upload_hf.py

"""
upload_hf.py
============
Carica su HuggingFace solo i file necessari del progetto GenerAI.

Uso:
    python upload_hf.py
    python upload_hf.py --repo amogaddy/GenerAI --model-weights
"""

import argparse
import os
import shutil
import tempfile
from pathlib import Path

# Carica .env se presente
if Path(".env").exists():
    for line in Path(".env").read_text(encoding="utf-8").splitlines():
        if "=" in line and not line.startswith("#"):
            k, v = line.split("=", 1)
            os.environ.setdefault(k.strip(), v.strip())

# File del codice da includere
SOURCE_FILES = [
    "app.py",
    "brain.py",
    "knowledge_base.py",
    "scraper.py",
    "errors.py",
    "seed_italian.py",
    "export_dataset.py",
    "finetune.py",
    "upload_hf.py",
    "requirements.txt",
    "requirements-train.txt",
]

DEFAULT_REPO = "amogaddy/GenerAI"
MODEL_DIR    = "./generai-finetuned"


def check_deps():
    try:
        from huggingface_hub import login, upload_folder, HfApi
        return login, upload_folder, HfApi
    except ImportError:
        print("❌ huggingface_hub non installato.")
        print("   pip install huggingface-hub")
        exit(1)


def build_model_card(repo_id: str, include_weights: bool) -> str:
    return f"""---
license: mit
base_model: Qwen/Qwen2.5-0.5B-Instruct
language:
  - it
tags:
  - italian
  - generai
  - fine-tuned
  - rag
---

# GenerAI 🤖

Assistente AI in italiano con memoria locale (ChromaDB) e ricerca web.

## Come usarlo

```bash
pip install -r requirements.txt
python app.py
```

## Fine-tuning

```bash
pip install -r requirements-train.txt
python export_dataset.py
python finetune.py --dataset dataset.jsonl --hf-repo {repo_id}
```

## Licenza

MIT — fai quello che vuoi, basta lasciare il credito.
"""


def main():
    parser = argparse.ArgumentParser(description="Carica GenerAI su HuggingFace")
    parser.add_argument("--repo",          default=DEFAULT_REPO, help=f"repo_id HuggingFace (default: {DEFAULT_REPO})")
    parser.add_argument("--model-weights", action="store_true",  help="Includi anche i pesi del modello fine-tunato")
    parser.add_argument("--no-login",      action="store_true",  help="Salta il login (usa token già salvato)")
    args = parser.parse_args()

    login, upload_folder, HfApi = check_deps()

    # ── Login ──────────────────────────────────────────────────────────────────
    if not args.no_login:
        token = os.environ.get("HF_TOKEN", "")
        if token:
            print("Login HuggingFace con token da .env...")
            login(token=token)
        else:
            print("Login HuggingFace...")
            login()

    # ── Crea cartella temporanea con solo i file necessari ─────────────────────
    with tempfile.TemporaryDirectory() as tmp:
        tmp_path = Path(tmp)
        copied = []
        missing = []

        for fname in SOURCE_FILES:
            src = Path(fname)
            if src.exists():
                shutil.copy2(src, tmp_path / src.name)
                copied.append(fname)
            else:
                missing.append(fname)

        # Genera README.md / model card
        (tmp_path / "README.md").write_text(
            build_model_card(args.repo, args.model_weights),
            encoding="utf-8",
        )
        copied.append("README.md (generato)")

        print(f"\nFile da caricare ({len(copied)}):")
        for f in copied:
            print(f"  + {f}")
        if missing:
            print(f"\nFile non trovati (saltati):")
            for f in missing:
                print(f"  - {f}")

        # ── Upload codice ──────────────────────────────────────────────────────
        print(f"\nUpload codice -> {args.repo} ...")
        upload_folder(
            folder_path=str(tmp_path),
            repo_id=args.repo,
            repo_type="model",
            commit_message="Upload GenerAI — codice + grammatica italiana",
        )
        print("Codice caricato.")

    # ── Upload pesi modello (opzionale) ────────────────────────────────────────
    if args.model_weights:
        if not Path(MODEL_DIR).exists():
            print(f"\nCartella pesi non trovata: {MODEL_DIR}")
            print("  Esegui prima: python finetune.py --dataset dataset.jsonl")
        else:
            print(f"\nUpload pesi modello -> {args.repo} ...")
            upload_folder(
                folder_path=MODEL_DIR,
                repo_id=args.repo,
                repo_type="model",
                commit_message="Upload pesi modello fine-tunato GenerAI",
            )
            print("Pesi modello caricati.")

    print(f"\nCompletato! -> https://huggingface.co/{args.repo}")


if __name__ == "__main__":
    main()