app.py

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(
 """
 <style>
 .menu-container {
 padding: 20px;
 background-color: transparent; /* Fondo transparente */
 border-bottom: 1px solid #e1e1e1;
 }
 .menu-title {
 font-size: 24px;
 font-weight: bold;
 margin-bottom: 10px;
 }
 .menu-description {
 line-height: 1.5;
 }
 .menu-description a {
 color: #1f77b4;
 text-decoration: none;
 }
 .menu-description a:hover {
 text-decoration: underline;
 }
 </style>
 <div class="menu-container">
 <p class="menu-title">Bot con I.A. para crear BENEFICIOS de productos.</p>
 <p class="menu-description">
 Estos BENEFICIOS van en la descripcion LARGA de producto (En la parte de ARRIBA).<br><br>
 Si desea usar otro BOT de I.A. escoja:<br>
 <a href='https://magnetimpact-mc-bot.hf.space'>Marketing de Contenidos |</a>
 <a href='https://magnetimpact-tit-bot.hf.space'> Creacion de TITULOS |</a>
 <a href='https://magnetimpact-dp-bot.hf.space'> Descripcion de Productos |</a>
 <a href='https://magnetimpact-cp-bot.hf.space'> Caracteristicas de Productos |</a>
 <a href='https://wa.me/51927929109'> Desarrollado por MAGNET IMPACT - Agencia de Marketing Digital</a>
 </p>
 </div>
 """,
 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="❌")