tried adapting demo code from docs

app.py

@@ -13,23 +13,16 @@ def preprocess(text):
     text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
     return text
 
-# Define a function to make a prediction on the input text
-def predict_sentiment(text):
-    # Preprocess the text
-    text = preprocess(text)
-    # Make a prediction using the loaded model
-    proba = model.predict(text)[0]
-    # Normalize the probabilities
-    proba /= proba.sum()
-    # Return the probability distribution
-    return {"Positive": float(proba[0]), "Negative": float(proba[1]), "Neutral": float(proba[2])}
+def sentiment_analysis(text):
+    scores = model.predict(text)
+    del scores["compound"]
+    return scores
 
-# Create a Gradio interface
 iface = gr.Interface(
-    fn=predict_sentiment, 
-    inputs=gr.inputs.Textbox(label="Enter text here"), 
-    outputs=gr.outputs.Label(label="Sentiment", default="Neutral")
-)
+    fn=sentiment_analysis, 
+    inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."), 
+    outputs="label", 
+    interpretation="default",
+    examples=[["This is wonderful!"]])
 
-# Launch the interface
 iface.launch()