app.py

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-3.5-turbo",
        messages= prompt,
        temperature=0.5,
        max_tokens=120
        ).choices[0].message.content
    chat_history.append((message, response))
    return "",chat_history
    #Streaming the tokens
    # for idx, response in enumerate(stream):
    #         text_token = response.token.text

    #         if response.details:
    #             return

    #         if idx == 0 and text_token.startswith(" "):
    #             text_token = text_token[1:]

    #         acc_text += text_token
    #         last_turn = list(chat_history.pop(-1))
    #         last_turn[-1] += acc_text
    #         chat_history = chat_history + [last_turn]
    #         yield "", chat_history
    #         acc_text = ""

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 podrás hablar en formato conversación con ChatGTP!
    </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.launch()