initial commit

app.py

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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))}
+        
+    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)

requirements.txt

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+fastai