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app.py

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+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)
+
+def detect_emotions(emotion_input):
+  prediction = classifier(emotion_input,)
+  output = {}
+  for emotion in prediction[0]:
+    output[emotion["label"]] = emotion["score"]
+  return output
+
+examples = [["I am excited to announce that I have been promoted"], ["Sorry for the late reply"]]
+
+demo = gr.Interface(fn=detect_emotions, inputs=gr.Textbox(placeholder="Enter text here", label="Input"), outputs=gr.Label(label="Emotion"), examples=examples)
+demo.launch()

requirements.txt

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+gradio
+torch
+timm
+sentencepiece
+transformers