Add PKL storage and CSV conversion

workspace/main.py

@@ -26,13 +26,11 @@ logger = logging.getLogger(__name__)
 # FastAPI 엔드포인트: CSV 파일 및 모델 리스트 업로드 처리
 @app.post("/start-inference/")
 async def process_csv(input_csv: UploadFile, model_list_txt: UploadFile, background_tasks: BackgroundTasks):
-    # 파일 형식 확인
     if not input_csv.filename.endswith(".csv"):
         return JSONResponse(content={"error": "Uploaded file is not a CSV."}, status_code=400)
     if not model_list_txt.filename.endswith(".txt"):
         return JSONResponse(content={"error": "Uploaded model list is not a TXT file."}, status_code=400)
 
-    # 파일 저장
     file_path = f"uploaded/{input_csv.filename}"
     model_list_path = f"uploaded/{model_list_txt.filename}"
     os.makedirs("uploaded", exist_ok=True)
@@ -45,7 +43,6 @@ async def process_csv(input_csv: UploadFile, model_list_txt: UploadFile, backgro
 
     logger.info(f"Files uploaded: {file_path}, {model_list_path}")
 
-    # 작업 큐에 추가
     job = queue.enqueue(run_inference, file_path, model_list_path, job_timeout=1800)
     logger.info(f"Job enqueued: {job.id}")
     return {"job_id": job.id, "status": "queued"}
@@ -74,6 +71,7 @@ def delete_csv_file(file_path: str):
     except Exception as e:
         logger.error(f"Error deleting CSV file: {e}")
 
+# 모델 템플릿 적용
 def chat_formating(input_sentence: str, model_name: str):
     try:
         if "llama" in model_name:
@@ -94,14 +92,12 @@ def run_inference(file_path: str, model_list_path: str, batch_size: int = 32):
     try:
         logger.info(f"Starting inference for file: {file_path} using models from {model_list_path}")
 
-        # 모델 리스트 읽기
         with open(model_list_path, "r") as f:
             model_list = [line.strip() for line in f.readlines()]
 
         if not model_list:
             raise ValueError("The model list file is empty.")
 
-        # CSV 읽기
         try:
             df = pd.read_csv(file_path, encoding="euc-kr")
         except Exception as e:
@@ -111,10 +107,8 @@ def run_inference(file_path: str, model_list_path: str, batch_size: int = 32):
         if "input" not in df.columns:
             raise ValueError("The input CSV must contain a column named 'input'.")
 
-        # 에러 발생한 행 저장용 DataFrame 초기화
         error_rows = pd.DataFrame(columns=df.columns)
         
-        # 각 모델로 추론
         for model in model_list:
             model_name = model.split("/")[-1]
             try:
@@ -128,7 +122,6 @@ def run_inference(file_path: str, model_list_path: str, batch_size: int = 32):
 
             sampling_params = SamplingParams(max_tokens=50, temperature=0.7, top_p=0.9, top_k=50)
 
-            # 추론 수행
             responses = []
             for i in tqdm(range(0, len(df), batch_size), desc=f"Processing {model}"):
                 batch = df.iloc[i:i+batch_size]
@@ -146,21 +139,18 @@ def run_inference(file_path: str, model_list_path: str, batch_size: int = 32):
                         batch_responses.append(None)
                 responses.extend(batch_responses)
 
-            # 결과 추가
             df[model_name] = responses
 
             del llm
             torch.cuda.empty_cache()
             gc.collect()
 
-        # 결과 저장
         output_path = file_path.replace("uploaded", "processed").replace(".csv", "_result.csv")
         os.makedirs("processed", exist_ok=True)
         # df.to_csv(output_path, index=False, encoding="utf-8")
         save_to_pkl(df, output_path)
         logger.info(f"Inference completed. Result saved to: {output_path}")
 
-        # 에러 행 저장
         if not error_rows.empty:
             error_path = file_path.replace("uploaded", "errors").replace(".csv", "_errors.csv")
             os.makedirs("errors", exist_ok=True)
@@ -177,12 +167,10 @@ def run_inference(file_path: str, model_list_path: str, batch_size: int = 32):
 @app.get("/download-latest-result", response_class=FileResponse)
 def download_latest_result(background_tasks: BackgroundTasks):
     try:
-        # PKL 파일 저장 디렉토리
         processed_dir = "processed"
         if not os.path.exists(processed_dir):
             return JSONResponse(content={"error": "Processed directory not found."}, status_code=404)
 
-        # 디렉토리 내 가장 최근에 저장된 PKL 파일 찾기
         pkl_files = [os.path.join(processed_dir, f) for f in os.listdir(processed_dir) if f.endswith(".pkl")]
         if not pkl_files:
             return JSONResponse(content={"error": "No PKL files found in the processed directory."}, status_code=404)
@@ -190,13 +178,10 @@ def download_latest_result(background_tasks: BackgroundTasks):
         latest_pkl = max(pkl_files, key=os.path.getctime)
         csv_path = latest_pkl.replace(".pkl", ".csv")
 
-        # PKL 파일을 CSV로 변환
         convert_pkl_to_csv(latest_pkl, csv_path)
 
-        # Background task에 파일 삭제 작업 추가
         background_tasks.add_task(delete_csv_file, csv_path)
 
-        # CSV 파일 응답 반환
         return FileResponse(csv_path, media_type="application/csv", filename=os.path.basename(csv_path))
 
     except Exception as e: