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import os
from fastai.vision.all import *
import gradio as gr
import pickle
import tempfile
from transformers import AutoTokenizer, AutoModelWithLMHead
from speechbrain.inference.interfaces import foreign_class
# Facial expression classifier
# Emotion
learn_emotion = load_learner('emotions_vgg.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']
image_mode=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')
# Txet Model
# Load tokenizer and model from pickles
with open("emotion_tokenizer.pkl", "rb") as f:
tokenizer = pickle.load(f)
with open("emotion_model.pkl", "rb") as f:
model = pickle.load(f)
def classify_emotion(text):
# Tokenize input text and generate output
input_ids = tokenizer.encode("emotion: " + text, return_tensors="pt")
output = model.generate(input_ids)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Classify the emotion into positive, negative, or neutral
if output_text in ["joy", "love"]:
return "Positive"
elif output_text == "surprise":
return "Neutral"
else:
return "Negative"
return output_text
text_model = gr.Interface(fn=classify_emotion, inputs="textbox", outputs="textbox")
# Initialize the classifier
classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
def save_uploaded_file(uploaded_file):
temp_dir = tempfile.TemporaryDirectory()
file_path = os.path.join(temp_dir.name, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
return file_path
def emotion(file_path):
if file_path:
# Classify the file
out_prob, score, index, text_lab = classifier.classify_file(file_path)
if isinstance(text_lab, list):
text_lab = text_lab[0]
# Map the original labels to the desired categories
emotion_mapping = {
'neu': 'Neutral',
'ang': 'Angry',
'hap': 'Happy',
'sad': 'Sadness'
}
# Get the corresponding category from the mapping
emotion_category = emotion_mapping.get(text_lab, 'Unknown')
emotion_category = emotion_mapping.get(text_lab, 'Unknown')
# Return the emotion category
return emotion_category
else:
return "Please provide the path to an audio file."
audio_model = gr.Interface(fn=emotion, inputs="textbox", outputs="textbox")
main_model = gr.TabbedInterface([text_model, image_mode,audio_model], ["Text Emotion Recognition", "Image Emotion Recognition" , "Audio Emotion Recognition"])
if _name_ == "_main_":
main_model.launch() |