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import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
# Load the emotion prediction model
@st.cache_resource
def load_model():
try:
# Use Hugging Face's pipeline for text classification
emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
return emotion_classifier
except Exception as e:
st.error(f"Error loading model: {str(e)}")
return None
emotion_classifier = load_model()
# Well-being suggestions based on emotions
def get_well_being_suggestions(emotion):
suggestions = {
"joy": {
"text": "You're feeling joyful! Keep the positivity going.",
"links": [
"https://www.nih.gov/health-information/emotional-wellness-toolkit",
"https://www.health.harvard.edu/health-a-to-z",
"https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"
],
"videos": [
"https://youtu.be/m1vaUGtyo-A",
"https://youtu.be/MIc299Flibs"
]
},
"anger": {
"text": "You're feeling angry. Take a moment to calm down.",
"links": [
"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
"https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"
],
"videos": [
"https://youtu.be/m1vaUGtyo-A",
"https://www.youtube.com/shorts/fwH8Ygb0K60?feature=share"
]
},
"sadness": {
"text": "You're feeling sad. It's okay to take a break.",
"links": [
"https://www.nih.gov/health-information/emotional-wellness-toolkit",
"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"
],
"videos": [
"https://youtu.be/-e-4Kx5px_I",
"https://youtu.be/Y8HIFRPU6pM"
]
},
"fear": {
"text": "You're feeling fearful. Try some relaxation techniques.",
"links": [
"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
"https://www.health.harvard.edu/health-a-to-z"
],
"videos": [
"https://www.youtube.com/shorts/Tq49ajl7c8Q?feature=share",
"https://youtu.be/yGKKz185M5o"
]
},
"disgust": {
"text": "You're feeling disgusted. Take a deep breath and refocus.",
"links": [
"https://www.health.harvard.edu/health-a-to-z",
"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"
],
"videos": [
"https://youtu.be/MIc299Flibs",
"https://youtu.be/-e-4Kx5px_I"
]
},
}
return suggestions.get(emotion, {
"text": "Feeling neutral? That's okay! Take care of your mental health.",
"links": [],
"videos": []
})
# Streamlit UI
def main():
# Set the background image
st.markdown("""
<style>
.stApp {
background-image: url('https://www.example.com/your-image.jpg');
background-size: cover;
background-position: center;
}
</style>
""", unsafe_allow_html=True)
# Title of the app
st.title("Emotion Prediction and Well-being Suggestions")
# User input for emotional state
st.header("Tell us how you're feeling today!")
user_input = st.text_area("Enter a short sentence about your current mood:", "")
if user_input:
# Use the model to predict emotion
try:
result = emotion_classifier(user_input)
emotion = result[0]['label'].lower()
st.subheader(f"Emotion Detected: {emotion.capitalize()}")
# Get well-being suggestions based on emotion
suggestions = get_well_being_suggestions(emotion)
# Display text suggestions
st.write(suggestions["text"])
# Display links
if suggestions["links"]:
st.write("Useful Resources:")
for link in suggestions["links"]:
st.markdown(f"[{link}]({link})")
# Display video links
if suggestions["videos"]:
st.write("Relaxation Videos:")
for video in suggestions["videos"]:
st.markdown(f"[Watch here]({video})")
# Add a button for a summary
if st.button('Summary'):
st.write(f"Emotion detected: {emotion.capitalize()}. Here are your well-being suggestions to enhance your mood.")
st.write("Explore the links and videos to improve your emotional health!")
except Exception as e:
st.error(f"Error predicting emotion: {str(e)}")
# Run the Streamlit app
if __name__ == "__main__":
main()
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