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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...") | |
def icon(emoji: str): | |
"""Muestra un emoji como ícono de página estilo Notion.""" | |
st.write( | |
f'<span style="font-size: 78px; line-height: 1">{emoji}</span>', | |
unsafe_allow_html=True, | |
) | |
# Encabezado de la aplicación | |
st.subheader("Groq Chat with LLaMA3.1 App", divider="rainbow", anchor=False) | |
# 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 = None | |
# Detalles de los modelos | |
models = { | |
"llama-3.1-70b-versatile": {"name": "LLaMA3.1-70b", "tokens": 4096, "developer": "Meta"}, | |
"llama-3.1-8b-instant": {"name": "LLaMA3.1-8b", "tokens": 4096, "developer": "Meta"}, | |
"llama3-70b-8192": {"name": "Meta Llama 3 70B", "tokens": 4096, "developer": "Meta"}, | |
"llama3-8b-8192": {"name": "Meta Llama 3 8B", "tokens": 4096, "developer": "Meta"}, | |
"llama3-groq-70b-8192-tool-use-preview": {"name": "Llama 3 Groq 70B Tool Use (Preview)", "tokens": 4096, "developer": "Groq"}, | |
"gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 4096, "developer": "Google"}, | |
"mixtral-8x7b-32768": { | |
"name": "Mixtral-8x7b-Instruct-v0.1", | |
"tokens": 32768, | |
"developer": "Mistral", | |
}, | |
} | |
# Diseño para la selección de modelo y slider de tokens | |
col1, col2 = st.columns([1, 3]) # Ajusta la proporción para hacer la primera columna más pequeña | |
with col1: | |
model_option = st.selectbox( | |
"Choose a model:", | |
options=list(models.keys()), | |
format_func=lambda x: models[x]["name"], | |
index=0, # Predeterminado al primer modelo en la lista | |
) | |
max_tokens_range = models[model_option]["tokens"] | |
max_tokens = st.slider( | |
"Max Tokens:", | |
min_value=512, | |
max_value=max_tokens_range, | |
value=min(32768, max_tokens_range), | |
step=512, | |
help=f"Adjust the maximum number of tokens (words) for the model's response. Max for selected model: {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("Clear Chat"): | |
st.session_state.messages = [] | |
# Mostrar mensajes de chat del historial en la aplicación | |
for message in st.session_state.messages: | |
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, | |
stream=True, | |
) | |
# Usar la función generadora con st.write_stream | |
with st.chat_message("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="❌") | |