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import cv2 | |
import gradio as gr | |
from predict_image import load_model, predict | |
def predict_fn(image, model_name): | |
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
cv2.imwrite('./myimage.jpg', image) | |
# model for emotion classification | |
if model_name == 'EfficientNetB0': | |
model_name = 'effb0' | |
elif model_name == 'ResNet18': | |
model_name = 'res18' | |
else: | |
raise ValueError('Enter correct model_name') | |
model = load_model(model_name) | |
out = predict('./myimage.jpg', './result.jpg', model) | |
out = cv2.cvtColor(out, cv2.COLOR_BGR2RGB) | |
return out | |
demo = gr.Interface( | |
fn=predict_fn, | |
inputs=[ | |
gr.inputs.Image(label="Input Image"), | |
gr.Radio(['EfficientNetB0', 'ResNet18'], value='EfficientNetB0', label='Model Name') | |
], | |
outputs=[ | |
gr.inputs.Image(label="Prediction"), | |
], | |
title="Emotion Recognition Demo", | |
description="Emotion Classification Model trained on FER Dataset", | |
examples=[ | |
["example/fear.jpg", 'EfficientNetB0'], | |
["example/sad.jpg", 'EfficientNetB0'], | |
["example/happy.jpg", 'EfficientNetB0'], | |
], | |
) | |
demo.launch(debug=True) |