Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from PIL import Image
+import PIL
 import sys
 import zipfile
 from fastai import *
@@ -9,6 +9,7 @@ import os
 import timm
 from timm import create_model 
 from fastai.vision.all import *
+import torchvision.transforms as T
 
 torch.device('cpu')
 def seed_everything(seed):
@@ -46,10 +47,12 @@ file_name = st.file_uploader("Upload a hot dog candidate image")
 if file_name is not None:
     col1, col2 = st.columns(2)
 
-    image = Image.open(file_name)
+
+    image = Image.open(path2img)
+    img_tensor = T.ToTensor()(img_pil)
+    img_fastai = Image(img_tensor)
     col1.image(image, use_column_width=True)
-    image_pred = Image.create(img_bytes)
-    pred = catanddog.predict(image_pred)[1]
+    pred = catanddog.predict(img_fastai)[1]
     print(pred)
     def metric_rmse(input,target):
         return 100*torch.sqrt(F.mse_loss(F.sigmoid(input.flatten()), target))
@@ -58,7 +61,7 @@ if file_name is not None:
     # learner = learner.load(f'/kaggle/input/puredog/pure-dog-{fold}')
     if pred == 0:
         learn.load('../pure-dog-5')
-        pred = learn.predict(image_pred)
+        pred = learn.predict(img_fastai)
         print("Score for this doggy:", int(pred[2] * 100))
     if pred == 1:
         learn.load(f'../pure-cat-0')