app.py

import gradio as gr
import os
import torch
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    pipeline,
    AutoProcessor, 
    MusicgenForConditionalGeneration
)
from scipy.io.wavfile import write
import tempfile
from dotenv import load_dotenv
import spaces

load_dotenv()
hf_token = os.getenv("HF_TOKEN")

@spaces.GPU(duration=120)
def load_llama_pipeline_zero_gpu(model_id: str, token: str):
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token)
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            use_auth_token=token,
            torch_dtype=torch.float16,
            device_map="auto",
            trust_remote_code=True
        )
        return pipeline("text-generation", model=model, tokenizer=tokenizer)
    except Exception as e:
        return str(e)

@spaces.GPU(duration=120)
def generate_audio(prompt: str, audio_length: int, mg_model, mg_processor):
    try:
        mg_model.to("cuda")
        inputs = mg_processor(text=[prompt], padding=True, return_tensors="pt")
        outputs = mg_model.generate(**inputs, max_new_tokens=audio_length)
        mg_model.to("cpu")

        sr = mg_model.config.audio_encoder.sampling_rate
        audio_data = outputs[0, 0].cpu().numpy()
        normalized_audio = (audio_data / max(abs(audio_data)) * 32767).astype("int16")

        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav:
            write(temp_wav.name, sr, normalized_audio)
            return temp_wav.name
    except Exception as e:
        return f"Error generating audio: {e}"

with gr.Blocks() as demo:
    gr.Markdown("# 🎧 AI Radio Imaging with Llama 3 + MusicGen (Zero GPU)")
    user_prompt = gr.Textbox(label="Enter your promo idea", placeholder="E.g., A 15-second hype jingle for a morning talk show.")
    llama_model_id = gr.Textbox(label="Llama 3 Model ID", value="meta-llama/Meta-Llama-3-70B")
    hf_token = gr.Textbox(label="Hugging Face Token", type="password")
    audio_length = gr.Slider(label="Audio Length (tokens)", minimum=128, maximum=1024, step=64, value=512)

    generate_button = gr.Button("Generate Promo Script and Audio")
    script_output = gr.Textbox(label="Generated Script")
    audio_output = gr.Audio(label="Generated Audio", type="filepath")

    generate_button.click(
        fn=lambda prompt, model_id, token, length: (prompt, None),  # Simplify for demo
        inputs=[user_prompt, llama_model_id, hf_token, audio_length],
        outputs=[script_output, audio_output]
    )

demo.launch(debug=True)