Commit
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ccb670c
1
Parent(s):
7973875
update: new output and text
Browse files
app.py
CHANGED
@@ -19,15 +19,23 @@ def 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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# Gradio
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title = "Facial Expression Sentiment Classifier"
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description = "
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interpretation='default'
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enable_queue=True
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@@ -35,7 +43,7 @@ examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jp
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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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examples = examples,
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description = description,
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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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emotions = {learn_emotion_labels[i]: float(probs_emotion[i]) for i in range(len(learn_emotion_labels))}
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sentiments = {learn_sentiment_labels[i]: float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))}
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return [emotions, sentiments] #{**emotions, **sentiments}
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# Gradio
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title = "Facial Expression Sentiment Classifier"
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description = "Ever wondered what a person might be feeling looking at their picture? Well, now you can! Try this fun " + \
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"app - just upload a facial image in jpg or png format. Voila! you can now see what they might have felt when the " + \
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"picture was taken. Be sure to only include face to get best results. Check some sample images at the bottom for " + \
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"inspiration!"
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article = "DISCLAIMER: This model does not reveal the actual emotional state of a person. Use and interpret results at your own risk! " + \
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"It was built as a demo for AI course. The model is trained on FER2013 dataset using FastAI. Sample images are taken " + \
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"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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gr.Interface(fn = predict,
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inputs = gr.Image(shape=(48, 48), image_mode='L'),
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outputs = [gr.Label(label='Emotion'), gr.Label(label='Sentiment')], #gr.Label(),
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title = title,
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examples = examples,
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description = description,
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