app.py

import streamlit as st
from transformers import pipeline

# Load the emotion classification model
emotion_analyzer = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment")

# Streamlit app interface
st.title("Emotion Prediction and Well-Being Suggestions")

# Ask 3 questions to the user
question1 = st.text_input("How are you feeling today?")
question2 = st.text_input("Have you felt stressed recently?")
question3 = st.text_input("Do you have enough time for relaxation?")

if st.button("Get Suggestions"):
    # Combine responses to analyze sentiment
    user_input = f"{question1} {question2} {question3}"
    
    # Perform emotion analysis
    emotion = emotion_analyzer(user_input)
    
    # Provide suggestions based on emotion
    if "positive" in emotion[0]['label'].lower():
        suggestion = "It's great to hear that you're feeling positive! Consider doing a relaxing activity like walking on the beach or practicing mindfulness."
        video_url = "https://www.youtube.com/watch?v=1LkV2STw2so"  # Example relaxation video
    elif "negative" in emotion[0]['label'].lower():
        suggestion = "It seems like you're experiencing some stress. Try deep breathing exercises or a walk in nature to relieve tension."
        video_url = "https://www.youtube.com/watch?v=5J5nZ9Tr6Cw"  # Example deep breathing exercise video
    else:
        suggestion = "It seems like you're having a mixed experience. Balance your day with some light exercise like hula dancing or yoga."
        video_url = "https://www.youtube.com/watch?v=Hk4pJOPsFq4"  # Example hula dancing video
    
    # Show suggestions and video
    st.write(suggestion)
    st.write(f"Check out this video for more guidance: {video_url}")