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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']})") | |