app.py

import os
import json
from pathlib import Path
from datetime import datetime

from datasets import load_dataset, DatasetDict
from huggingface_hub import login, create_repo, upload_folder
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    DataCollatorForSeq2Seq,
    TrainingArguments,
    Trainer,
)
from peft import LoraConfig, get_peft_model

# ------------- Config utilisateur -------------
HF_TOKEN = os.environ["HF_TOKEN"]  # ⚠️ récupère ton token depuis l'environnement
BASE_MODEL_ID = "Gopu-poss/gopu-agent-2k-fdf"
ALIGNED_MODEL_ID = "Gopu-poss/gopu-agent-2k-fdf-aligned"
DATASET_REPO_ID = "Gopu-poss/gopu-agent-2k-fdf-dataset-prepared"
OUTPUT_DIR = "./gopu-lora-out"
SEED = 42

# ------------- Auth -------------
print(">> Logging into Hugging Face Hub...")
login(token=HF_TOKEN)

# ------------- Chargement des datasets bruts -------------
print(">> Loading HuggingFaceFW/finewiki (train split)...")
finewiki = load_dataset("HuggingFaceFW/finewiki", split="train")
print(">> Loading fka/awesome-chatgpt-prompts (train split)...")
awesome = load_dataset("fka/awesome-chatgpt-prompts", split="train")

# ------------- Échantillonnage / préparation -------------
FW_SAMPLE_SIZE = 20000
if len(finewiki) > FW_SAMPLE_SIZE:
    finewiki = finewiki.shuffle(seed=SEED).select(range(FW_SAMPLE_SIZE))

print(f">> finewiki sampled: {len(finewiki)} rows; awesome: {len(awesome)} rows")

# ------------- Normalisation en instruction / input / output -------------
def map_finewiki(example):
    title = example.get("title", "")
    text = example.get("text", "")
    instruction = f"Explique en termes clairs et techniques l'article: {title}"
    input_ctx = text[:2000]
    output = (
        "Résumé technique et stylisé (GopuOS): "
        "Points clés, concepts, et relations. Maintiens un ton clair, concis, et agentique."
    )
    return {"instruction": instruction, "input": input_ctx, "output": output}

finewiki_mapped = finewiki.map(map_finewiki)

def map_awesome(example):
    act = example.get("act", "")
    prompt = example.get("prompt", "")
    instruction = f"Rôle/acte: {act}. Réponds au prompt en style GopuOS."
    input_ctx = prompt
    output = (
        "Réponse alignée GopuOS: concise, technique, introspectable, bilingue possible FR/EN."
    )
    return {"instruction": instruction, "input": input_ctx, "output": output}

awesome_mapped = awesome.map(map_awesome)

prepared = DatasetDict({
    "train": finewiki_mapped,
    "eval": awesome_mapped
})

# ------------- Sauvegarde locale du dataset prétraité -------------
prepared_dir = Path("./prepared_dataset")
prepared_dir.mkdir(parents=True, exist_ok=True)
for split in prepared.keys():
    out_path = prepared_dir / f"{split}.jsonl"
    with out_path.open("w", encoding="utf-8") as f:
        for ex in prepared[split]:
            f.write(json.dumps(ex, ensure_ascii=False) + "\n")

# ------------- Push du dataset prétraité sur le Hub -------------
print(f">> Creating/updating dataset repo: {DATASET_REPO_ID}")
create_repo(repo_id=DATASET_REPO_ID, token=HF_TOKEN, repo_type="dataset", private=False, exist_ok=True)

upload_folder(
    repo_id=DATASET_REPO_ID,
    repo_type="dataset",
    folder_path=str(prepared_dir),
    token=HF_TOKEN,
    commit_message=f"Prepared dataset push {datetime.utcnow().isoformat()}",
)

# ------------- Chargement modèle/tokenizer -------------
print(f">> Loading base model: {BASE_MODEL_ID}")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, use_auth_token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL_ID, use_auth_token=HF_TOKEN)

# ------------- PEFT LoRA config -------------
peft_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)
model = get_peft_model(model, peft_config)

# ------------- Tokenization -------------
def format_example(ex):
    system_prompt = (
        "Tu es Gopu, agent intelligent de GopuOS. Réponds de manière claire, technique, stylisée, et introspectable."
    )
    user = f"Utilisateur: {ex['instruction']}\nContexte: {ex['input']}\nGopu:"
    target = ex["output"]
    src = system_prompt + "\n\n" + user
    return {"src": src, "tgt": target}

formatted = prepared.map(format_example)

def tokenize(batch):
    model_inputs = tokenizer(
        batch["src"],
        truncation=True,
        max_length=1024,
    )
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(
            batch["tgt"],
            truncation=True,
            max_length=256,
        )
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_train = formatted["train"].map(tokenize, batched=False, remove_columns=formatted["train"].column_names)
tokenized_eval = formatted["eval"].map(tokenize, batched=False, remove_columns=formatted["eval"].column_names)

data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, padding="longest")

# ------------- Entraînement -------------
training_args = TrainingArguments(
    output_dir=OUTPUT_DIR,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=2,
    eval_strategy="steps",
    eval_steps=200,
    logging_steps=50,
    save_steps=500,
    save_total_limit=2,
    num_train_epochs=1,
    learning_rate=2e-4,
    warmup_steps=200,
    weight_decay=0.01,
    fp16=True,
    bf16=False,
    report_to=[],
    seed=SEED,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=tokenized_train,
    eval_dataset=tokenized_eval,
)

print(">> Starting training...")
trainer.train()
print(">> Training complete")

# ------------- Sauvegarde et push du modèle -------------
print(f">> Creating/updating model repo: {ALIGNED_MODEL_ID}")
create_repo(repo_id=ALIGNED_MODEL_ID, token=HF_TOKEN, repo_type="model", private=False, exist_ok=True)

trainer.save_model(OUTPUT_DIR)
tokenizer.save_pretrained(OUTPUT_DIR)

upload_folder(
    repo_id=ALIGNED_MODEL_ID,
    repo_type="model",
    folder_path=OUTPUT_DIR,
    token=HF_TOKEN,
    commit_message=f"Push aligned LoRA model {datetime.utcnow().isoformat()}",
)

print(f">> Model pushed: https://huggingface.co/{ALIGNED_MODEL_ID}")