app.py

import os
from dotenv import load_dotenv
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments
from datasets import load_dataset, concatenate_datasets
from huggingface_hub import login
import time
import uvicorn
from fastapi import FastAPI
import threading
import spaces

load_dotenv()
login(token=os.getenv('HUGGINGFACE_TOKEN'))

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Modelo entrenado y en ejecución."}

@spaces.GPU()
def load_and_train():
    model_name = 'gpt2'
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    model = GPT2LMHeadModel.from_pretrained(model_name)

    dataset_humanizado = load_dataset('daily_dialog', split='train', trust_remote_code=True)
    dataset_codigo = load_dataset('code_search_net', split='train', trust_remote_code=True)
    dataset_prompts = load_dataset('openai_humaneval', split='train', trust_remote_code=True)

    combined_dataset = concatenate_datasets([
        dataset_humanizado,
        dataset_codigo,
        dataset_prompts
    ])

    def tokenize_function(examples):
        return tokenizer(examples['text'], truncation=True, padding='max_length', max_length=512)

    tokenized_dataset = combined_dataset.map(tokenize_function, batched=True)

    training_args = TrainingArguments(
        output_dir='./results',
        per_device_train_batch_size=100,
        per_device_eval_batch_size=100,
        num_train_epochs=0,
        learning_rate=1e-5,
        logging_steps=-1,
        max_grad_norm=1,
        save_total_limit=1,
        seed=42,
        weight_decay=0,
        warmup_ratio=0.0,
        evaluation_strategy="no",
        optim="adamw_torch",
        lr_scheduler_type="constant",
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset,
    )

    while True:
        try:
            trainer.train()
            model.push_to_hub('Yhhxhfh/nombre_de_tu_modelo', repo_type='model', use_temp_dir=True, commit_message="Actualización del modelo")
            tokenizer.push_to_hub('Yhhxhfh/nombre_de_tu_modelo', repo_type='model', use_temp_dir=True, commit_message="Actualización del tokenizador")
            time.sleep(5)
        except Exception as e:
            print(f"Error durante el entrenamiento: {e}. Reiniciando el proceso de entrenamiento...")
            time.sleep(10)

if __name__ == "__main__":
    threading.Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=7860)).start()
    load_and_train()