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import random
import streamlit as st
from transformers import pipeline
# Load the emotion analyzer model
emotion_analyzer = pipeline("text-classification", model="distilbert-base-uncased")
# Define the questions for mood analysis
questions = [
"How are you feeling today in one word?",
"What's currently on your mind?",
"Do you feel calm or overwhelmed right now?",
]
# Define a suggestion database for different moods
suggestion_database = {
"NEGATIVE": {
"suggestions": ["Try guided meditation", "Take a walk in nature", "Connect with a loved one"],
"articles": [
{"title": "Emotional Wellness Toolkit", "url": "https://www.nih.gov/health-information/emotional-wellness-toolkit"},
{"title": "Understanding Anxiety", "url": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
],
"videos": [
{"title": "Mindfulness for Calm", "url": "https://youtu.be/m1vaUGtyo-A"},
{"title": "Relaxation Techniques", "url": "https://www.youtube.com/shorts/Tq49ajl7c8Q?feature=share"},
],
},
"POSITIVE": {
"suggestions": ["Practice gratitude", "Engage in a hobby", "Celebrate your wins"],
"articles": [
{"title": "Benefits of Joy", "url": "https://www.health.harvard.edu/health-a-to-z"},
{"title": "Gratitude Practices", "url": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"},
],
"videos": [
{"title": "Boosting Happiness", "url": "https://youtu.be/MIc299Flibs"},
{"title": "Celebrating Wins", "url": "https://www.youtube.com/shorts/fwH8Ygb0K60?feature=share"},
],
},
"NEUTRAL": {
"suggestions": ["Take a short break", "Engage in a relaxing activity", "Spend time outdoors"],
"articles": [
{"title": "Self-Care Practices", "url": "https://www.nih.gov/health-information/emotional-wellness-toolkit"},
{"title": "Stress Management", "url": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
],
"videos": [
{"title": "Relaxation Exercises", "url": "https://youtu.be/Y8HIFRPU6pM"},
{"title": "Mindfulness Tips", "url": "https://youtu.be/-e-4Kx5px_I"},
],
},
}
# Function to fetch relevant resources based on detected mood
def get_relevant_resources(mood):
resources = suggestion_database.get(mood, {})
return resources.get("suggestions", []), resources.get("articles", []), resources.get("videos", [])
# Function to suggest activities based on the mood
def suggest_activity(mood):
suggestions, articles, videos = get_relevant_resources(mood)
return {
"suggestions": suggestions,
"articles": articles,
"videos": videos,
}
# Streamlit app
def main():
st.title("Mood Analysis and Suggestions")
st.write("Answer the following 3 questions to help us understand your mood:")
# Collect responses
responses = []
for i, question in enumerate(questions):
response = st.text_input(f"{i+1}. {question}")
if response:
responses.append(response)
# Analyze responses if all questions are answered
if len(responses) == len(questions):
combined_text = " ".join(responses)
analysis_result = emotion_analyzer(combined_text)
detected_emotion = analysis_result[0]['label']
# Map detected emotion to a mood state
mood_mapping = {
"LABEL_0": "NEGATIVE", # for negative emotions
"LABEL_1": "POSITIVE", # for positive emotions
"LABEL_2": "NEUTRAL", # for neutral emotions
}
# Map the detected emotion into a proper mood
mood = mood_mapping.get(detected_emotion, "NEUTRAL")
st.write(f"Detected Mood: {mood}")
# Fetch suggestions based on mood
resources = suggest_activity(mood)
# Display suggestions
st.write("### Suggestions")
for suggestion in resources["suggestions"]:
st.write(f"- {suggestion}")
# Display articles
st.write("### Articles")
for article in resources["articles"]:
st.write(f"- [{article['title']}]({article['url']})")
# Display videos
st.write("### Videos")
for video in resources["videos"]:
st.write(f"- [{video['title']}]({video['url']})")
else:
st.write("Please answer all 3 questions to receive suggestions.")
if __name__ == "__main__":
main()
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