example_usage.py

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

def load_model():
    """Load the model and tokenizer"""
    model = T5ForConditionalGeneration.from_pretrained("synyyy/t5-spotify-features-v2")
    tokenizer = T5Tokenizer.from_pretrained("synyyy/t5-spotify-features-v2")
    return model, tokenizer

def generate_spotify_features(prompt, model, tokenizer):
    """Generate Spotify features from text prompt"""
    input_text = f"prompt: {prompt}"
    
    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
    )
    
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Post-process JSON if needed
    if not result.strip().startswith(') and not result.strip().endswith('):
        result = " + result + "
    
    try:
        return json.loads(result)
    except json.JSONDecodeError as e:
        print(f"JSON parsing failed: {e}")
        print(f"Raw output: {result}")
        return None

if __name__ == "__main__":
    # Load model
    print("Loading model...")
    model, tokenizer = load_model()
    
    # Test prompts
    test_prompts = [
        "energetic dance music for parties",
        "calm acoustic music for studying",
        "upbeat pop songs for working out",
        "relaxing instrumental background music",
        "happy music for road trips"
    ]
    
    print("\nGenerating features for test prompts:")
    print("=" * 50)
    
    for prompt in test_prompts:
        print(f"\nPrompt: {prompt}")
        features = generate_spotify_features(prompt, model, tokenizer)
        if features:
            print(f"Features: {json.dumps(features, indent=2)}")
        else:
            print("Failed to generate valid features")
        print("-" * 30)