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import os
import openai
from transformers import pipeline, Conversation
import gradio as gr
import json
from dotenv import load_dotenv
# Load environment variables from the .env file de forma local
load_dotenv()
import base64
with open("Iso_Logotipo_Ceibal.png", "rb") as image_file:
encoded_image = base64.b64encode(image_file.read()).decode()
openai.api_key = os.environ['OPENAI_API_KEY']
def clear_chat(message, chat_history):
return "", []
def add_new_message(message,person, chat_history):
new_chat = []
new_chat.append({"role": "system", "content": 'Sos {} y tendr谩s que responder preguntas que te har谩n ni帽os de escuela, las respuestas tienen que ser c贸mo si hablaras con {} y con la informaci贸n de su vida. Las respuestas tienen que estar orientadas a ni帽os entre 9 y 10 a帽os.'.format(person,person)})
for turn in chat_history:
user, bot = turn
new_chat.append({"role": "user", "content": user})
new_chat.append({"role": "assistant","content":bot})
new_chat.append({"role": "user","content":message})
return new_chat
def respond(message, person, chat_history):
prompt = add_new_message(message, person, chat_history)
# stream = client.generate_stream(prompt,
# max_new_tokens=1024,
# stop_sequences=["\nUser:", "<|endoftext|>"],
# temperature=temperature)
# #stop_sequences to not generate the user answer
# acc_text = ""
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages= prompt,
temperature=0.5,
max_tokens=1000,
stream=True,
)#.choices[0].message.content
#chat_history.append((message, response))
token_counter = 0
partial_words = ""
counter=0
for chunk in response:
chunk_message = chunk['choices'][0]['delta']
if(len(chat_history))<1:
# print("entr贸 aca谩")
partial_words += chunk_message.content
chat_history.append([message,chunk_message.content])
else:
# print("antes", chat_history)
if(len(chunk_message)!=0):
if(len(chunk_message)==2):
partial_words += chunk_message.content
chat_history.append([message,chunk_message.content])
else:
partial_words += chunk_message.content
chat_history[-1] =([message,partial_words])
yield "",chat_history
with gr.Blocks() as demo:
gr.Markdown("""
<center>
<h1>
Uso de AI para un chatbot.
</h1>
<img src='data:image/jpg;base64,{}' width=200px>
<h3>
Con este espacio podr谩s hablar en formato conversaci贸n con el personaje famoso que quieras, puede ser Albert Einstein, Marie Curie o el/la que quieras!
</h3>
</center>
""".format(encoded_image))
with gr.Row():
person = gr.Textbox(label="Escrib铆 el nombre del perosnaje famoso:")
with gr.Row():
chatbot = gr.Chatbot( height=550) #just to fit the notebook
with gr.Row():
with gr.Row():
with gr.Column(scale=4):
msg = gr.Textbox(label="Texto de entrada")
with gr.Column(scale=1):
btn = gr.Button("Enviar")
clear = gr.ClearButton(components=[msg, chatbot], value="Borrar chat")
btn.click(respond, inputs=[msg,person, chatbot], outputs=[msg, chatbot])
msg.submit(respond, inputs=[msg, person,chatbot], outputs=[msg, chatbot]) #Press enter to submit
clear.click(clear_chat,inputs=[msg, chatbot], outputs=[msg, chatbot])
demo.queue()
demo.launch() |