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import gradio as gr
import plotly.express as px
from openai import OpenAI
# Configura el cliente de OpenAI con la API key directamente
client = OpenAI(api_key="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")
# ID de tu asistente
assistant_id = "asst_0hq3iRy6LX0YLZP0QVzg17fT"
def random_plot():
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
size='petal_length', hover_data=['petal_width'])
return fig
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def add_message(history, message):
if message["text"] is not None:
history.append((message["text"], None))
return history, gr.MultimodalTextbox(value=None, interactive=False)
def bot(history):
last_message = history[-1][0] if history else "Hola"
# Crear un nuevo hilo para la conversación
thread = client.beta.threads.create()
# Agregar el mensaje del usuario al hilo
client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content=last_message
)
# Ejecutar el asistente
run = client.beta.threads.runs.create(
thread_id=thread.id,
assistant_id=assistant_id
)
# Esperar a que el asistente complete la tarea
while run.status != "completed":
run = client.beta.threads.runs.retrieve(thread_id=thread.id, run_id=run.id)
# Obtener los mensajes del hilo
messages = client.beta.threads.messages.list(thread_id=thread.id)
# Obtener la última respuesta del asistente
for message in messages.data:
if message.role == "assistant":
bot_response = message.content[0].text.value
break
history[-1] = (history[-1][0], bot_response)
return history
fig = random_plot()
with gr.Blocks(fill_height=True) as demo:
chatbot = gr.Chatbot(
elem_id="chatbot",
bubble_full_width=False,
scale=1,
)
chat_input = gr.MultimodalTextbox(interactive=True,
file_count="multiple",
placeholder="Enter message or upload file...", show_label=False)
chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
chatbot.like(print_like_dislike, None, None)
demo.queue()
demo.launch(share=True) |