Spaces:
Runtime error
Runtime error
# 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 | |
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')], #gr.Label(), | |
title = title, | |
examples = examples, | |
description = description, | |
article=article, | |
allow_flagging='never').launch(enable_queue=enable_queue) |