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 environment variables (e.g., Hugging Face token)
load_dotenv()
hf_token = os.getenv("HF_TOKEN")

# Globals for Lazy Loading
llama_pipeline = None
musicgen_model = None
musicgen_processor = None

# ---------------------------------------------------------------------
# Load Llama 3 Model with Zero GPU (Lazy Loading)
# ---------------------------------------------------------------------
@spaces.GPU(duration=120)
def load_llama_pipeline_zero_gpu(model_id: str, token: str):
    global llama_pipeline
    if llama_pipeline is None:
        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",  # Automatically handles GPU allocation
                trust_remote_code=True
            )
            llama_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
        except Exception as e:
            return f"Error loading Llama pipeline: {e}"
    return llama_pipeline

# ---------------------------------------------------------------------
# Load MusicGen Model (Lazy Loading)
# ---------------------------------------------------------------------
@spaces.GPU(duration=120)
def load_musicgen_model():
    global musicgen_model, musicgen_processor
    if musicgen_model is None or musicgen_processor is None:
        try:
            musicgen_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
            musicgen_processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
        except Exception as e:
            return None, f"Error loading MusicGen model: {e}"
    return musicgen_model, musicgen_processor

# ---------------------------------------------------------------------
# Generate Radio Script
# ---------------------------------------------------------------------
def generate_script(user_input: str, llama_pipeline):
    try:
        system_prompt = (
            "You are a top-tier radio imaging producer using Llama 3. "
            "Take the user's concept and craft a short, creative promo script."
        )
        combined_prompt = f"{system_prompt}\nUser concept: {user_input}\nRefined script:"
        result = llama_pipeline(combined_prompt, max_new_tokens=200, do_sample=True, temperature=0.9)
        return result[0]['generated_text'].split("Refined script:")[-1].strip()
    except Exception as e:
        return f"Error generating script: {e}"

# ---------------------------------------------------------------------
# Generate Audio
# ---------------------------------------------------------------------
@spaces.GPU(duration=120)
def generate_audio(prompt: str, audio_length: int):
    mg_model, mg_processor = load_musicgen_model()
    if mg_model is None or isinstance(mg_processor, str):
        return mg_processor

    try:
        mg_model.to("cuda")  # Move the model to GPU
        inputs = mg_processor(text=[prompt], padding=True, return_tensors="pt")
        outputs = mg_model.generate(**inputs, max_new_tokens=audio_length)
        mg_model.to("cpu")  # Return the 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}"

# ---------------------------------------------------------------------
# Gradio Interface
# ---------------------------------------------------------------------
def radio_imaging_script(user_prompt, llama_model_id):
    llama_pipeline = load_llama_pipeline_zero_gpu(llama_model_id, hf_token)
    if isinstance(llama_pipeline, str):
        return llama_pipeline

    # Generate Script
    script = generate_script(user_prompt, llama_pipeline)
    return script

def radio_imaging_audio(script, audio_length):
    return generate_audio(script, audio_length)

# ---------------------------------------------------------------------
# Interface
# ---------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# 🎧 AI Radio Imaging with Llama 3 + MusicGen (Zero GPU)")

    # Script Generation Section
    with gr.Row():
        with gr.Column():
            gr.Markdown("## Step 1: Generate the Promo Script")
            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")
            generate_script_button = gr.Button("Generate Promo Script")
            script_output = gr.Textbox(label="Generated Script", interactive=False)

            generate_script_button.click(
                fn=radio_imaging_script,
                inputs=[user_prompt, llama_model_id],
                outputs=script_output
            )

    # Audio Generation Section
    with gr.Row():
        with gr.Column():
            gr.Markdown("## Step 2: Generate the Sound")
            audio_length = gr.Slider(label="Audio Length (tokens)", minimum=128, maximum=1024, step=64, value=512)
            generate_audio_button = gr.Button("Generate Sound from Script")
            audio_output = gr.Audio(label="Generated Audio", type="filepath")

            generate_audio_button.click(
                fn=radio_imaging_audio,
                inputs=[script_output, audio_length],
                outputs=audio_output
            )

demo.launch(debug=True)