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Update app.py
Browse files
app.py
CHANGED
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from transformers import pipeline
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#
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st.set_page_config(page_title="Emotion Detection and Well-Being Suggestions", layout="wide")
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# Load pre-trained model and tokenizer
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@st.cache_resource
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def load_model():
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#
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<style>
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background-image: url('https://
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background-size: cover;
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background-
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background-attachment: fixed;
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color: white;
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}
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.stButton button {
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background-color: #6c63ff;
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color: white;
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font-size: 20px;
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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# Display header
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st.title("Emotion Detection and Well-Being Suggestions")
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# User input for text (emotion detection)
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user_input = st.text_area("How are you feeling today?", "Enter your thoughts here...")
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# Model prediction
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if user_input:
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pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
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result = pipe(user_input)
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# Extracting the emotion from the model's result
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emotion = result[0]['label']
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# Display emotion
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st.write(f"**Emotion Detected:** {emotion}")
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#
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elif emotion == 'anger':
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st.write("You're feeling angry. It's okay to feel this way. Let's try to calm down.")
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st.write("Useful Resources:")
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st.markdown("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
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st.write("[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)")
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st.write("[Dealing with Anger](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
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st.write("Relaxation Videos:")
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st.markdown("[Watch on YouTube](https://youtu.be/MIc299Flibs)")
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elif emotion == 'fear':
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st.write("You're feeling fearful. Take a moment to breathe and relax.")
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st.write("Useful Resources:")
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st.markdown("[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
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st.write("[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
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st.write("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
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st.write("Relaxation Videos:")
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st.markdown("[Watch on YouTube](https://youtu.be/-e-4Kx5px_I)")
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st.write("Useful Resources based on your mood:")
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if emotion == 'joy':
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st.write("[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
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elif emotion == 'anger':
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st.write("[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)")
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elif emotion == 'fear':
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st.write("[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
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elif emotion == 'sadness':
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st.write("[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
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elif emotion == 'surprise':
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st.write("[Managing Stress](https://www.health.harvard.edu/health-a-to-z)")
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import streamlit as st
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from transformers import pipeline
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# Load emotion classification model
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@st.cache_resource
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def load_model():
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try:
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emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
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return emotion_classifier
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except Exception as e:
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st.error(f"Error loading model: {str(e)}")
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return None
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emotion_classifier = load_model()
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# Well-being suggestions based on emotions
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def get_well_being_suggestions(emotion):
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suggestions = {
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"joy": {
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"text": "You're feeling joyful! Keep the positivity going.",
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"links": [
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"https://www.nih.gov/health-information/emotional-wellness-toolkit",
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"https://www.health.harvard.edu/health-a-to-z",
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"https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"
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],
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"videos": [
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"https://youtu.be/m1vaUGtyo-A",
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"https://youtu.be/MIc299Flibs"
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]
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},
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"anger": {
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"text": "You're feeling angry. Take a moment to calm down.",
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"links": [
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"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
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"https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"
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],
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"videos": [
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"https://youtu.be/m1vaUGtyo-A",
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"https://www.youtube.com/shorts/fwH8Ygb0K60?feature=share"
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]
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},
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"sadness": {
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"text": "You're feeling sad. It's okay to take a break.",
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"links": [
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"https://www.nih.gov/health-information/emotional-wellness-toolkit",
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"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"
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],
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"videos": [
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"https://youtu.be/-e-4Kx5px_I",
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"https://youtu.be/Y8HIFRPU6pM"
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]
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},
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"fear": {
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"text": "You're feeling fearful. Try some relaxation techniques.",
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"links": [
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"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
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"https://www.health.harvard.edu/health-a-to-z"
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],
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"videos": [
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"https://www.youtube.com/shorts/Tq49ajl7c8Q?feature=share",
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"https://youtu.be/yGKKz185M5o"
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]
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},
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"disgust": {
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"text": "You're feeling disgusted. Take a deep breath and refocus.",
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"links": [
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"https://www.health.harvard.edu/health-a-to-z",
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"https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"
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],
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"videos": [
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"https://youtu.be/MIc299Flibs",
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"https://youtu.be/-e-4Kx5px_I"
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]
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},
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}
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return suggestions.get(emotion, {
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"text": "Feeling neutral? That's okay! Take care of your mental health.",
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"links": [],
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"videos": []
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})
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# Streamlit UI
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def main():
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# Set the background image
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st.markdown("""
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<style>
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.stApp {
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background-image: url('https://www.example.com/your-image.jpg');
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background-size: cover;
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background-position: center;
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}
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</style>
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""", unsafe_allow_html=True)
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# Title of the app
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st.title("Emotion Prediction and Well-being Suggestions")
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# User input for emotional state
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st.header("Tell us how you're feeling today!")
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user_input = st.text_area("Enter a short sentence about your current mood:", "")
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if user_input:
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# Clean the input text (stripping unnecessary spaces, lowercasing)
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clean_input = user_input.strip().lower()
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# Use the model to predict emotion
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try:
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result = emotion_classifier(clean_input)
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st.write(f"Raw Model Result: {result}") # Debug output to see raw result
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emotion = result[0]['label'].lower()
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st.subheader(f"Emotion Detected: {emotion.capitalize()}")
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# Get well-being suggestions based on emotion
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suggestions = get_well_being_suggestions(emotion)
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# Display text suggestions
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st.write(suggestions["text"])
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# Display links
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if suggestions["links"]:
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st.write("Useful Resources:")
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for link in suggestions["links"]:
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st.markdown(f"[{link}]({link})")
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# Display video links
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if suggestions["videos"]:
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st.write("Relaxation Videos:")
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for video in suggestions["videos"]:
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st.markdown(f"[Watch here]({video})")
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# Add a button for a summary
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if st.button('Summary'):
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st.write(f"Emotion detected: {emotion.capitalize()}. Here are your well-being suggestions to enhance your mood.")
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st.write("Explore the links and videos to improve your emotional health!")
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except Exception as e:
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st.error(f"Error predicting emotion: {str(e)}")
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# Run the Streamlit app
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if __name__ == "__main__":
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main()
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