from pathlib import Path

md = """# LLM Handoff: What the User Is Asking to Achieve (ACE-Step 1.5 on Hugging Face)

## Primary Goal
Deploy **ACE-Step 1.5** as a usable music-generation service on Hugging Face, then call it from scripts/app code.

---

## User Intent (Condensed)

The user wants to:

1. **Host ACE-Step 1.5 on Hugging Face** (not just local test).
2. Use a **private token-protected endpoint**.
3. Run on **GPU (preferably A100)**.
4. Configure **aggressive cost controls** (scale-to-zero, pause when not in use).
5. Build a working **`handler.py`** that:
   - loads the real model,
   - accepts generation inputs (`prompt`, `lyrics`, etc.),
   - returns generated audio as base64 WAV.
6. Have a clear **request format** and **test scripts** to generate audio.
7. Produce docs so another LLM in Cursor can continue implementation without losing context.

---

## What the User Specifically Asked For in Chat

- Step-by-step setup for:
  - repo creation on HF,
  - pushing code,
  - connecting endpoint to repo,
  - testing endpoint calls.
- Clarification on:
  - token permissions,
  - whether ZeroGPU can be used,
  - where “Inference Endpoints → New” is in HF UI.
- Confirmation on billing behavior:
  - paused endpoint billing,
  - scale-to-zero behavior.
- Conversion of test call into runnable scripts:
  - `.bat` / `.ps1` for Windows.
- Upgrade from **sine-wave smoke test** to **actual ACE-Step inference** in `handler.py`.
- A consolidated markdown handoff for LLM continuation.

---

## Current Technical Direction (Expected by User)

### Deployment Architecture
- Use **custom endpoint repo** (not direct one-click deployment from model card).
- Repo should contain:
  - `handler.py`
  - `requirements.txt`
  - optional `README.md`

### Endpoint Behavior
- Private endpoint (HF token required).
- Input JSON under `inputs`.
- Output JSON includes `audio_base64_wav`.

### Minimum Input Contract
```json
{
  "inputs": {
    "prompt": "upbeat pop rap with emotional guitar",
    "lyrics": "[Verse] city lights and midnight rain",
    "duration_sec": 12,
    "sample_rate": 44100,
    "seed": 42,
    "guidance_scale": 7.0,
    "steps": 50,
    "use_lm": true
  }
}