Managing AI model memory involves strategies to efficiently handle both short-term and long-term data storage and retrieval for AI models, particularly large language models (LLMs). This includes techniques like breaking down tasks into smaller prompts, using vector databases for long-term memory, and implementing mechanisms to prevent memory bloat. 
Here's a breakdown of key strategies:
1. Understanding Memory Needs:
Short-term memory:
.
LLMs have a limited context window (number of tokens) for recent interactions. Strategies are needed to manage this efficiently. 
Long-term memory:
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Storing and retrieving past interactions, user preferences, and other relevant information is crucial for personalization and continuity. 
2. Short-Term Memory Management:
Prompt Engineering:
Design prompts to be concise and avoid redundancy. 
Chunking:
Break down large tasks into smaller, manageable prompts that fit within the context window. 
Summarization:
Use summarization techniques to condense past conversations and reduce the amount of information stored in the short-term memory. 
Memory Trimming:
Remove less relevant or older messages from the conversation history to stay within the context window. 
3. Long-Term Memory Management:
Vector Databases:
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Store embeddings (numerical representations of text) of past interactions in vector databases. This allows for similarity searches to retrieve relevant information for new queries. 
Memory Storage:
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Implement mechanisms to store and retrieve key information from past interactions, avoid duplication, and update/append new information. 
Data Governance:
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Establish policies for data collection, storage, usage, and access to ensure data quality and privacy. 
Memory Decay:
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Implement mechanisms to remove outdated or less frequently accessed information to prevent memory bloat. 
4. Tools and Frameworks:
LangChain:
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A framework that simplifies building memory-enabled AI agents by integrating memory, APIs, and reasoning workflows. 
LangGraph:
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Provides tools like Checkpointers for maintaining thread-specific context in short-term memory. 
Redis:
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A fast, in-memory data structure store that can be used to manage memory for AI agents. 
5. Other Considerations:
Model Size:
If memory limitations are a significant issue, consider using a larger and more capable AI model. 
Training Data:
Retraining models on better data can improve their ability to handle information and context. 
Regular Testing:
Continuously monitor and test the performance of memory management strategies to ensure optimal efficiency. 
Privacy:
Be mindful of data privacy regulations and implement appropriate measures to protect user information. 
By implementing these strategies, you can effectively manage AI model memory, enabling more efficient, personalized, and context-aware interactions. 

'the task'
please fix memory management in my script for the ai models it's loading, I want it to keep the model in memory until the application is exited but the current script keeps making my PC bluescreen. Please adjust the script and return the entire script fixed ty.

'the script'
import os
import tempfile
import torch
import traceback
import time
import gradio as gr
from huggingface_hub import snapshot_download
from models.vc.vevo.vevo_utils import VevoInferencePipeline, save_audio

# ====== Setup device ======
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# ====== Download & prepare model checkpoints ======

def careful_join(*parts):
    # Safe path join AND normalize slashes for WSL / Windows compat
    return os.path.normpath(os.path.join(*parts))

print("[INFO] Downloading content/style tokenizer...")
tokenizer_dir = snapshot_download(
    repo_id="amphion/Vevo",
    repo_type="model",
    cache_dir="./ckpts/Vevo",
    allow_patterns=["tokenizer/vq8192/*"],
)
content_style_tokenizer_ckpt_path = careful_join(tokenizer_dir, "tokenizer/vq8192")

print("[INFO] Downloading autoregressive transformer...")
ar_dir = snapshot_download(
    repo_id="amphion/Vevo",
    repo_type="model",
    cache_dir="./ckpts/Vevo",
    allow_patterns=["contentstyle_modeling/PhoneToVq8192/*"],
)
ar_cfg_path = "./models/vc/vevo/config/PhoneToVq8192.json"
ar_ckpt_path = careful_join(ar_dir, "contentstyle_modeling/PhoneToVq8192")

print("[INFO] Downloading flow matching transformer...")
fmt_dir = snapshot_download(
    repo_id="amphion/Vevo",
    repo_type="model",
    cache_dir="./ckpts/Vevo",
    allow_patterns=["acoustic_modeling/Vq8192ToMels/*"],
)
fmt_cfg_path = "./models/vc/vevo/config/Vq8192ToMels.json"
fmt_ckpt_path = careful_join(fmt_dir, "acoustic_modeling/Vq8192ToMels")

print("[INFO] Downloading vocoder...")
vocoder_dir = snapshot_download(
    repo_id="amphion/Vevo",
    repo_type="model",
    cache_dir="./ckpts/Vevo",
    allow_patterns=["acoustic_modeling/Vocoder/*"],
)
vocoder_cfg_path = "./models/vc/vevo/config/Vocoder.json"
vocoder_ckpt_path = careful_join(vocoder_dir, "acoustic_modeling/Vocoder")

print("[INFO] Initializing VevoInferencePipeline (this may take a while)...")
inference_pipeline = VevoInferencePipeline(
    content_style_tokenizer_ckpt_path=content_style_tokenizer_ckpt_path,
    ar_cfg_path=ar_cfg_path,
    ar_ckpt_path=ar_ckpt_path,
    fmt_cfg_path=fmt_cfg_path,
    fmt_ckpt_path=fmt_ckpt_path,
    vocoder_cfg_path=vocoder_cfg_path,
    vocoder_ckpt_path=vocoder_ckpt_path,
    device=device,
)
print("[INFO] Model ready!")

