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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| from examples import yellow, stairway, numb, puppets, firework | |
| def lyrics_categories(input_text): | |
| spotify_model = "spotify/autonlp-huggingface-demo-song-lyrics-18923587" | |
| model = AutoModelForSequenceClassification.from_pretrained(spotify_model) | |
| tokenizer = AutoTokenizer.from_pretrained(spotify_model) | |
| labels = model.config.id2label | |
| inputs = tokenizer(input_text, return_tensors="pt") | |
| outputs = model(**inputs) | |
| predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) | |
| predictions = predictions.detach().numpy()[0] | |
| index_sorted = np.argsort(predictions)[::-1] | |
| clean_outputs = {labels[idx]:str(predictions[idx]) for idx in index_sorted} | |
| print(clean_outputs) | |
| return clean_outputs | |
| description = "With lyrics, find the top 5 genres this song belongs to! (Powered by Spotify)" | |
| iface = gr.Interface(fn=lyrics_categories, | |
| inputs=gr.inputs.Textbox(lines=20, placeholder="Enter song lyrics here...", label="Song Lyrics"), | |
| outputs=gr.outputs.Label(num_top_classes=5, label="Genres/Categories"), | |
| examples=[stairway, numb, puppets, firework, yellow], | |
| article=description, | |
| title="Song Genre Predictor", | |
| ) | |
| iface.launch() | |