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app.py

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+import gradio as gr
+from transformers import pipeline
+
+
+classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+ 
+def fn_emotion(text):
+    results = classifier(text, padding='max_length', max_length=512)
+    return {label['label']: [label['score']] for label in results[0]}
+
+
+
+with gr.Blocks(title="Emotion",css="footer {visibility: hidden}") as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Sentence Emotion")
+            with gr.Row():           
+                with gr.Column():
+                    inputs = gr.TextArea(label="sentence",value=" I am so excited to go on vacation!",interactive=True)
+                    btn = gr.Button(value="RUN")
+                with gr.Column():
+                    output = gr.Label(label="output")
+                btn.click(fn=fn_emotion,inputs=[inputs],outputs=[output])
+demo.launch()

requirements.txt

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+gradio==3.32.0
+transformers==4.28.1