from nlp import analyze_kakao_csv, get_json_result
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import os

# 업로드 디렉토리 생성
UPLOAD_DIR = "uploads"
os.makedirs(UPLOAD_DIR, exist_ok=True)

app = FastAPI()

model_name = "nlp04/korean_sentiment_analysis_kcelectra"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

@app.get("/")
def greet_json():
    return {"v": 0.1}

@app.post("/upload/")
async def upload_file(file: UploadFile = File(...)):
    file_location = os.path.join(UPLOAD_DIR, file.filename)
    try:
        contents = await file.read()
        with open(file_location, "wb") as f:
            f.write(contents)

        # analyze_kakao_upload_file이 비동기 함수라면 await 필요
        results_df = await analyze_kakao_csv(contents, model, tokenizer)
        
        if results_df is None:
            return {"error": "CSV 파일을 읽을 수 없습니다."}
        else:
            result_json = get_json_result(results_df, "KCElectra")
            print(f"\nAnalysis complete! Results saved to the 'results' folder.")
            
        return {
            "filename": file.filename,
            "saved_to": file_location,
            "result": result_json
        }
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": str(e)})