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| import gradio as gr | |
| from transformers import pipeline | |
| # Load model locally (no API call) | |
| emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True) | |
| def detect_emotion(text): | |
| results = emotion_classifier(text)[0] | |
| return {res["label"]: res["score"] for res in results} | |
| iface = gr.Interface(fn=detect_emotion, | |
| inputs="text", | |
| outputs="label", | |
| title="Emotion Detection", | |
| description="Enter text to detect emotion using DistilRoBERTa") | |
| iface.launch() | |