added app

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['device', 'model', 'CLASS_LABELS', 'image', 'label', 'examples', 'intf', 'classify_emotions']
+
+# %% ../app.ipynb 2
+import gradio as gr 
+import torch
+from torch.nn.functional import softmax
+import numpy as np
+
+# %% ../app.ipynb 3
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = torch.load('model.pth').to(device)
+model.eval()
+
+# %% ../app.ipynb 4
+CLASS_LABELS  = ['Anger', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sadness', "Surprise"]
+
+# %% ../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 = 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()))
+    
+
+# %% ../app.ipynb 6
+image = gr.inputs.Image((48, 48))
+label = gr.outputs.Label()
+examples = ['happy.png', 'fear.png', 'anger.png']
+
+intf = gr.Interface(fn=classify_emotions, 
+                    inputs=image, 
+                    outputs=label, 
+                    title='Emotion classification',
+                    examples=examples)
+intf.launch(inline=False)

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:faf147864e48423a9559cfb6bdff95a79c1621a24c1ee46e50e1b02dae740e1b
+size 44808077

requirements.txt

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+torch
+torchvision
+pillow