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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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learn_sentiment = load_learner('sentiment_vgg19.pkl') |
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learn_sentiment_labels = learn_sentiment.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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pred_sentiment, pred_sentiment_idx, probs_sentiment = learn_sentiment.predict(img) |
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emotions = {f'emotion_{learn_emotion_labels[i]}': float(probs_emotion[i]) for i in range(len(learn_emotion_labels))} |
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sentiments = {f'sentiment_{learn_sentiment_labels[i]}': float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))} |
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return {**emotions, **sentiments} |
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title = "Facial Expression Sentiment Classifier" |
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description = "A model to detect emotion and sentiment from facial expressions trained on FER2013 dataset using FastAi. Created as a demo for AI Course." |
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article = 'Sample images are taken from VG & AftenPoften webpages. Copyrights belong to respective brands. All rights reserved.' |
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interpretation='default' |
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enable_queue=True |
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examples = ['happy1.jpg', 'happy2.jpg', 'angry1.jpg', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg'] |
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gr.Interface(fn = predict, |
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inputs = gr.Image(shape=(48, 48), image_mode='L'), |
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outputs = gr.Label(), |
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title = title, |
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description = description, |
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article=article).launch(share=True, enable_queue=enable_queue) |