Revert "[Feat] voice to text by stt"

README.md

@@ -19,42 +19,17 @@ pip install -r requirements.txt
 
 ## 실행
 
-개발 서버(Uvicorn) 실행:
-
 ```bash
-uvicorn app.main:app --reload --port 8000
-```
-
-API 문서: `http://127.0.0.1:8000/docs`
-
-## 환경 변수 설정
-
-프로젝트 루트에 `.env` 파일을 생성하고 다음 값을 채우세요.
-
-### AWS S3 설정
+python -m app.main
 ```
-AWS_ACCESS_KEY_ID=...
-AWS_SECRET_ACCESS_KEY=...
-AWS_REGION=ap-northeast-2
-S3_BUCKET_NAME=your-bucket
-S3_PREFIX=voices
-```
-
-### Google Cloud Speech-to-Text 설정
-```
-# 서비스 계정 키 파일 경로 설정
-GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/service-account-key.json
-```
-
-`.env`는 `app/__init__.py`에서 자동 로드됩니다.
 
 ## 프로젝트 구조
 
 ```
 caring-voice/
 ├── app/
 │   ├── __init__.py
-│   └── main.py          # FastAPI 엔트리 포인트 및 엔드포인트
+│   └── main.py          # 메인 엔트리 포인트
 ├── .gitignore
 ├── README.md
 ├── requirements.txt

app/__init__.py

@@ -1,3 +0,0 @@
-from dotenv import load_dotenv  # type: ignore
-load_dotenv()
-

app/main.py

@@ -1,144 +1,7 @@
-import os
-from typing import Optional
-from fastapi import FastAPI, UploadFile, File, HTTPException, Form
-from fastapi.responses import JSONResponse
-from typing import List
-from .s3_service import upload_fileobj, list_bucket_objects
-from .constants import VOICE_BASE_PREFIX, DEFAULT_UPLOAD_FOLDER
-from .emotion_service import analyze_voice_emotion
-from .stt_service import transcribe_voice
+from fastapi import FastAPI
 
 app = FastAPI(title="Caring API")
 
 @app.get("/health")
 def health():
     return {"status": "ok"}
-
-
-# POST : upload voice
-@app.post("/voices/upload")
-async def upload_voice(
-    file: UploadFile = File(...),
-    folder: Optional[str] = Form(default=None),  # 예: "raw" 또는 "user123/session1"
-):
-    bucket = os.getenv("S3_BUCKET_NAME")
-    if not bucket:
-        raise HTTPException(status_code=500, detail="S3_BUCKET_NAME not configured")
-
-    # 키: optional prefix/YYYYMMDD_originalname
-    base_prefix = VOICE_BASE_PREFIX.rstrip("/")
-    effective_prefix = f"{base_prefix}/{folder or DEFAULT_UPLOAD_FOLDER}".rstrip("/")
-    filename = os.path.basename(file.filename or "upload.wav")
-    key = f"{effective_prefix}/{filename}"
-
-    # 파일을 S3에 업로드
-    # Content-Type 저장
-    upload_fileobj(bucket=bucket, key=key, fileobj=file.file, content_type=file.content_type)
-    # 이후 소비자를 위해 포인터 리셋
-    try:
-        file.file.seek(0)
-    except Exception:
-        pass
-
-    # 감정 분석 수행
-    emotion_result = analyze_voice_emotion(file)
-
-    # DB가 없으므로, 버킷의 파일 목록을 반환
-    names = list_bucket_objects(bucket=bucket, prefix=effective_prefix)
-    return {
-        "uploaded": key, 
-        "files": names,
-        "emotion_analysis": emotion_result
-    }
-
-
-# GET : query my voice histories
-@app.get("/voices")
-async def list_voices(skip: int = 0, limit: int = 50, folder: Optional[str] = None):
-    bucket = os.getenv("S3_BUCKET_NAME")
-    if not bucket:
-        raise HTTPException(status_code=500, detail="S3_BUCKET_NAME not configured")
-    base_prefix = VOICE_BASE_PREFIX.rstrip("/")
-    effective_prefix = f"{base_prefix}/{folder or DEFAULT_UPLOAD_FOLDER}".rstrip("/")
-
-    keys = list_bucket_objects(bucket=bucket, prefix=effective_prefix)
-    # 페이징 비슷하게 slice만 적용
-    sliced = keys[skip: skip + limit]
-    return {"items": sliced, "count": len(sliced), "next": skip + len(sliced)}
-
-
-# GET : query specific voice & show result
-@app.get("/voices/{voice_id}")
-async def get_voice(voice_id: str):
-    # 내부 로직은 생략, 더미 상세 반환
-    result = {
-        "voice_id": voice_id,
-        "filename": f"{voice_id}.wav",
-        "status": "processed",
-        "duration_sec": 12.34,
-        "analysis": {"pitch_mean": 220.5, "energy": 0.82}
-    }
-    return JSONResponse(content=result)
-
-
-# POST : analyze emotion from uploaded voice file
-@app.post("/voices/analyze-emotion")
-async def analyze_emotion(file: UploadFile = File(...)):
-    """음성 파일의 감정을 분석합니다."""
-    emotion_result = analyze_voice_emotion(file)
-    return emotion_result
-
-
-# POST : convert speech to text using Google STT
-@app.post("/voices/transcribe")
-async def transcribe_speech(
-    file: UploadFile = File(...),
-    language_code: str = "ko-KR"
-):
-    """음성 파일을 텍스트로 변환합니다."""
-    stt_result = transcribe_voice(file, language_code)
-    return stt_result
-
-
-# POST : upload voice with both emotion analysis and STT
-@app.post("/voices/upload-with-analysis")
-async def upload_voice_with_analysis(
-    file: UploadFile = File(...),
-    folder: Optional[str] = Form(default=None),
-    language_code: str = Form(default="ko-KR")
-):
-    """음성 파일을 업로드하고 감정 분석과 STT를 모두 수행합니다."""
-    bucket = os.getenv("S3_BUCKET_NAME")
-    if not bucket:
-        raise HTTPException(status_code=500, detail="S3_BUCKET_NAME not configured")
-
-    # S3 업로드
-    base_prefix = VOICE_BASE_PREFIX.rstrip("/")
-    effective_prefix = f"{base_prefix}/{folder or DEFAULT_UPLOAD_FOLDER}".rstrip("/")
-    filename = os.path.basename(file.filename or "upload.wav")
-    key = f"{effective_prefix}/{filename}"
-    upload_fileobj(bucket=bucket, key=key, fileobj=file.file, content_type=file.content_type)
-    try:
-        file.file.seek(0)
-    except Exception:
-        pass
-
-    # 감정 분석
-    emotion_result = analyze_voice_emotion(file)
-    try:
-        file.file.seek(0)
-    except Exception:
-        pass
-
-    # STT 변환
-    stt_result = transcribe_voice(file, language_code)
-
-    # 파일 목록 조회
-    names = list_bucket_objects(bucket=bucket, prefix=effective_prefix)
-
-    return {
-        "uploaded": key,
-        "files": names,
-        "emotion_analysis": emotion_result,
-        "transcription": stt_result
-    }