Add example usage script

example_usage.py

@@ -0,0 +1,58 @@
+"""
+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)}")