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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
from PIL import Image
# %% ../app.ipynb 3
device = "cuda" if torch.cuda.is_available() else "cpu"
model = torch.load('model.pth', map_location=torch.device('cpu')).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)
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()))
# %% ../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)
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