| import gradio as gr | |
| from transformers import pipeline | |
| classifier = pipeline( | |
| "text-classification", | |
| model="bhadresh-savani/distilbert-base-uncased-emotion", | |
| return_all_scores=True, | |
| ) | |
| EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"] | |
| def predict_emotion(text): | |
| results = classifier(text)[0] | |
| return {result["label"]: result["score"] for result in results if result["label"] in EMOTIONS} | |
| iface = gr.Interface( | |
| fn=predict_emotion, | |
| inputs=gr.Textbox(lines=3, placeholder="Enter text here..."), | |
| outputs=gr.Label(num_top_classes=6), | |
| title="Creative Machines: Sentiment Analysis", | |
| description="Enter some text and see the predicted emotions.", | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |