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import streamlit as st
from transformers import pipeline

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

# Enhanced Suggestion Database (Now includes resources)
suggestion_database = {
    "sadness": {
        "suggestions": ["Try a guided meditation", "Take a walk in nature", "Connect with a friend"],
        "articles": [
            {"title": "Overcoming Sadness", "url": "https://example.com/sadness1"},
            {"title": "Understanding Depression", "url": "https://example.com/sadness2"},
        ],
        "videos": [
            {"title": "Mindfulness for Sadness", "url": "https://www.youtube.com/watch?v=sadnessvideo1"},
            {"title": "Coping with Grief", "url": "https://www.youtube.com/watch?v=sadnessvideo2"},
        ],
    },
    "joy": {
        "suggestions": ["Practice gratitude", "Engage in a hobby", "Spend time with loved ones"],
        "articles": [
            {"title": "The Benefits of Joy", "url": "https://example.com/joy1"},
            {"title": "Maintaining Positive Emotions", "url": "https://example.com/joy2"},
        ],
        "videos": [
            {"title": "Boosting Your Happiness", "url": "https://www.youtube.com/watch?v=joyvideo1"},
            {"title": "Practicing Gratitude", "url": "https://www.youtube.com/watch?v=joyvideo2"},
        ],
    },
    "neutral": {
        "suggestions": ["Take a break", "Engage in a relaxing activity", "Spend time in nature"],
        "articles": [
            {"title": "Importance of Self-Care", "url": "https://example.com/selfcare1"},
            {"title": "Stress Management Techniques", "url": "https://example.com/stress1"},
        ],
        "videos": [
            {"title": "Relaxation Techniques", "url": "https://www.youtube.com/watch?v=relaxvideo1"},
            {"title": "Mindfulness Exercises", "url": "https://www.youtube.com/watch?v=mindfulnessvideo1"},
        ],
    }
}

# Function to fetch relevant resources based on emotion
def get_relevant_resources(emotion):
    resources = suggestion_database.get(emotion, {})
    return resources.get("suggestions", []), resources.get("articles", []), resources.get("videos", [])

# Enhanced Suggestion Function
def suggest_activity(emotion_analysis):
    max_emotion = max(emotion_analysis, key=emotion_analysis.get) if emotion_analysis else "neutral"
    suggestions, articles, videos = get_relevant_resources(max_emotion)
    return {
        "suggestions": suggestions,
        "articles": articles,
        "videos": videos,
    }

# 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_analysis = emotion_analyzer(user_input)
    
    # Get suggestions, articles, and videos based on the emotion
    resources = suggest_activity(emotion_analysis)
    
    # Display suggestions, articles, and videos
    st.write("Here are some suggestions to help you:")
    for suggestion in resources["suggestions"]:
        st.write(f"- {suggestion}")
    
    st.write("Articles you may find helpful:")
    for article in resources["articles"]:
        st.markdown(f"[{article['title']}]({article['url']})")
    
    st.write("Videos you may find helpful:")
    for video in resources["videos"]:
        st.markdown(f"[{video['title']}]({video['url']})")