app.py

import gradio as gr
import torch
import logging
from transformers import AutoTokenizer, AutoModel
from diffusers import DiffusionPipeline
import soundfile as sf
import numpy as np

# Set up logging to debug startup issues
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

try:
    # Load text tokenizer and embedding model (umt5-base)
    def load_text_processor():
        logger.info("Loading text processor (umt5-base)...")
        tokenizer = AutoTokenizer.from_pretrained("./umt5-base")
        text_model = AutoModel.from_pretrained(
            "./umt5-base",
            use_safetensors=True,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        logger.info("Text processor loaded successfully.")
        return tokenizer, text_model

    # Load the transformer backbone (phantomstep_transformer)
    def load_transformer():
        logger.info("Loading transformer (phantomstep_transformer)...")
        transformer = DiffusionPipeline.from_pretrained(
            "./phantomstep_transformer",
            use_safetensors=True,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        logger.info("Transformer loaded successfully.")
        return transformer

    # Load the DCAE for audio encoding/decoding (phantomstep_dcae)
    def load_dcae():
        logger.info("Loading DCAE (phantomstep_dcae)...")
        dcae = DiffusionPipeline.from_pretrained(
            "./phantomstep_dcae",
            use_safetensors=True,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        logger.info("DCAE loaded successfully.")
        return dcae

    # Load the vocoder for audio synthesis (phantomstep_vocoder)
    def load_vocoder():
        logger.info("Loading vocoder (phantomstep_vocoder)...")
        vocoder = DiffusionPipeline.from_pretrained(
            "./phantomstep_vocoder",
            use_safetensors=True,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        logger.info("Vocoder loaded successfully.")
        return vocoder

    # Generate music from a text prompt
    def generate_music(prompt, duration=20, seed=42):
        logger.info(f"Generating music with prompt: {prompt}, duration: {duration}, seed: {seed}")
        torch.manual_seed(seed)
        
        # Load all components
        tokenizer, text_model = load_text_processor()
        transformer = load_transformer()
        dcae = load_dcae()
        vocoder = load_vocoder()
        
        # Step 1: Process text prompt to embeddings
        logger.info("Processing text prompt to embeddings...")
        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
        inputs = {k: v.to(text_model.device) for k, v in inputs.items()}
        with torch.no_grad():
            embeddings = text_model(**inputs).last_hidden_state.mean(dim=1)
        
        # Step 2: Pass embeddings through transformer
        logger.info("Generating with transformer...")
        transformer_output = transformer(
            embeddings,
            num_inference_steps=50,
            audio_length_in_s=duration
        ).audios[0]
        
        # Step 3: Decode audio features with DCAE
        logger.info("Decoding with DCAE...")
        dcae_output = dcae(
            transformer_output,
            num_inference_steps=50,
            audio_length_in_s=duration
        ).audios[0]
        
        # Step 4: Synthesize final audio with vocoder
        logger.info("Synthesizing with vocoder...")
        audio = vocoder(
            dcae_output,
            num_inference_steps=50,
            audio_length_in_s=duration
        ).audios[0]
        
        # Save audio to a file
        output_path = "output.wav"
        sf.write(output_path, audio, 22050)  # 22kHz sample rate
        logger.info("Music generation complete.")
        return output_path

    # Gradio interface
    logger.info("Setting up Gradio interface...")
    with gr.Blocks(title="PhantomStep: Text-to-Music Generation 🎵") as demo:
        gr.Markdown("# PhantomStep by GhostAI 🚀")
        gr.Markdown("Enter a text prompt to generate music! 🎶")
        
        prompt_input = gr.Textbox(label="Text Prompt", placeholder="A jazzy piano melody with a fast tempo")
        duration_input = gr.Slider(label="Duration (seconds)", minimum=10, maximum=60, value=20, step=1)
        seed_input = gr.Number(label="Random Seed", value=42, precision=0)
        generate_button = gr.Button("Generate Music")
        
        audio_output = gr.Audio(label="Generated Music")
        
        generate_button.click(
            fn=generate_music,
            inputs=[prompt_input, duration_input, seed_input],
            outputs=audio_output
        )

    logger.info("Launching Gradio app...")
    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)

except Exception as e:
    logger.error(f"Failed to start the application: {str(e)}")
    raise