app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# --- Modelo español entrenado para chat ---
MODEL_NAME = "PlanTL-GOB-ES/gpt2-base-bne"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)

# --- Función principal del chatbot ---
def answer(history, message):
    if not message.strip():
        return history, ""

    # Prompt inicial para guiar la conversación
    system_prompt = (
        "Eres un asistente virtual que siempre responde en español de forma lógica y natural. "
        "No hagas listas ni repitas palabras innecesariamente.\n"
    )

    context = system_prompt
    for user, bot in history[-6:]:
        context += f"Usuario: {user}\nIA: {bot}\n"
    context += f"Usuario: {message}\nIA:"

    output = generator(
        context,
        max_new_tokens=80,
        do_sample=True,
        top_k=20,
        top_p=0.8,
        temperature=0.7
    )[0]["generated_text"]

    # Extraer la respuesta
    if "IA:" in output:
        response = output.split("IA:")[-1].strip()
    else:
        response = output

    history.append((message, response))
    return history, ""

# --- Interfaz Gradio ---
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 Chatbot en Español - Coherente")
    chat = gr.Chatbot()
    msg = gr.Textbox(placeholder="Escribe tu mensaje…")
    clear_btn = gr.Button("Limpiar")
    state = gr.State([])

    msg.submit(answer, [state, msg], [chat, msg])
    clear_btn.click(lambda: [], None, chat)

demo.launch()