docs/Summary2.md

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

{
  "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
  }
}