Initial version

app.py

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+import torch
+import numpy as np
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+repo_name = 'spotify/autonlp-huggingface-demo-song-lyrics-18923587'
+
+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 = torch.nn.functional.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]])
+
+input = gr.inputs.Textbox(lines=20)
+iface = gr.Interface(fn=predict, inputs=input, outputs="text")
+iface.launch()

requirements.txt

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+torch
+transformers