Update app.py

app.py

@@ -1,56 +1,2 @@
-# 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
-
-
-# Predict
-def predict(img):
-    img = PILImage.create(img)
-    pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(img)
-    predicted_emotion = learn_emotion_labels[pred_emotion_idx]
-    return predicted_emotion
-
-
-# Gradio
-title = "Facial Emotion 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. 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!. 
-
-             **PREMISE:** The idea is to determine an overall emotion of a person
-             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.Images
-             are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral.
-             
-              """).value
-
-enable_queue=True
-
-examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg']
-
-gr.Interface(fn = predict, 
-             inputs = gr.Image( image_mode='L'), 
-             outputs = [gr.Label(label='Emotion')], #gr.Label(),
-             title = title,
-             examples = examples,
-             description = description,
-             article=article,
-             allow_flagging='never').launch()