| import gradio as gr | |
| from transformers import pipeline | |
| model_id = "Subhajit01/distilbert-base-uncased-finetuned-emotion" | |
| classifier = pipeline("text-classification", model=model_id) | |
| labels = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'] | |
| def predict(text): | |
| max_prob_id = 0 | |
| max_prob = 0 | |
| preds = classifier(text, return_all_scores=True) | |
| for i in range(len(preds[0])): | |
| if (preds[0][i]["score"] > max_prob): | |
| max_prob = preds[0][i]["score"] | |
| max_prob_id = i | |
| return labels[max_prob_id] | |
| iface = gr.Interface(fn=predict, | |
| inputs="text", | |
| outputs="text", | |
| title="Sentiment Analyzer", | |
| description="Enter text to analyze its sentiment.") | |
| iface.launch(share= True) |