Update app.py

app.py

@@ -4,6 +4,58 @@ 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")
 
@@ -17,20 +69,20 @@ if st.button("Get Suggestions"):
     user_input = f"{question1} {question2} {question3}"
     
     # Perform emotion analysis
-    emotion = emotion_analyzer(user_input)
+    emotion_analysis = 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
+    # Get suggestions, articles, and videos based on the emotion
+    resources = suggest_activity(emotion_analysis)
     
-    # Show suggestions and video
-    st.write(suggestion)
-    st.write(f"Check out this video for more guidance: {video_url}")
-
+    # 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']})")