| |
| import os |
| from fastai.vision.all import * |
| import gradio as gr |
|
|
| |
| learn_emotion = load_learner('emotions_vgg19.pkl') |
| learn_emotion_labels = learn_emotion.dls.vocab |
|
|
| |
| learn_sentiment = load_learner('sentiment_vgg19.pkl') |
| learn_sentiment_labels = learn_sentiment.dls.vocab |
|
|
| |
| 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 = {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] |
|
|
| |
| 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 |
| interpret results at your own risk! It was built as a demo for AI course. Samples images |
| were downloaded from VG & AftenPosten news webpages. Copyrights belong to respective |
| brands. All rights reserved. |
| |
| **PREMISE:** The idea is to determine an overall sentiment of a news site on a daily basis |
| based on the pictures. We are restricting pictures to only include close-up facial |
| images. |
| |
| **DATA:** FER2013 dataset consists of 48x48 pixel grayscale images of faces. There are 28,709 |
| images in the training set and 3,589 images in the test set. However, for this demo all |
| pictures were combined into a single dataset and 80:20 split was used for training. Images |
| are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. |
| In addition to these 7 classes, images were re-classified into 3 sentiment categories based |
| on emotions: |
| |
| Positive (Happy, Surprise) |
| |
| Negative (Angry, Disgust, Fear, Sad) |
| |
| Neutral (Neutral) |
| |
| FER2013 (preliminary version) dataset can be downloaded at: |
| https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data |
| |
| **MODEL:** VGG19 was used as the base model and trained on FER2013 dataset. Model was trained |
| using PyTorch and FastAI. Two models were trained, one for detecting emotion and the other |
| for detecting sentiment. Although, this could have been done with just one model, here two |
| models were trained for the demo.""").value |
|
|
| enable_queue=True |
|
|
| examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg'] |
|
|
| gr.Interface(fn = predict, |
| inputs = gr.Image(shape=(48, 48), image_mode='L'), |
| outputs = [gr.Label(label='Emotion'), gr.Label(label='Sentiment')], |
| title = title, |
| examples = examples, |
| description = description, |
| article=article, |
| allow_flagging='never').launch(enable_queue=enable_queue) |
