uses huggingface demo and autonlp model

app.py

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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)
+   
+
+

requirements.txt

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+transformers==4.12.3
+torch
+numpy