diff --git a/backend/petkinApp/__pycache__/main.cpython-311.pyc b/backend/petkinApp/__pycache__/main.cpython-311.pyc
index d59565d..f11bc04 100644
Binary files a/backend/petkinApp/__pycache__/main.cpython-311.pyc and b/backend/petkinApp/__pycache__/main.cpython-311.pyc differ
diff --git a/backend/petkinApp/routers/prediction.py b/backend/petkinApp/routers/prediction.py
index bf27131..ef94143 100644
--- a/backend/petkinApp/routers/prediction.py
+++ b/backend/petkinApp/routers/prediction.py
@@ -1,7 +1,6 @@
 import json
 import os
 from datetime import datetime
-
 import pandas as pd
 import gdown
 from fastapi import FastAPI, File, UploadFile, HTTPException, APIRouter, Form, Depends
@@ -9,12 +8,12 @@
 from sqlalchemy.orm import Session, session
 from torch import nn
 import torch
+from torch.nn import Module
 from torchvision import transforms, models
 from PIL import Image
 from sklearn.preprocessing import MinMaxScaler
 from httpx import AsyncClient
 import torch.nn.functional as F
-
 from petkinApp.database import get_db
 from petkinApp.models import AIResult, DiseasePredictionRecord
 from petkinApp.routers.pets import get_pet
@@ -84,7 +83,7 @@ def load_model(model_path="model.pt"):
         # 모델 파일이 없으면 Google Drive에서 다운로드
         if not os.path.exists(model_path):
             print(f"{model_path} not found. Downloading from Google Drive...")
-            download_model_from_google_drive(file_id="1vF5oXignqWuLBE7uQUthx4sTc8w4RydA", output_path=model_path)
+            download_model_from_google_drive(file_id="1YKt3OayICzlpXLYFVYD5TOZ2Lg8bnSJ0", output_path=model_path)
 
         # 모델 로드
         print("Loading model...")
@@ -172,15 +171,8 @@ async def predict_api(
             print("Model is None!")
             raise HTTPException(status_code=500, detail="Model is not loaded properly.")
 
-        # 펫 정보 추출 및 전처리
-        df = pd.DataFrame([{
-            "breed": pet_info.breed,
-            "gender": pet_info.gender,
-            "lesions": None,  # 필요한 경우 기본값 설정
-            "age": pet_info.age
-        }])  # PetDetailResponse를 DataFrame으로 변환
-        processed_features = preprocess_dataframe(df)
-        additional_features_tensor = torch.tensor(processed_features.values, dtype=torch.float32)
+        # 펫 정보 전처리
+        additional_features_tensor = preprocess_pet_info(pet_info)
 
         # 이미지 처리
         image_tensor = image_transform(image.file)
@@ -188,9 +180,9 @@ async def predict_api(
 
         # 모델 예측
         with torch.no_grad():
-            logits = model(image_tensor, additional_features_tensor)
-            probabilities = F.softmax(logits, dim=1).squeeze(0).tolist()  # 1D 리스트로 변환
-            print("Logits with features:", logits)
+            logit = model(image_tensor, additional_features_tensor)
+            probabilities = F.softmax(logit, dim=1).squeeze(0).tolist()  # 1D 리스트로 변환
+            print("Logits with features:", logit)
             print("Probabilities with features:", probabilities)
 
         # 이미지 저장 및 URL 생성
@@ -211,29 +203,25 @@ async def predict_api(
         image_path = os.path.join(upload_dir, image_filename)  # 저장 경로
 
         try:
-            from PIL import Image
+            # 이미지를 RGB 형식으로 변환
+            pil_image = Image.open(image.file).convert("RGB")
+
+            # 확장자에 따른 이미지 저장 포맷 결정
+            save_format = "JPEG" if file_extension in [".jpg", ".jpeg"] else file_extension[1:].upper()
 
-            # `Pillow`를 이용한 이미지 변환 및 저장
-            pil_image = Image.open(image.file).convert("RGB")  # `webp` 포함 모든 파일을 RGB로 변환
-            pil_image.save(image_path, format=file_extension[1:].upper())  # 확장자에 맞는 형식으로 저장
+            # 이미지 저장
+            pil_image.save(image_path, format=save_format)
+            print(f"Image saved successfully at {image_path}")  # 저장 성공 로그 추가
 
-            # 저장된 이미지 URL 생성
-            image_url = f"/static/uploads/{image_filename}"
         except Exception as e:
+            print(f"Failed to save image: {str(e)}")  # 에러 로그 출력
             raise HTTPException(status_code=500, detail=f"Failed to process the image: {str(e)}")
 
-        # 가장 높은 클래스 인덱스 및 이름 가져오기
-        class_mapping = {
-            0: "A1 구진/플라크",
-            1: "A2 비듬/각질/상피성잔고리",
-            2: "A3 태선화 과다색소침착",
-            3: "A4 농포/여드름",
-            4: "A5 미란/궤양",
-            5: "A6 결정/종괴",
-            6: "A7 무증상"
-        }
-        predicted_class_index = torch.argmax(torch.tensor(probabilities)).item()  # 가장 높은 인덱스
-        predicted_class_label = class_mapping[predicted_class_index]
+        # 저장된 이미지 URL 생성
+        image_url = f"/static/uploads/{image_filename}"
+
+        # 클래스 라벨 반환
+        predicted_class_label, predicted_class_index = get_predicted_class_label(probabilities)
 
         # 모델 이름 (예: EfficientNet)
         model_name = "MultimodalModel-EfficientNetB0"
@@ -436,3 +424,35 @@ async def get_result_detail_by_analysis_id(
             status_code=500,
             detail=f"Failed to retrieve AIResult: {str(e)}"
         )
+
+
+def get_predicted_class_label(probabilities):
+    """
+    확률 값을 바탕으로 예측된 클래스 라벨을 반환합니다.
+    """
+    class_mapping = {
+        0: "A1 구진/플라크",
+        1: "A2 비듬/각질/상피성잔고리",
+        2: "A3 태선화 과다색소침착",
+        3: "A4 농포/여드름",
+        4: "A5 미란/궤양",
+        5: "A6 결정/종괴",
+        6: "A7 무증상"
+    }
+    predicted_class_index = torch.argmax(torch.tensor(probabilities)).item()
+    return class_mapping[predicted_class_index], predicted_class_index
+
+
+def preprocess_pet_info(pet_info: PetDetailResponse):
+    """
+    펫 정보를 받아 DataFrame으로 변환하고 전처리를 수행합니다.
+    """
+    df = pd.DataFrame([{
+        "breed": pet_info.breed,
+        "gender": pet_info.gender,
+        "lesions": None,  # 기본값 설정
+        "age": pet_info.age
+    }])
+    processed_features = preprocess_dataframe(df)
+    additional_features_tensor = torch.tensor(processed_features.values, dtype=torch.float32)
+    return additional_features_tensor