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from transformers import pipeline, Conversation
import gradio as gr
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()
# chatbot = pipeline(model="microsoft/DialoGPT-medium")
# conversation = Conversation("Hi")
# response = chatbot(conversation)
# #conversation.mark_processed()
# #conversation.append_response(response)
# conversation.add_user_input("How old are you?")
# conversation2 = chatbot(conversation)
# print(conversation2)
# def respond(text, conversation):
# chatbot = pipeline(model="microsoft/DialoGPT-medium")
# if len(conversation)==0:
# conversation = Conversation(text)
# conversation = chatbot(conversation)
# print(conversation.iter_texts())
# # test = []
# # for user,text in conversation.iter_texts():
# return text, conversation.iter_texts()
# else:
# conversation.add_user_input(text)
# conversation = chatbot(conversation)
# return text, conversation.iter_texts()
import os
import openai
openai.api_key = os.environ['OPENAI_API_KEY']
def clear_chat(message, chat_history):
return "", []
def add_new_message(message,chat_history):
new_chat = []
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, chat_history):
prompt = add_new_message(message, 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-4-1106-preview",
messages= prompt,
temperature=0.5,
max_tokens=1000,
stream = True
)#.choices[0].message.content
# chat_history.append((message, response))
token_counter = 0
partial_words = ""
for chunk in response:
chunk_message = chunk['choices'][0]['delta']
if(len(chat_history))<1:
# print("entró acaá")
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
# return "",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>
En este espacio podrás interactuar con ChatGPT y su modelo GPT4!
</h3>
</center>
""".format(encoded_image))
with gr.Row():
chatbot = gr.Chatbot() #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, chatbot], outputs=[msg, chatbot])
msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot]) #Press enter to submit
clear.click(clear_chat,inputs=[msg, chatbot], outputs=[msg, chatbot])
demo.queue(concurrency_count=4)
demo.launch() |