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import streamlit as st
import yaml
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
class ModelManager:
def __init__(self, model_name="microsoft/Phi-4-mini-instruct"):
# Diccionario de modelos: usa los identificadores de Hugging Face
self.models = {
"microsoft/Phi-4-mini-instruct": "microsoft/Phi-4-mini-instruct",
"microsoft/Phi-4-multimodal": "microsoft/Phi-4-multimodal",
"meta-llama/Llama-3.3-70B-Instruct": "meta-llama/Llama-3.3-70B-Instruct"
}
self.current_model_name = model_name
self.tokenizer = None
self.model = None
self.load_model(model_name)
def load_model(self, model_name):
self.current_model_name = model_name
model_path = self.models[model_name]
st.info(f"Cargando modelo: {model_name} ...")
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.model = AutoModelForCausalLM.from_pretrained(model_path)
def generate(self, prompt, max_length=50, temperature=0.7):
inputs = self.tokenizer(prompt, return_tensors="pt")
outputs = self.model.generate(inputs["input_ids"], max_length=max_length, temperature=temperature)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
def switch_model(self, model_name):
if model_name in self.models:
self.load_model(model_name)
else:
raise ValueError(f"El modelo {model_name} no est谩 disponible.")
@st.cache_data
def load_prompts():
with open("prompt.yml", "r", encoding="utf-8") as f:
prompts = yaml.safe_load(f)
return prompts
def main():
st.title("Switcher de Modelos de Transformers")
# Cargar configuraci贸n de prompts
prompts_config = load_prompts()
# Selecci贸n de modelo desde la barra lateral
st.sidebar.title("Selecci贸n de Modelo")
model_choice = st.sidebar.selectbox("Selecciona un modelo", list(prompts_config.keys()))
# Instanciar el manejador de modelos
model_manager = ModelManager(model_name=model_choice)
# Obtener el prompt de estilo para el modelo seleccionado
style_prompt = prompts_config.get(model_choice, prompts_config.get("default_prompt", ""))
st.write(f"**Modelo en uso:** {model_choice}")
# 脕rea de texto para ingresar el prompt, iniciando con el estilo predefinido
user_prompt = st.text_area("Ingresa tu prompt:", value=style_prompt)
max_length = st.slider("Longitud m谩xima", min_value=10, max_value=200, value=50)
temperature = st.slider("Temperatura", min_value=0.1, max_value=1.0, value=0.7)
if st.button("Generar respuesta"):
result = model_manager.generate(user_prompt, max_length=max_length, temperature=temperature)
st.text_area("Salida", value=result, height=200)
if __name__ == "__main__":
main()
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