see ALL the emotions, even the ones with low scores

app.py

@@ -4,11 +4,21 @@ from transformers import pipeline
 # Load the Hugging Face pipeline
 classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
 
+# Define the full list of possible emotions (based on the model output structure)
+ALL_EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"]
+
 def classify_emotion(text):
     # Make predictions using the Hugging Face pipeline
     predictions = classifier(text)
-    # Convert predictions to a dictionary for gr.Label output
-    return {item["label"]: item["score"] for item in predictions}
+    
+    # Initialize a dictionary with all emotions and a default score of 0
+    emotion_scores = {emotion: 0.0 for emotion in ALL_EMOTIONS}
+    
+    # Update the dictionary with the scores returned by the model
+    for item in predictions:
+        emotion_scores[item["label"]] = item["score"]
+    
+    return emotion_scores
 
 # Create a custom Gradio interface with title, description, and examples
 gr.Interface(
@@ -22,10 +32,10 @@ gr.Interface(
     title="CMACHINES | Emotion Detection with DistilBERT",
     description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter a piece of text to analyze its emotional content!",
     examples=[
+        "I like you. I love you. Sometime I hate you too.",
         "I am so happy to see you!",
         "I'm really angry about what happened.",
         "The sunset was absolutely beautiful today.",
-        "I'm worried about the upcoming exam.",
-        "Fear is the mind-killer. I will face my fear."
+        "I'm worried about the upcoming exam."
     ]
 ).launch()