox(
                            label="현재 순간의 감상을 적어주세요",
                            lines=3
                        )
                        save_btn = gr.Button("감상 저장하기")
                        reflections_display = gr.Dataframe(
                            headers=["시간", "감상", "감정 분석"],
                            label="기록된 감상들"
                        )

            # 기원
            with gr.Tab("기원"):
                gr.Markdown("## 기원 - 목소리로 전하기")
                with gr.Row():
                    with gr.Column():
                        record_btn = gr.Button("🎤 녹음 시작/중지")
                        voice_input = gr.Audio(
                            label="나누고 싶은 이야기를 들려주세요",
                            sources=["microphone"],
                            type="filepath",
                            interactive=True
                        )
                        clear_btn = gr.Button("녹음 지우기")
                    
                    with gr.Column():
                        transcribed_text = gr.Textbox(
                            label="인식된 텍스트",
                            interactive=False
                        )
                        voice_emotion = gr.Textbox(
                            label="음성 감정 분석",
                            interactive=False
                        )
                        text_emotion = gr.Textbox(
                            label="텍스트 감정 분석",
                            interactive=False
                        )
                        analyze_btn = gr.Button("분석하기")

            # 송신
            with gr.Tab("송신"):
                gr.Markdown("## 송신 - 시각화 결과")
                with gr.Column():
                    final_prompt = gr.Textbox(
                        label="생성된 프롬프트",
                        interactive=False
                    )
                    generate_btn = gr.Button("이미지 생성하기")
                    gallery = gr.Gallery(
                        label="시각화 결과",
                        columns=2,
                        show_label=True,
                        elem_id="gallery"
                    )

        def clear_voice_input():
            """음성 입력 초기화"""
            return None

        def analyze_voice(audio_path, state):
            """음성 분석"""
            if audio_path is None:
                return state, "음성을 먼저 녹음해주세요.", "", "", ""
            
            try:
                # 오디오 로드
                y, sr = librosa.load(audio_path, sr=16000)
                
                # 음성 인식
                transcription = speech_recognizer(y)
                text = transcription["text"]
                
                # 감정 분석
                voice_emotions = emotion_classifier(y)
                text_sentiment = text_analyzer(text)[0]
                
                return (
                    state,
                    text,
                    f"음성 감정: {voice_emotions[0]['label']} ({voice_emotions[0]['score']:.2f})",
                    f"텍스트 감정: {text_sentiment['label']} ({text_sentiment['score']:.2f})",
                    "분석이 완료되었습니다."
                )
            except Exception as e:
                return state, f"오류 발생: {str(e)}", "", "", ""

        def generate_image(prompt, state):
            """이미지 생성"""
            try:
                images = pipe(prompt).images
                image_paths = []
                for i, image in enumerate(images):
                    path = f"output_{i}.png"
                    image.save(path)
                    image_paths.append(path)
                return image_paths
            except Exception as e:
                return []

        # 이벤트 연결
        start_btn.click(
            fn=lambda name: (f"# 환영합니다, {name}님의 디지털 굿판", gr.update(selected="청신")),
            inputs=[name_input],
            outputs=[user_display, tabs]
        )

        save_btn.click(
            fn=lambda text, state: save_reflection(text, state),
            inputs=[reflection_input, state],
            outputs=[state, reflections_display]
        )

        clear_btn.click(
            fn=clear_voice_input,
            inputs=[],
            outputs=[voice_input]
        )

        analyze_btn.click(
            fn=analyze_voice,
            inputs=[voice_input, state],
            outputs=[state, transcribed_text, voice_emotion, text_emotion, final_prompt]
        )

        generate_btn.click(
            fn=generate_image,
            inputs=[final_prompt, state],
            outputs=[gallery]
        )

    return app

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