app.py

import os
import gradio as gr
from huggingface_hub import login
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling, pipeline
from peft import PeftModel

# ============================================================
# 🔐 Autenticación HuggingFace
# ============================================================
hf_token = os.environ.get("HF_TOKEN")
if hf_token:
    login(token=hf_token)
else:
    print("⚠️ No se encontró el token. Agrega 'HF_TOKEN' en Settings → Secrets.")

# ============================================================
# ⚙️ Configuración del modelo y dataset
# ============================================================
BASE_MODEL = "bigcode/santacoder"  # Modelo público
LORA_PATH = "./lora_output"        # Carpeta donde se guardará LoRA
DATASET_PATH = "tu_dataset.json"   # Cambia aquí al nombre de tu dataset

# ============================================================
# 🔧 Inicializar tokenizer y modelo base
# ============================================================
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL)

# 🔧 Asegurar que haya un pad_token
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# ============================================================
# 🧩 Función de entrenamiento LoRA
# ============================================================
def train_lora(epochs, batch_size, learning_rate):
    try:
        dataset = load_dataset("json", data_files=DATASET_PATH)
        tokenized = dataset.map(
            lambda e: tokenizer(
                e["prompt"] + e["completion"],
                truncation=True,
                padding="max_length",
                max_length=256
            ),
            batched=True
        )

        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

        training_args = TrainingArguments(
            output_dir=LORA_PATH,
            per_device_train_batch_size=int(batch_size),
            num_train_epochs=int(epochs),
            learning_rate=learning_rate,
            save_total_limit=1,
            logging_steps=10,
            push_to_hub=False
        )

        trainer = Trainer(
            model=base_model,
            args=training_args,
            train_dataset=tokenized["train"],
            data_collator=data_collator,
        )

        trainer.train()
        # Guardar LoRA
        base_model.save_pretrained(LORA_PATH)
        tokenizer.save_pretrained(LORA_PATH)

        return "✅ Entrenamiento completado y guardado en ./lora_output"
    except Exception as e:
        return f"❌ Error durante el entrenamiento: {e}"

# ============================================================
# 🤖 Función para generar texto usando LoRA sobre el modelo base
# ============================================================
def generate_text(prompt_text):
    try:
        # Cargar modelo base
        tokenizer_gen = AutoTokenizer.from_pretrained(BASE_MODEL)
        base_model_gen = AutoModelForCausalLM.from_pretrained(BASE_MODEL)

        # Aplicar LoRA
        model = PeftModel.from_pretrained(base_model_gen, LORA_PATH)

        generator = pipeline("text-generation", model=model, tokenizer=tokenizer_gen)
        output = generator(prompt_text, max_new_tokens=100, temperature=0.7, top_p=0.9)
        return output[0]["generated_text"]
    except Exception as e:
        return f"❌ Error generando texto: {e}"

# ============================================================
# 💻 Interfaz Gradio
# ============================================================
with gr.Blocks(title="AmorCoderAI - Entrenamiento LoRA") as demo:
    gr.Markdown("# 💙 AmorCoderAI - Entrenamiento y Pruebas")
    gr.Markdown("Entrena y prueba tu modelo basado en `bigcode/santacoder` con LoRA")

    with gr.Tab("🧠 Entrenar"):
        epochs = gr.Number(value=1, label="Épocas")
        batch_size = gr.Number(value=2, label="Tamaño de lote")
        learning_rate = gr.Number(value=5e-5, label="Tasa de aprendizaje")
        train_button = gr.Button("🚀 Iniciar entrenamiento")
        train_output = gr.Textbox(label="Resultado")
        train_button.click(train_lora, inputs=[epochs, batch_size, learning_rate], outputs=train_output)

    with gr.Tab("✨ Probar modelo"):
        prompt = gr.Textbox(label="Escribe un prompt")
        generate_button = gr.Button("💬 Generar texto")
        output_box = gr.Textbox(label="Salida generada")
        generate_button.click(generate_text, inputs=prompt, outputs=output_box)

# ============================================================
# 🚀 Lanzar app
# ============================================================
if __name__ == "__main__":
    demo.launch()