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| import streamlit as st | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| repo_name = "adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717" | |
| tokenizer = AutoTokenizer.from_pretrained(repo_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(repo_name) | |
| labels = model.config.id2label | |
| def predict(lyrics): | |
| inputs = tokenizer(lyrics, padding=True, truncation=True, return_tensors="pt") | |
| outputs = model(**inputs) | |
| predictions = F.softmax(outputs.logits, dim=-1) | |
| predictions = predictions.detach().numpy()[0] | |
| predictions = predictions*100 | |
| sorted_indexes = np.argsort(predictions) | |
| return "These lyrics are {:.2f}% {}, {:.2f}% {} and {:.2f}% {}.".format( | |
| predictions[sorted_indexes[-1]], labels[sorted_indexes[-1]], | |
| predictions[sorted_indexes[-2]], labels[sorted_indexes[-2]], | |
| predictions[sorted_indexes[-3]], labels[sorted_indexes[-3]]) | |
| col1, col2 = st.columns(2) | |
| lyrics = col1.text_area("Lyrics") | |
| clicked = col1.button("Submit") | |
| output = "" | |
| if clicked: | |
| output = predict(lyrics) | |
| col2.write(output) | |