Duplicate from schibsted/facial_expression_classifier

Co-authored-by: Saikiran Tharimena <drsaikirant88@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,31 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,14 @@
+---
+title: Facial Expression Classifier
+emoji: 🏢
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 3.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: schibsted/facial_expression_classifier
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,84 @@
+# 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)

emotions_vgg19.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:335570ccf5133bc1fc0d4cc9bb7835b72fd457a64c0cdefbbc863f1c24cc4454
+size 82965753

requirements.txt

@@ -0,0 +1 @@
+fastai

sentiment_vgg19.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6fc0f18d5a082cd835b6b8cbd32041170be0ac20b7ca555df1209e2e4d181c3
+size 82957497