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,
)

# ============================================================
# 🔐 Autenticación segura con tu token
# ============================================================
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 → Add new secret")

# ============================================================
# ⚙️ Configuración del modelo base y dataset
# ============================================================
MODEL_NAME = "bigcode/santacoder"   # Modelo libre y compatible con Hugging Face
DATASET_PATH = "dataset.json"        # Archivo dataset que subiste al Space
OUTPUT_DIR = "lora_output"           # Carpeta donde se guarda el modelo entrenado

# Crear carpeta de salida si no existe
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Cargar modelo y tokenizer
print("🔄 Cargando modelo base...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=hf_token)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, use_auth_token=hf_token)

# ============================================================
# 🧩 Función de entrenamiento LoRA (simple y funcional)
# ============================================================
def train_lora(epochs, batch_size, learning_rate):
    try:
        # Cargar dataset JSON
        dataset = load_dataset("json", data_files=DATASET_PATH)

        # Tokenización del dataset
        def tokenize_fn(example):
            text = example["prompt"] + example["completion"]
            return tokenizer(
                text,
                truncation=True,
                padding="max_length",
                max_length=256,
            )

        tokenized = dataset.map(tokenize_fn, batched=True)

        # Preparar data collator
        data_collator = DataCollatorForLanguageModeling(
            tokenizer=tokenizer, mlm=False
        )

        # Configuración del entrenamiento
        training_args = TrainingArguments(
            output_dir=OUTPUT_DIR,
            per_device_train_batch_size=int(batch_size),
            num_train_epochs=int(epochs),
            learning_rate=float(learning_rate),
            logging_steps=10,
            save_total_limit=1,
            push_to_hub=False,
            report_to="none",
        )

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

        # Entrenar modelo
        trainer.train()

        # Guardar resultados
        model.save_pretrained(OUTPUT_DIR)
        tokenizer.save_pretrained(OUTPUT_DIR)

        return "✅ Entrenamiento completado con éxito. Modelo guardado en ./lora_output"
    except Exception as e:
        return f"❌ Error durante el entrenamiento: {str(e)}"

# ============================================================
# 🤖 Función de prueba del modelo entrenado
# ============================================================
def generate_text(prompt):
    try:
        generator = pipeline(
            "text-generation",
            model=OUTPUT_DIR,
            tokenizer=tokenizer,
        )
        output = generator(prompt, max_new_tokens=100, temperature=0.7, top_p=0.9)
        return output[0]["generated_text"]
    except Exception as e:
        return f"⚠️ Error al generar texto: {str(e)}"

# ============================================================
# 💻 Interfaz de usuario (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", lines=3)
        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", lines=6)
        generate_button.click(generate_text, inputs=prompt, outputs=output_box)

# ============================================================
# 🚀 Lanzar app
# ============================================================
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)