# 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))}
        
    return {**emotions, **sentiments}

# Gradio
title = "Facial Expression Sentiment Classifier"
description = "A model to detect emotion and sentiment from facial expressions trained on FER2013 dataset using FastAi. Created as a demo for AI Course."
article = 'Sample images are taken from VG & AftenPoften webpages. Copyrights belong to respective brands. All rights reserved.'
interpretation='default'
enable_queue=True

examples = ['happy1.jpg', 'happy2.jpg', 'angry1.jpg', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg']

gr.Interface(fn = predict, 
             inputs = gr.Image(shape=(48, 48), image_mode='L'), 
             outputs = gr.Label(),
             title = title,
             description = description,
             article=article).launch(share=True, enable_queue=enable_queue)