app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Cargar modelo y tokenizer
model_name = "google/flan-t5-base"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def get_system_prompt():
    with open("prompt.txt", "r", encoding="utf-8") as f:
        return f.read().strip()

def generate_response(user_input):
    system_prompt = get_system_prompt()
    full_prompt = f"{system_prompt}\n\nUsuario: {user_input}\nBITER:"

    inputs = tokenizer(full_prompt, return_tensors="pt")
    output = model.generate(**inputs, max_new_tokens=200)

    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
    return decoded_output.strip()

# Interfaz Gradio para probar el modelo directamente en Hugging Face
demo = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=2, placeholder="Escribe tu pregunta..."),
    outputs=gr.Textbox(),
    title="BITER - Mentor IA para Emprendedores",
    description="Respuestas rápidas, estratégicas y en español. Como un CEO que te asesora al instante.",
)

if __name__ == "__main__":
    demo.launch()