| 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() |