def vevo_tts(
    src_text: str,
    ref_wav_path: str,
    ref_text: str = None,
    src_language: str = "en",
    ref_language: str = "en",
):
    """
    Run the Vevo TTS model inference.
    Returns the path to the generated wav audio and a status message.
    """

    # Input validation
    if not src_text or src_text.strip() == "":
        return None, "Error: Source text is empty. Please enter text to synthesize."

    if not ref_wav_path or ref_wav_path.strip() == "":
        return None, "Error: Reference wav file is missing. Please upload a style reference wav audio file."

    if not src_language or src_language.strip() == "":
        return None, "Error: Source language code is empty. Please select source language."

    ref_wav_path = os.path.normpath(ref_wav_path)
    timbre_ref_wav_path = ref_wav_path

    if torch.cuda.is_available():
        torch.cuda.empty_cache()

    fd_out, output_path = tempfile.mkstemp(suffix=".wav", prefix="vevo_tts_out_")
    os.close(fd_out)

    try:
        inf_args = dict(
            src_wav_path=None,
            src_text=src_text,
            style_ref_wav_path=ref_wav_path,
            timbre_ref_wav_path=timbre_ref_wav_path,
            src_text_language=src_language,
        )
        if ref_text and ref_text.strip() != "":
            inf_args["style_ref_wav_text"] = ref_text
            inf_args["style_ref_wav_text_language"] = ref_language if ref_language.strip() else "en"

        gen_audio = inference_pipeline.inference_ar_and_fm(**inf_args)

        print(f"[DEBUG] Generated audio shape: {gen_audio.shape}, dtype={gen_audio.dtype}")

        # Pass tensor directly (detached, on cpu) to save_audio
        gen_audio_tensor = gen_audio.cpu().detach().clone()
        save_audio(gen_audio_tensor, output_path=output_path)

        time.sleep(0.1)

        return output_path, "✅ Inference complete!"

    except Exception as e:
        if os.path.exists(output_path):
            try:
                os.remove(output_path)
            except:
                pass
        tb = traceback.format_exc()
        err_msg = f"❌ Exception during inference:\n{str(e)}\n\nTraceback:\n{tb}"
        return None, err_msg

# ====== Gradio UI ======

language_options = [
    ("English", "en"),
    ("Chinese", "zh"),
    ("Japanese", "ja"),
    ("Korean", "ko"),
    # Add more languages if supported
]

with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Vevo Zero-Shot TTS Demo
        Upload a **style reference wav audio** and enter the **text** you want to synthesize.
        """
    )

    with gr.Row():
        with gr.Column():
            ref_wav_input = gr.Audio(
                label="Style Reference Audio (wav)", type="filepath", interactive=True
            )
            src_text_input = gr.Textbox(
                value="I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.",
                lines=6,
                label="Source Text (to synthesize)",
                interactive=True,
            )
            ref_text_input = gr.Textbox(
                value="Flip stood undecided, his ears strained to catch the slightest sound.",
                lines=2,
                label="Reference Text (optional)",
                interactive=True,
            )
            src_language_input = gr.Dropdown(
                choices=language_options,
                value="en",
                label="Source Text Language",
                interactive=True,
            )
            ref_language_input = gr.Dropdown(
                choices=language_options,
                value="en",
                label="Reference Text Language",
                interactive=True,
            )
            run_button = gr.Button("Run Inference")

        with gr.Column():
            output_audio = gr.Audio(label="Generated Speech", type="filepath")
            status = gr.Textbox(label="Status / Logs", interactive=False)

    def run_inference_wrapper(ref_wav_path, src_text, ref_text, src_lang, ref_lang):
        return vevo_tts(
            src_text=src_text,
            ref_wav_path=ref_wav_path,
            ref_text=ref_text,
            src_language=src_lang,
            ref_language=ref_lang,
        )

    run_button.click(
        run_inference_wrapper,
        inputs=[ref_wav_input, src_text_input, ref_text_input, src_language_input, ref_language_input],
        outputs=[output_audio, status],
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)