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| # Facial expression classifier | |
| import os | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| # Emotion | |
| learn_emotion = load_learner('emotions_vgg19.pkl') | |
| learn_emotion_labels = learn_emotion.dls.vocab | |
| # Sentiment | |
| learn_sentiment = load_learner('sentiment_vgg19.pkl') | |
| learn_sentiment_labels = learn_sentiment.dls.vocab | |
| # Predict | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(img) | |
| pred_sentiment, pred_sentiment_idx, probs_sentiment = learn_sentiment.predict(img) | |
| #emotions = {f'emotion_{learn_emotion_labels[i]}': float(probs_emotion[i]) for i in range(len(learn_emotion_labels))} | |
| #sentiments = {f'sentiment_{learn_sentiment_labels[i]}': float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))} | |
| emotions = {learn_emotion_labels[i]: float(probs_emotion[i]) for i in range(len(learn_emotion_labels))} | |
| sentiments = {learn_sentiment_labels[i]: float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))} | |
| return [emotions, sentiments] #{**emotions, **sentiments} | |
| # Gradio | |
| title = "Facial Emotion and Sentiment Detector" | |
| description = gr.Markdown( | |
| """Ever wondered what a person might be feeling looking at their picture? | |
| Well, now you can! Try this fun app. Just upload a facial image in JPG or | |
| PNG format. Voila! you can now see what they might have felt when the picture | |
| was taken. | |
| **Tip**: Be sure to only include face to get best results. Check some sample images | |
| below for inspiration!""").value | |
| article = gr.Markdown( | |
| """**DISCLAIMER:** This model does not reveal the actual emotional state of a person. Use and | |
| Positive (Happy, Surprise) | |
| Negative (Angry, Disgust, Fear, Sad) | |
| Neutral (Neutral) | |
| **MODEL:** VGG19""").value | |
| enable_queue=True | |
| examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg'] | |
| gr.Interface(fn = predict, | |
| inputs = gr.Image( image_mode='L'), | |
| outputs = [gr.Label(label='Emotion'), gr.Label(label='Sentiment')], #gr.Label(), | |
| title = title, | |
| examples = examples, | |
| description = description, | |
| article=article, | |
| allow_flagging='never').launch() |