weights to folder

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):
@@ -46,9 +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)
+    print(image)
+    img_fastai = np.array(image)
     col1.image(image, use_column_width=True)
-    pred = catanddog.predict(image)[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))
@@ -65,4 +69,4 @@ if file_name is not None:
         print(pred)
         print("Score for this catto:", min(int(pred[2] * 100 + 35), 100))
     col2.header("Probabilities")
-    col2.subheader(f"{ pred[2] }: { round(pred[2] * 100, 1)}%")
+    col2.subheader(f"{ pred[2] }: { round(float(pred[2]) * 100, 1)}%")

requirements.txt

@@ -1,3 +1,3 @@
-timm
 torch
-fastai
+fastai
+timm==0.6.5