fixed for cpu

app.py

@@ -8,11 +8,12 @@ import gradio as gr
 import torch
 from torch.nn.functional import softmax
 import numpy as np
+from PIL import Image
 
 # %% ../app.ipynb 3
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-model = torch.load('model.pth').to(device)
+model = torch.load('model.pth', map_location=torch.device('cpu')).to(device)
 model.eval()
 
 # %% ../app.ipynb 4
@@ -20,11 +21,10 @@ CLASS_LABELS  = ['Anger', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sadness', "Sur
 
 # %% ../app.ipynb 5
 def classify_emotions(im):
-    im = np.array(im) / 255
-    if len(im.shape) == 2:
-        im = im[..., np.newaxis]
-    if im.shape[-1] == 1:
-        im = np.concatenate((im, im, im), 2)
+    im = np.array(im) 
+    im = np.array(Image.fromarray(im).convert('L')) / 255
+    im = im[..., np.newaxis]
+    im = np.concatenate((im, im, im), 2)
     im = torch.tensor(im.transpose(2, 0, 1), dtype=torch.float32)
     prediction = model.forward(im[np.newaxis, ...].to(device))
     return dict(zip(CLASS_LABELS, *softmax(prediction, dim=1).tolist()))