weights to folder

app.py

@@ -37,7 +37,7 @@ paw_dls = dls.dataloaders(train_df, batch_size=8, seed=12, device='cpu')
 test = paw_dls.test_dl(train_df)
 learn = cnn_learner(paw_dls, models.resnet50, pretrained=False, metrics=error_rate) 
 learn.to('cpu')
-catanddog = learn.load('../dog')
+catanddog = learn.load('dog')
 
 st.title("Hot Dog? Or Not?")
 
@@ -56,11 +56,11 @@ if file_name is not None:
     learn = Learner(paw_dls, model, loss_func = BCEWithLogitsLossFlat(),  metrics=metric_rmse)
     # learner = learner.load(f'/kaggle/input/puredog/pure-dog-{fold}')
     if pred == 0:
-        learn.load('../pure-dog-5')
+        learn.load('pure-dog-5')
         pred = learn.predict(image)
         print("Score for this doggy:", int(pred[2] * 100))
     if pred == 1:
-        learn.load(f'../pure-cat-0')
+        learn.load(f'pure-cat-0')
         pred = learn.predict(image)
         print(pred)
         print("Score for this catto:", min(int(pred[2] * 100 + 35), 100))