Update app.py

app.py

@@ -1,64 +1,35 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the emotion analysis pipeline using a publicly available fine-tuned model
-emotion_analyzer = pipeline(
-    "text-classification", 
-    model="j-hartmann/emotion-english-distilroberta-base"
-)
-
-# Streamlit app setup
-st.set_page_config(page_title="Emotion Wellness App", page_icon="🌺", layout="centered")
-
-st.title("Emotion Wellness Predictor 🌺")
-st.subheader("Discover your emotions and find ways to enhance your well-being.")
-
-# Questions for the user
-st.markdown("### Answer the following questions:")
-q1 = st.text_input("How are you feeling today? (e.g., happy, sad, stressed)")
-q2 = st.text_input("What’s currently on your mind?")
-q3 = st.slider("On a scale of 1-10, how motivated do you feel?", 1, 10)
-
-# Analyze emotions when the button is pressed
-if st.button("Analyze My Emotions"):
-    if q1 and q2:
-        # Combine user inputs into a single string for emotion analysis
-        user_input = f"{q1} {q2}"
-        results = emotion_analyzer(user_input)
-        predicted_emotion = results[0]['label']
-        st.markdown(f"### Detected Emotion: **{predicted_emotion}** 🎭")
-
-        # Suggestions based on emotion
-        st.markdown("### Suggested Activities:")
-        if predicted_emotion == "joy":
-            st.markdown("- 🌊 Take a walk on the beach\n- 🧘‍♀️ Try a yoga session\n- 🎵 Listen to upbeat music")
-            st.markdown("[Explore mindfulness exercises](https://www.mindfulness.org)")
-        elif predicted_emotion == "sadness":
-            st.markdown("- 🌼 Practice gratitude journaling\n- 🧘‍♂️ Meditate for 10 minutes\n- 📖 Read something uplifting")
-            st.markdown("[Watch a calming video](https://www.youtube.com/watch?v=ZQ6aGy7tjTc)")
-        elif predicted_emotion == "anger":
-            st.markdown("- 🏃‍♂️ Go for a run\n- 🌿 Spend time in nature\n- 💬 Talk to a friend")
-            st.markdown("[Explore anger management tips](https://www.mentalhealth.org.uk)")
-        else:
-            st.markdown("- 💡 Stay positive and focus on your goals.")
-
+# 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:
-        st.error("Please answer all questions to analyze your emotions.")
-
-# Footer
-st.markdown("---")
-st.markdown("Built with ❤️ for the Hawaii Hackathon 2024 by [Your Team Name].")
-
-# Deployment Instructions
-st.markdown(
-    """
-    To deploy this app:
-    1. Save this script as `app.py`.
-    2. Create a `requirements.txt` file:
-       ```
-       streamlit
-       transformers
-       ```
-    3. Deploy on Hugging Face Spaces or Streamlit Sharing.
-    """
-)
+        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}")