| 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() |
|
|
