update: description

app.py

@@ -28,15 +28,48 @@ def predict(img):
     return [emotions, sentiments] #{**emotions, **sentiments}
 
 # Gradio
-title = "Facial Expression Sentiment Classifier"
-description = "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. Be sure to only include face to get best results. Check some sample images at the bottom for " + \
-              "inspiration!"
-article = "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. The model is trained on FER2013 dataset using FastAI. Sample images are taken " + \
-          "from VG & AftenPoften webpages. Copyrights belong to respective brands. All rights reserved."
-interpretation='default'
+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']
@@ -47,4 +80,5 @@ gr.Interface(fn = predict,
              title = title,
              examples = examples,
              description = description,
-             article=article).launch(enable_queue=enable_queue)
+             article=article,
+             allow_flagging='never').launch(enable_queue=enable_queue)