example_usage.py

"""
Example script for using the T5 Spotify Features model
"""
from transformers import T5ForConditionalGeneration, T5Tokenizer
import json

def predict_spotify_features(prompt_text, model_name="afsagag/t5-spotify-features"):
    """
    Generate Spotify audio features from a text prompt
    
    Args:
        prompt_text (str): Natural language description of music preferences
        model_name (str): Hugging Face model name
    
    Returns:
        dict: Spotify audio features or None if JSON parsing fails
    """
    # Load model and tokenizer
    model = T5ForConditionalGeneration.from_pretrained(model_name)
    tokenizer = T5Tokenizer.from_pretrained(model_name)
    
    # Format input
    input_text = f"prompt: {prompt_text}"
    
    # Tokenize and generate
    input_ids = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True).input_ids
    outputs = model.generate(
        input_ids, 
        max_length=256, 
        num_beams=4, 
        early_stopping=True,
        do_sample=False
    )
    
    # Decode and clean result
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    cleaned_result = result.replace("ll", "null").replace("nu", "null")
    
    try:
        return json.loads(cleaned_result)
    except json.JSONDecodeError:
        print(f"Failed to parse JSON: {cleaned_result}")
        return None

if __name__ == "__main__":
    # Example prompts
    test_prompts = [
        "I want energetic dance music",
        "Play some calm acoustic songs",
        "Upbeat pop music for working out",
        "Sad slow songs for rainy days"
    ]
    
    for prompt in test_prompts:
        print(f"\nPrompt: {prompt}")
        features = predict_spotify_features(prompt)
        if features:
            print(f"Features: {json.dumps(features, indent=2)}")