Update app.py

app.py

@@ -21,7 +21,7 @@ sia = SentimentIntensityAnalyzer()
 # Load English emotion detection pipeline
 emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
 
-# Load a **fully compatible** multilingual sentiment analysis model
+# Load multilingual sentiment model (returns star ratings 1-5 for non-English texts)
 multilingual_pipeline = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
 
 # Sample texts
@@ -76,35 +76,46 @@ if text_option == "Choose Your Own Text":
 if text_option == "Choose Your Own Text" and word_count <= MAX_WORDS:
     st.session_state.user_text = text
 
+# Function to convert star ratings (1-5) into emotion categories
+def convert_star_rating_to_emotion(stars):
+    if stars == "1 star":
+        return "very negative"
+    elif stars == "2 stars":
+        return "negative"
+    elif stars == "3 stars":
+        return "neutral"
+    elif stars == "4 stars":
+        return "positive"
+    elif stars == "5 stars":
+        return "very positive"
+    else:
+        return "unknown"
+
 # Function to generate AI-driven feedback
 def generate_feedback(sentiment_score, top_emotion):
-    if sentiment_score['pos'] > 0.6:
+    if sentiment_score['pos'] > 0.6 or top_emotion in ["very positive", "positive", "joy", "optimism"]:
         return "😊 Your reading is positive! This text might boost engagement and motivation."
-    elif sentiment_score['neg'] > 0.6:
+    elif sentiment_score['neg'] > 0.6 or top_emotion in ["very negative", "negative", "anger", "disgust"]:
         return "😔 The text seems to have a strong negative tone. Consider balancing it with positive information."
-    elif top_emotion in ["anger", "disgust"]:
-        return "😡 This text might evoke strong emotions. Reflect on how it makes you feel and why."
-    elif top_emotion in ["joy", "optimism"]:
-        return "🎉 This is an engaging and uplifting text. Keep exploring such content!"
-    else:
+    elif top_emotion in ["neutral"]:
         return "🤔 The text is neutral. You might want to explore different perspectives."
+    else:
+        return "⚖️ The analysis is inconclusive. Try refining your text for clearer sentiment."
 
 # Sentiment Analysis
 if st.button("Analyze Engagement"):
     if text and (text_option != "Choose Your Own Text" or word_count <= MAX_WORDS):
-        # Sentiment Analysis
+        # Sentiment Analysis (English)
         sentiment_score = sia.polarity_scores(text)
 
         # Emotion Detection
         if selected_language == "English":
             emotion_results = emotion_pipeline(text)
             top_emotion = max(emotion_results[0], key=lambda x: x['score'])['label']
-            emotion_scores = {e['label']: e['score'] for e in emotion_results[0]}
         else:
-            # Multilingual Sentiment Analysis (Now Fully Supported)
+            # Multilingual Sentiment Analysis (Converts Star Ratings to Emotion)
             multilingual_results = multilingual_pipeline(text)
-            top_emotion = multilingual_results[0]["label"]
-            emotion_scores = {top_emotion: multilingual_results[0]["score"]}
+            top_emotion = convert_star_rating_to_emotion(multilingual_results[0]["label"])
 
         # Display Sentiment
         st.subheader("📊 Sentiment Analysis")
@@ -114,20 +125,11 @@ if st.button("Analyze Engagement"):
         st.subheader("🎭 Emotion Detection")
         st.write(f"Top Emotion: **{top_emotion.capitalize()}**")
 
-        # Display Multiple Emotion Scores
-        st.subheader("🔍 Expanded Emotion Insights")
-        for emotion, score in emotion_scores.items():
-            st.write(f"**{emotion.capitalize()}**: {score:.2f}")
-
         # AI-Generated Feedback
         st.subheader("💡 AI-Generated Feedback")
         feedback = generate_feedback(sentiment_score, top_emotion)
         st.write(feedback)
 
-        # Visualization
-        fig, ax = plt.subplots()
-        ax.bar(emotion_scores.keys(), emotion_scores.values())
-        st.pyplot(fig)
     elif text_option == "Choose Your Own Text" and word_count > MAX_WORDS:
         st.warning("⚠️ Please reduce the text length to analyze.")
     else: