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import os |
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from fastai.vision.all import * |
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import gradio as gr |
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learn_emotion = load_learner('emotions_vgg19.pkl') |
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learn_emotion_labels = learn_emotion.dls.vocab |
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def predict(img): |
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img = PILImage.create(img) |
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pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(img) |
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predicted_emotion = learn_emotion_labels[pred_emotion_idx] |
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return predicted_emotion |
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title = "Facial Emotion Detector" |
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description = gr.Markdown( |
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"""Ever wondered what a person might be feeling looking at their picture? |
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Well, now you can! Try this fun app. Just upload a facial image in JPG or |
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PNG format. You can now see what they might have felt when the picture |
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was taken. |
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**Tip**: Be sure to only include face to get best results. Check some sample images |
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below for inspiration!""").value |
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article = gr.Markdown( |
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"""**DISCLAIMER:** This model does not reveal the actual emotional state of a person. Use and |
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interpret results at your own risk!. |
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**PREMISE:** The idea is to determine an overall emotion of a person |
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based on the pictures. We are restricting pictures to only include close-up facial |
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images. |
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**DATA:** FER2013 dataset consists of 48x48 pixel grayscale images of faces.Images |
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are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. |
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""").value |
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enable_queue=True |
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examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg'] |
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gr.Interface(fn = predict, |
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inputs = gr.Image( image_mode='L'), |
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outputs = [gr.Label(label='Emotion')], |
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title = title, |
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examples = examples, |
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description = description, |
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article=article, |
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allow_flagging='never').launch() |
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