diff --git a/streamlit/models/__init__.py b/streamlit/models/__init__.py
index c0e6d2a7..c6fb2a47 100644
--- a/streamlit/models/__init__.py
+++ b/streamlit/models/__init__.py
@@ -1,2 +1,3 @@
+# from .cnn_st import *
 from .hmm import *
 from .cnn.cnn_st import *
\ No newline at end of file
diff --git a/streamlit/models/cnn/cnn_inference.py b/streamlit/models/cnn/cnn_inference.py
index f5796d29..5e796dd0 100644
--- a/streamlit/models/cnn/cnn_inference.py
+++ b/streamlit/models/cnn/cnn_inference.py
@@ -115,28 +115,28 @@ def forward(self, x): # input: [N, 64, 60]
 def get_CNN5d_5d():
     model = CNN5d()
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    state_dict = torch.load('./cnn_model/I5R5_Model.tar',map_location=torch.device(device))
+    state_dict = torch.load('models/cnn/I5R5_Model.tar',map_location=torch.device(device))
     model.load_state_dict(state_dict['model_state_dict'])
     return model
 
 def get_CNN5d_20d():
     model = CNN5d()
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    state_dict = torch.load('./cnn_model/I5R20_Model.tar',map_location=torch.device(device))
+    state_dict = torch.load('models/cnn/I5R20_Model.tar',map_location=torch.device(device))
     model.load_state_dict(state_dict['model_state_dict'])
     return model
 
 def get_CNN20d_5d():
     model = CNN20d()
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    state_dict = torch.load('./cnn_model/I20R5_Model.tar',map_location=torch.device(device))
+    state_dict = torch.load('models/cnn/I20R5_Model.tar',map_location=torch.device(device))
     model.load_state_dict(state_dict['model_state_dict'])
     return model
 
 def get_CNN20d_20d():
     model = CNN20d()
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    state_dict = torch.load('./cnn_model/I20R20_Model.tar',map_location=torch.device(device))
+    state_dict = torch.load('models/cnn/I20R20_Model.tar',map_location=torch.device(device))
     model.load_state_dict(state_dict['model_state_dict'])
     return model
 
diff --git a/streamlit/models/cnn/cnn_st.py b/streamlit/models/cnn/cnn_st.py
index 275c8b19..5bbb1441 100644
--- a/streamlit/models/cnn/cnn_st.py
+++ b/streamlit/models/cnn/cnn_st.py
@@ -5,7 +5,7 @@
 import numpy as np
 import torch 
 from PIL import Image
-from cnn_model.cnn_inference import get_CNN5d_5d, get_CNN5d_20d, get_CNN20d_5d, get_CNN20d_20d, inference, image_to_np, grad_cam, image_to_tensor, time_calc
+from .cnn_inference import get_CNN5d_5d, get_CNN5d_20d, get_CNN20d_5d, get_CNN20d_20d, inference, image_to_np, grad_cam, image_to_tensor, time_calc
 
 def get_stock_data(ticker, period, interval):
         stock = yf.Ticker(ticker)
@@ -130,13 +130,13 @@ def cnn_model_inference(company, ticker, period, interval):
                     percent = round(model_pred[pred_idx].item()*100,2)
 
                     if pred_idx == 0:
-                        img = Image.open('cnn_model/bear.png').resize((256,256))
+                        img = Image.open('models/cnn/bear.png').resize((256,256))
                         p_col1.image(img)
                         p_col2.markdown(f'''AI 모델의 분석 결과  
                                         **{company}**의 **{output_period}** 이후 주가는  
                                         :red[**{percent}%**] 확률로 :red[**하락**]을 예측합니다''')
                     elif pred_idx == 1:
-                        img = Image.open('cnn_model/bull.png').resize((256,256))
+                        img = Image.open('models/cnn/bull.png').resize((256,256))
                         p_col1.image(img)
                         p_col2.markdown(f'''AI 모델의 분석 결과  
                                         **{company}**의 **{output_period}** 이후 주가는  
diff --git a/streamlit/views/prediction.py b/streamlit/views/prediction.py
index 30e8b958..30cdf5ce 100644
--- a/streamlit/views/prediction.py
+++ b/streamlit/views/prediction.py
@@ -2,8 +2,8 @@
 import streamlit as st
 import yfinance as yf
 import plotly.graph_objs as go
-from models.cnn import cnn_model_inference
-from models.hmm import HMM
+from models import HMM
+from models import cnn_model_inference
 
 def app():
     st.title('Stock Price Prediction')