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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 = {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] |
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title = "Facial Emotion and Sentiment 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. Voila! 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! It was built as a demo for AI course. Samples images |
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were downloaded from VG & AftenPosten news webpages. Copyrights belong to respective |
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brands. All rights reserved. |
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**PREMISE:** The idea is to determine an overall sentiment of a news site on a daily basis |
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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. There are 28,709 |
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images in the training set and 3,589 images in the test set. However, for this demo all |
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pictures were combined into a single dataset and 80:20 split was used for training. Images |
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are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. |
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In addition to these 7 classes, images were re-classified into 3 sentiment categories based |
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on emotions: |
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Positive (Happy, Surprise) |
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Negative (Angry, Disgust, Fear, Sad) |
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Neutral (Neutral) |
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FER2013 (preliminary version) dataset can be downloaded at: |
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https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data |
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**MODEL:** VGG19 was used as the base model and trained on FER2013 dataset. Model was trained |
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using PyTorch and FastAI. Two models were trained, one for detecting emotion and the other |
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for detecting sentiment. Although, this could have been done with just one model, here two |
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models were trained for the demo.""").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(shape=(48, 48), image_mode='L'), |
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outputs = [gr.Label(label='Emotion'), gr.Label(label='Sentiment')], |
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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(enable_queue=enable_queue) |
