app.py

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

# Cargar el modelo y el tokenizador
model_name = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)

def generate_response(prompt, max_length=200):
    inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            inputs, 
            max_length=max_length, 
            num_return_sequences=1,
            temperature=0.7,
            top_p=0.9,
            do_sample=True
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.strip()

def chatbot(message, history):
    history = history or []
    
    # Construir el prompt en el formato que Llama-2 espera
    prompt = "Eres un asistente AI amigable y útil. Responde de manera concisa y coherente.\n\n"
    for human, ai in history:
        prompt += f"Human: {human}\nAssistant: {ai}\n"
    prompt += f"Human: {message}\nAssistant:"
    
    response = generate_response(prompt)
    
    history.append((message, response))
    return history, history

iface = gr.Interface(
    fn=chatbot,
    inputs=["text", "state"],
    outputs=["chatbot", "state"],
    title="Tu Compañero AI con Llama-2",
    description="Un chatbot de IA avanzado utilizando el modelo Llama-2-7b-chat para conversaciones coherentes y naturales.",
)

iface.launch()