starting over

app.py

@@ -1,49 +1,28 @@
+```python
 import gradio as gr
 from transformers import pipeline
 
-# Load the Hugging Face pipeline
-classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
+classifier = pipeline(
+    "text-classification",
+    model="bhadresh-savani/distilbert-base-uncased-emotion",
+    return_all_scores=True,
+)
 
-# Define the full list of possible emotions (based on the model output structure)
-ALL_EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"]
+EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"]
 
-def classify_emotion(text):
-    # Make predictions using the Hugging Face pipeline
-    predictions = classifier(text)
-    
-    # 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
-    print(predictions)
-    for item in predictions:
-        iscore = float(item["score"])
-        ilabel = item["label"]
-        print(f"iscore is {iscore}")
-        if iscore < 0.1:
-            iscore = iscore*10.0
+def predict_emotion(text):
+    results = classifier(text)[0]
+    return {result["label"]: result["score"] for result in results if result["label"] in EMOTIONS}
 
-        print(f"iscore for {ilabel} is now {iscore}")
-        emotion_scores[ilabel] = iscore
-    
-    return emotion_scores
 
-# Create a custom Gradio interface with title, description, and examples
-gr.Interface(
-    fn=classify_emotion,
-    inputs=gr.Textbox(
-        placeholder="Enter text to analyze...",
-        label="Input Text",
-        lines=4
-    ),
-    outputs=gr.Label(label="Emotion Probabilities"),  # Use gr.Label for a cleaner interface
-    title="CMACHINES25 | 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 am not 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."
-    ]
-).launch()
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text here..."),
+    outputs=gr.Label(num_top_classes=6),
+    title="Emotion Detector",
+    description="Enter some text and see the predicted emotions.",
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```