Update app.py

app.py

@@ -3,103 +3,99 @@ from transformers import pipeline
 import torch
 import time
 
-# App configuration
+# ---- Page Configuration ----
 st.set_page_config(
-    page_title="Aloha Emotion App 🌴",
-    page_icon="🌺",
+    page_title="Emotion Prediction App",
+    page_icon="🤗",
     layout="centered",
-    initial_sidebar_state="expanded"
+    initial_sidebar_state="expanded",
 )
 
-# Function to load the model with error handling and retries
+# ---- App Title ----
+st.title("🌺 Emotion Prediction App 🌈")
+st.subheader("Aloha! Enter your thoughts and let me predict your emotions. 🧠💡")
+
+# ---- Background Information ----
+st.markdown(
+    """
+    Welcome to the Emotion Prediction App! 
+    This tool uses a state-of-the-art natural language processing (NLP) model to analyze your responses and predict your emotions.
+    Perfect for everyone in Hawaii or anywhere looking for a simple, fun way to understand feelings better! 🌴✨
+    """
+)
+
+# ---- Function to Load Emotion Analysis Model ----
 @st.cache_resource
-def load_model():
+def load_emotion_model():
     try:
-        st.info("🌺 Loading the emotion analysis model...")
-        emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta", device=0 if torch.cuda.is_available() else -1)
+        st.info("⏳ Loading the emotion analysis model, please wait...")
+        # Using a public model for emotion classification
+        model = pipeline(
+            "text-classification",
+            model="bhadresh-savani/distilbert-base-uncased-emotion",
+            device=0 if torch.cuda.is_available() else -1,  # Automatically use GPU if available
+        )
         st.success("✅ Model loaded successfully!")
-        return emotion_analyzer
+        return model
     except Exception as e:
-        st.error(f"Error loading the model: {e}")
+        st.error(f"⚠️ Error loading model: {e}")
         return None
 
-# Function to predict emotion for a single response
-def predict_emotion_single(response, emotion_analyzer):
+# ---- Load the Model ----
+emotion_analyzer = load_emotion_model()
+
+# ---- Function for Predicting Emotion ----
+def predict_emotion(text):
     if emotion_analyzer is None:
-        st.error("❌ Model not loaded. Please try reloading the app.")
-        return {"Error": "Emotion analyzer model not initialized. Please check model loading."}
+        return {"Error": "Emotion analyzer model not initialized. Please reload the app."}
     
     try:
-        # Analyze the emotion of the response
-        result = emotion_analyzer([response])
+        # Analyze emotions
+        result = emotion_analyzer([text])
         return {res["label"]: round(res["score"], 4) for res in result}
     except Exception as e:
-        st.error(f"Prediction failed: {e}")
         return {"Error": f"Prediction failed: {e}"}
 
-# Streamlit App Layout
-st.markdown("<h1 style='text-align: center; color: #2E8B57;'>Aloha Emotion Analyzer 🌺</h1>", unsafe_allow_html=True)
-st.write("**Welcome to the Emotion Analyzer!** Feel the tropical vibes and explore your emotions.")
-
-# Custom background
-page_bg_img = '''
-<style>
-body {
-    background-color: #F5F5DC;
-    background-image: url("https://www.hawaiimagazine.com/wp-content/uploads/2022/06/aloha-background.jpg");
-    background-size: cover;
-}
-</style>
-'''
-st.markdown(page_bg_img, unsafe_allow_html=True)
+# ---- User Input Section ----
+st.write("### 🌟 Let's Get Started!")
 
-# Define questions for the user
 questions = [
-    "🌞 How are you feeling today?",
-    "🌴 Describe your mood in a few words.",
-    "🌊 What was the most significant emotion you felt this week?",
-    "🌺 How do you handle stress or challenges?",
-    "🌈 What motivates you the most right now?"
+    "How are you feeling today?",
+    "Describe your mood in a few words.",
+    "What was the most significant emotion you felt this week?",
+    "How do you handle stress or challenges?",
+    "What motivates you the most right now?",
 ]
 
-# Initialize a dictionary to store responses
 responses = {}
 
-# Initialize the emotion analysis model with retries
-emotion_analyzer = None
-max_retries = 3
-retry_delay = 5  # seconds
-
-# Try loading the model with retries
-for attempt in range(max_retries):
-    emotion_analyzer = load_model()
-    if emotion_analyzer:
-        break
-    if attempt < max_retries - 1:
-        st.warning(f"⚠️ Retrying model load... Attempt {attempt + 2}/{max_retries}")
-        time.sleep(retry_delay)
-    else:
-        st.error("❌ Model failed to load after multiple attempts. Please try again later.")
-
-# Function to handle responses and emotion analysis
+# ---- Ask Questions and Analyze Responses ----
 for i, question in enumerate(questions, start=1):
-    user_response = st.text_input(f"Question {i}: {question}")
+    st.write(f"#### 🧐 Question {i}: {question}")
+    user_response = st.text_input(f"Your answer to Q{i}:", key=f"q{i}")
+    
     if user_response:
-        analysis = predict_emotion_single(user_response, emotion_analyzer)
-        responses[question] = (user_response, analysis)
-        st.write(f"**Your Response:** {user_response}")
-        st.write(f"**Emotion Analysis:** {analysis}")
-
-# Buttons for clearing or submitting responses
-if st.button("Clear Responses"):
-    st.experimental_rerun()
+        with st.spinner("Analyzing emotion... 🎭"):
+            analysis = predict_emotion(user_response)
+        responses[question] = {"Response": user_response, "Analysis": analysis}
+        st.success(f"🎯 Emotion Analysis: {analysis}")
 
+# ---- Display Results ----
 if st.button("Submit Responses"):
+    st.write("### 📊 Emotion Analysis Results")
     if responses:
-        st.markdown("<h3 style='color: #2E8B57;'>🌟 Final Emotion Analysis Results:</h3>", unsafe_allow_html=True)
-        for i, (question, (response, analysis)) in enumerate(responses.items(), start=1):
-            st.write(f"**Question {i}:** {question}")
-            st.write(f"**Your Response:** {response}")
-            st.write(f"**Emotion Analysis:** {analysis}")
+        for i, (question, details) in enumerate(responses.items(), start=1):
+            st.write(f"#### Question {i}: {question}")
+            st.write(f"**Your Response:** {details['Response']}")
+            st.write(f"**Emotion Analysis:** {details['Analysis']}")
     else:
-        st.write("❗ Please answer all the questions before submitting.")
+        st.warning("Please answer at least one question before submitting!")
+
+# ---- Footer ----
+st.markdown(
+    """
+    ---
+    **Developed with 🤗 Transformers by OpenAI**  
+    Designed for an intuitive and aesthetic experience. 🌺  
+    """
+)