Update app.py

app.py

@@ -1,107 +1,64 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the emotion analysis pipeline using an open-access model
+# Load the emotion analysis pipeline using a publicly available fine-tuned model
 emotion_analyzer = pipeline(
     "text-classification", 
     model="j-hartmann/emotion-english-distilroberta-base"
 )
 
-# App title and description
-st.set_page_config(page_title="Hawaii Emotion Wellness", layout="centered", page_icon="🌴")
-st.markdown(
-    """
-    <style>
-        body {
-            background-color: #E0F7FA;
-            color: #004D40;
-        }
-        .main-header {
-            font-size: 36px;
-            font-weight: bold;
-            text-align: center;
-            margin-bottom: 10px;
-        }
-        .sub-header {
-            font-size: 18px;
-            text-align: center;
-            margin-bottom: 20px;
-        }
-        .suggestion-card {
-            background-color: #B2EBF2;
-            padding: 15px;
-            border-radius: 8px;
-            margin-bottom: 15px;
-        }
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
-
-st.markdown('<div class="main-header">🌺 Hawaii Emotion Wellness App 🌴</div>', unsafe_allow_html=True)
-st.markdown('<div class="sub-header">Understand your emotions and find the right balance in paradise.</div>', unsafe_allow_html=True)
-
-# Step 1: Collect user's responses
-st.markdown("### Answer these three questions to get started:")
-questions = [
-    "How are you feeling right now? (e.g., stressed, happy, sad)",
-    "What is the most pressing issue on your mind currently?",
-    "On a scale of 1-10, how motivated do you feel to take care of yourself today?",
-]
-
-responses = []
-for question in questions:
-    response = st.text_input(question)
-    responses.append(response)
+# Streamlit app setup
+st.set_page_config(page_title="Emotion Wellness App", page_icon="🌺", layout="centered")
 
-# Analyze the emotions if the user has answered all questions
-if st.button("Analyze Emotions"):
-    if all(responses):
-        # Aggregate responses into a single input for emotion analysis
-        aggregated_response = " ".join(responses)
-        emotion_results = emotion_analyzer(aggregated_response)
+st.title("Emotion Wellness Predictor 🌺")
+st.subheader("Discover your emotions and find ways to enhance your well-being.")
 
-        # Get the most likely emotion
-        predicted_emotion = emotion_results[0]["label"]
-        st.markdown(f"### Your Predicted Emotion: **{predicted_emotion}** 🎭")
+# 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)
 
-        # Provide well-being suggestions
-        st.markdown("### Here's what we recommend for you:")
-        suggestions = {
-            "joy": [
-                {"activity": "Go for a walk on the beach", "url": "https://www.hawaiibeachwalks.com"},
-                {"activity": "Try a short surfing session", "url": "https://www.learnsurf.com"},
-                {"activity": "Join a hula dancing class", "url": "https://www.hulahawaii.com"},
-            ],
-            "sadness": [
-                {"activity": "Practice deep breathing for 5 minutes", "url": "https://www.breathingexercise.com"},
-                {"activity": "Watch a calming ocean video", "url": "https://www.youtube.com/watch?v=lM02vNMRRB0"},
-                {"activity": "Do a quick yoga session", "url": "https://www.doyogawithme.com"},
-            ],
-            # Add more emotions as needed
-        }
+# 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}** 🎭")
 
-        user_suggestions = suggestions.get(predicted_emotion.lower(), [])
-        if user_suggestions:
-            for suggestion in user_suggestions:
-                st.markdown(
-                    f"""
-                    <div class="suggestion-card">
-                        <strong>{suggestion['activity']}</strong>
-                        <br>
-                        <a href="{suggestion['url']}" target="_blank">Learn More</a>
-                    </div>
-                    """,
-                    unsafe_allow_html=True,
-                )
+        # 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! Enjoy the aloha spirit and take a deep breath.")
+            st.markdown("- 💡 Stay positive and focus on your goals.")
+
     else:
-        st.warning("Please answer all the questions to analyze your emotions.")
+        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(
-    "Built with ❤️ for the Hawaii Hackathon 2024 by [Your Team Name]. Deployed on Hugging Face Spaces."
+    """
+    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.
+    """
 )
-