Update app.py

app.py

@@ -33,7 +33,7 @@ if hf_token:
 
 # Available emotion detection models
 EMOTION_MODELS = {
-    "AnasAlokla/multilingual_go_emotions": "Multilingual Go Emotions (v1.0)",
+    "AnasAlokla/multilingual_go_emotions": "Multilingual Go Emotions (Original)",
     "AnasAlokla/multilingual_go_emotions_V1.1": "Multilingual Go Emotions (V1.1)"
 }
 
@@ -100,25 +100,57 @@ def get_ai_response(user_input, emotion_predictions):
     else:
         return responses
 
+def display_top_predictions(emotion_predictions, selected_language, num_predictions=10):
+    """Display top emotion predictions in sidebar."""
+    # Sort predictions by score in descending order
+    sorted_predictions = sorted(emotion_predictions, key=lambda x: x['score'], reverse=True)
+    
+    # Take top N predictions
+    top_predictions = sorted_predictions[:num_predictions]
+    
+    # Display in sidebar
+    st.sidebar.markdown("---")
+    st.sidebar.subheader("🎯 Top Emotion Predictions")
+    
+    for i, prediction in enumerate(top_predictions, 1):
+        emotion = prediction['label']
+        score = prediction['score']
+        percentage = score * 100
+        
+        # Create a progress bar for visual representation
+        st.sidebar.markdown(f"**{i}. {emotion.title()}**")
+        st.sidebar.progress(score)
+        st.sidebar.markdown(f"Score: {percentage:.1f}%")
+        st.sidebar.markdown("---")
+
 def main():
     # Sidebar configurations
-    st.sidebar.header("Configuration")
+    st.sidebar.header("⚙️ Configuration")
     
     # Language Selection
     selected_language = st.sidebar.selectbox(
-        "Select Interface Language",
+        "🌐 Select Interface Language",
         list(LANGUAGES.keys()),
         index=0  # Default to English
     )
     
     # Model Selection
     selected_model_key = st.sidebar.selectbox(
-        "Select Emotion Detection Model",
+        "🤖 Select Emotion Detection Model",
         list(EMOTION_MODELS.keys()),
         format_func=lambda x: EMOTION_MODELS[x],
         index=0  # Default to first model
     )
     
+    # Number of predictions to show in sidebar
+    num_predictions = st.sidebar.slider(
+        "📊 Number of predictions to show",
+        min_value=5,
+        max_value=15,
+        value=10,
+        step=1
+    )
+    
     # Load the selected emotion classifier
     emotion_classifier = load_emotion_classifier(selected_model_key)
     
@@ -128,18 +160,21 @@ def main():
         return
     
     # Display selected model info
-    st.sidebar.info(f"Current Model: {EMOTION_MODELS[selected_model_key]}")
+    st.sidebar.success(f"✅ Current Model: {EMOTION_MODELS[selected_model_key]}")
     
     # Display Image
     st.image('chatBot_image.jpg', channels='RGB')
     
     # Set page title and header based on selected language
     st.title(LANGUAGES[selected_language]['title'])
+    st.markdown("### 💬 Enter your text to analyze emotions and get AI response")
     
     # Input Text Box
-    user_input = st.text_input(
-        LANGUAGES[selected_language]['input_placeholder'], 
-        ""
+    user_input = st.text_area(
+        LANGUAGES[selected_language]['input_placeholder'],
+        "",
+        height=100,
+        help="Type your message here to analyze emotions"
     )
     
     if user_input:
@@ -147,10 +182,35 @@ def main():
         with st.spinner("Analyzing emotions..."):
             emotion_predictions = emotion_classifier(user_input)
         
-        # Display Emotions
+        # Display top predictions in sidebar
+        display_top_predictions(emotion_predictions, selected_language, num_predictions)
+        
+        # Display Emotions in main area (top 5)
         st.subheader(LANGUAGES[selected_language]['emotions_header'])
-        for prediction in emotion_predictions:
-            st.write(f"- {prediction['label']}: {prediction['score']:.2f}")
+        top_5_emotions = sorted(emotion_predictions, key=lambda x: x['score'], reverse=True)[:5]
+        
+        # Create columns for better display
+        col1, col2 = st.columns(2)
+        
+        for i, prediction in enumerate(top_5_emotions):
+            emotion = prediction['label']
+            score = prediction['score']
+            percentage = score * 100
+            
+            if i % 2 == 0:
+                with col1:
+                    st.metric(
+                        label=emotion.title(),
+                        value=f"{percentage:.1f}%",
+                        delta=None
+                    )
+            else:
+                with col2:
+                    st.metric(
+                        label=emotion.title(),
+                        value=f"{percentage:.1f}%",
+                        delta=None
+                    )
         
         # Get AI Response
         with st.spinner("Generating response..."):