app.py

import gradio as gr
from transformers import pipeline

text_emotion = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)

def analyze_emotion(text):
    results = text_emotion(text)[0]
    results = sorted(results, key=lambda x: x['score'], reverse=True)
    output = {r['label']: round(r['score'], 3) for r in results}
    return output

demo = gr.Interface(
    fn=analyze_emotion,
    inputs=gr.Textbox(lines=3, placeholder="Type something here..."),
    outputs=gr.Label(num_top_classes=3),
    title="Empath AI - Emotion Detection",
    description="Type a sentence to see what emotions it contains!"
)

if __name__ == "__main__":
    demo.launch()