Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import gradio as gr
+
+# Load pre-trained tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('huggingartists/ed-sheeran')
+model = AutoModelForCausalLM.from_pretrained('huggingartists/ed-sheeran', pad_token_id=50269)
+
+# Function to generate predictions
+def ed_lyrics(prompt):
+    encoded_prompt = tokenizer.encode(prompt + "\n\nLyrics: ", add_special_tokens=False, return_tensors='pt').to('cpu')
+    output_sequences = model.generate(encoded_prompt, max_length=75+len(encoded_prompt), top_p=0.8, do_sample=True)[0].tolist()
+    generated_song = tokenizer.decode(output_sequences[:], clean_up_tokenization_spaces=True)
+    final_result = generated_song.split("\n\n")[-1:]
+    return final_result
+
+# Launch interactive web demo
+iface = gr.Interface(fn=ed_lyrics, inputs=["textbox", outputs="text").launch()