Update app.py

app.py

@@ -9,13 +9,13 @@ from sklearn.pipeline import make_pipeline
 # Load multi-class sentiment analysis model
 sentiment_model = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment", top_k=None)
 
-# Define possible sentiment classes based on the actual model output
+# Define possible sentiment classes with a reduced, logical set
 label_mapping = {
-    "LABEL_0": "very negative",
-    "LABEL_1": "negative",
-    "LABEL_2": "neutral",
-    "LABEL_3": "positive",
-    "LABEL_4": "very positive"
+    "LABEL_0": "negative",
+    "LABEL_1": "neutral",
+    "LABEL_2": "positive",
+    "LABEL_3": "anger",
+    "LABEL_4": "chill"
 }
 
 # Function to get sentiment prediction
@@ -28,7 +28,7 @@ def analyze_sentiment(text):
 
 # Suggest test cases to ensure correct labeling
 def get_suggestions():
-    return "Try these examples:\n- 'I love this! Best experience ever!' (very positive)\n- 'I am so happy today!' (positive)\n- 'It was okay, nothing special.' (neutral)\n- 'I am disappointed with this product.' (negative)\n- 'This is the worst day of my life.' (very negative)"
+    return "Try these examples:\n- 'I love this! Best experience ever!' (positive)\n- 'I am so happy today!' (positive)\n- 'It was okay, nothing special.' (neutral)\n- 'I am disappointed with this product.' (negative)\n- 'This is the worst day of my life.' (negative)\n- 'I am furious right now!' (anger)\n- 'I am extremely relaxed and enjoying the moment.' (chill)"
 
 # Explainability function using LIME
 def explain_prediction(text):
@@ -47,15 +47,65 @@ iface = gr.Interface(
     inputs="text", 
     outputs="text",
     title="Multi-Class Sentiment Analysis App",
-    description="Enter a sentence to analyze its sentiment across multiple categories.",
+    description="Enter a sentence to analyze its sentiment across multiple categories (Negative, Neutral, Positive, Anger, Chill).",
     live=True,
     examples=[
         ["I love this! Best experience ever!"],
         ["I am so happy today!"],
         ["It was okay, nothing special."],
         ["I am disappointed with this product."],
-        ["This is the worst day of my life."]
+        ["This is the worst day of my life."],
+        ["The movie was fantastic, I really enjoyed it!"],
+        ["I am so angry, I can't believe this happened!"],
+        ["I feel completely at peace right now."],
+        ["The service was terrible, I wouldn’t recommend this place."],
+        ["I feel great today, everything is going well!"],
+        ["It’s just another day, nothing special to report."],
+        ["This food is awful, I can’t even eat it!"],
+        ["The book was so engaging, I couldn’t put it down!"],
+        ["I don’t really have an opinion on this matter."],
+        ["My day has been okay, not good but not bad either."],
+        ["I regret buying this product, it’s a waste of money."],
+        ["The customer support was helpful and solved my issue quickly."],
+        ["This experience has been quite frustrating, honestly."],
+        ["I had fun at the party, it was a great time!"],
+        ["There was too much traffic today, it was so annoying."],
+        ["I appreciate your help, it really made a difference."],
+        ["The test was hard, but I think I did okay."],
+        ["I wouldn’t buy this again, it didn’t meet my expectations."],
+        ["This new update has improved the app significantly!"],
+        ["I’m not sure how I feel about this decision."],
+        ["Everything went smoothly today, no issues at all."],
+        ["The weather is nice today, not too hot or too cold."],
+        ["I had a terrible time at the event, it was poorly organized."],
+        ["My experience was neutral, I don’t have strong feelings either way."],
+        ["I highly recommend this to everyone, it’s fantastic!"],
+        ["This place is so relaxing, I could stay here forever."],
+        ["I had a bad day, but I’ll get through it."],
+        ["The lecture was informative, I learned a lot."],
+        ["It’s neither good nor bad, just okay overall."],
+        ["The store was crowded and the staff was rude, not a good experience."],
+        ["I’m satisfied with my purchase, it met my expectations."],
+        ["This situation is frustrating, I don’t know what to do."],
+        ["I’m feeling optimistic about the future!"],
+        ["It was a boring day, nothing interesting happened."],
+        ["I love spending time with my friends, they make me happy."],
+        ["The flight was delayed, but at least I got home safely."],
+        ["This dessert is absolutely delicious, I need more!"],
+        ["I wish things had gone differently, but it’s okay."],
+        ["The staff was unfriendly, I didn’t feel welcome at all."],
+        ["I had a productive day, I got a lot of work done."],
+        ["This movie was neither exciting nor dull, just in between."],
+        ["I’m really grateful for your kindness, it means a lot."],
+        ["I have no strong opinion about this, it’s just okay."],
+        ["The food was decent, but I’ve had better."],
+        ["Everything was perfect, I couldn’t have asked for more!"],
+        ["The trip was stressful, nothing went according to plan."],
+        ["I’m hopeful that things will get better soon."],
+        ["The presentation was well done, I was impressed."],
+        ["I feel indifferent about this, it doesn’t affect me much."],
+        ["The concert was amazing, I had a blast!"]
     ]
 )
 
-iface.launch()
+iface.launch()