import os
from dotenv import find_dotenv, load_dotenv
import streamlit as st
from typing import Generator
from groq import Groq
# Cargar variables de entorno
_ = load_dotenv(find_dotenv())
# Configurar la página de Streamlit
st.set_page_config(page_icon="📃", layout="wide", page_title="Groq & LLaMA3.1 Chat Bot...")
# Menú superior con fondo transparente
st.markdown(
"""
""",
unsafe_allow_html=True
)
# Inicializar cliente Groq
client = Groq(
api_key=os.environ['GROQ_API_KEY'],
)
# Inicializar historial de chat y modelo seleccionado
if "messages" not in st.session_state:
st.session_state.messages = []
if "selected_model" not in st.session_state:
st.session_state.selected_model = "mixtral-8x7b-32768"
# Detalles del modelo
models = {
"mixtral-8x7b-32768": {
"name": "Mixtral-8x7b-Instruct-v0.1",
"tokens": 32768,
"developer": "Mistral",
},
}
# Configurar el modelo y tokens
model_option = "mixtral-8x7b-32768"
max_tokens_range = models[model_option]["tokens"]
# No mostrar la selección del modelo ni la barra de tokens
st.session_state.max_tokens = max_tokens_range
# Detectar cambio de modelo y limpiar historial de chat si el modelo ha cambiado
if st.session_state.selected_model != model_option:
st.session_state.messages = []
st.session_state.selected_model = model_option
# Añadir un botón para "Limpiar Chat"
if st.button("Limpiar Chat"):
st.session_state.messages = []
# Cargar la imagen del avatar del asistente
assistant_avatar = "botm.png"
# Mostrar mensajes de chat del historial en la aplicación
for message in st.session_state.messages:
avatar = assistant_avatar if message["role"] == "assistant" else "🧑💻"
with st.chat_message(message["role"], avatar=avatar):
st.markdown(message["content"])
def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
"""Generar contenido de respuesta del chat a partir de la respuesta de la API de Groq."""
for chunk in chat_completion:
if chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
# Instrucción privada que se aplicará a cada mensaje
private_instruction = (
"# Extract the benefits of the product, not the features. # You should be as brief as possible. # Omit the price, if any. # Do not mention the name of the product. # Use 3 paragraphs. # Try to synthesize or summarize. # Focus only on the benefits. # Highlight how this product helps the customer. # Always respond in Spanish. # The text you create will be used in an e-commerce product sales page through the Internet, so it must be persuasive, attractive, and above all very short and summarized. # Remember to keep the text short, summarized, synthesized in three paragraphs. # Surprise me with your best ideas! # Always answers in AMERICAN SPANISH. Stop after finish the first content genreated."
)
# Manejar la entrada del chat del usuario
if prompt := st.chat_input("Escribe tu mensaje aquí..."):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user", avatar="🧑💻"):
st.markdown(prompt)
# Preparar los mensajes para la API, incluyendo la instrucción privada
messages_for_api = [
{"role": "system", "content": private_instruction},
] + [
{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages
]
# Obtener respuesta de la API de Groq
try:
chat_completion = client.chat.completions.create(
model=model_option,
messages=messages_for_api,
max_tokens=max_tokens_range,
stream=True,
)
# Usar la función generadora con st.write_stream
with st.chat_message("assistant", avatar=assistant_avatar):
chat_responses_generator = generate_chat_responses(chat_completion)
full_response = st.write_stream(chat_responses_generator)
# Añadir la respuesta completa al historial de mensajes
if isinstance(full_response, str):
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
else:
combined_response = "\n".join(str(item) for item in full_response)
st.session_state.messages.append(
{"role": "assistant", "content": combined_response}
)
except Exception as e:
st.error(e, icon="❌")