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, questions_guide, chat_history):
     new_chat = []
     
     new_chat.append({"role": "system", "content": '{}'.format(questions_guide)})
   
     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, questions_guide, chat_history):
    prompt = add_new_message(message, questions_guide, 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-4-0125-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 = "" 

    counter=0
    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


with gr.Blocks() as demo:
    gr.Markdown("""
    <center>
    <img src='data:image/jpg;base64,{}' width=200px>
    <h3>
    Este espacio permite generar preguntas sobre el texto de referencia.
    </h3>
    </center>
    """.format(encoded_image))
    with gr.Row():
        questions_guide = gr.Textbox(label="Indicar aquí la guía para generar las preguntas:", value="En base al texto o novela que recibas como entrada, deberás generar preguntas orientadas para estudiantes escolares entre 8 y 12 años. La idea es que las preguntas sirvan para evaluar la comprensión lectora de los estudiantes. Debes generar 10 preguntas múltiple opción, en orden creciente de dificultad, donde cada una de ellas tiene tres opciones y la opción correcta debe estar indicada con una X al comienzo.")
    with gr.Row():
        msg = gr.Textbox(label="Pegar aquí el texto de referencia:")
    with gr.Row():
        with gr.Column(scale=4):        
            chatbot = gr.Chatbot(label="Resultado:",height=150, show_copy_button=True) #just to fit the notebook
        with gr.Column(scale=1):
            btn = gr.Button("Enviar")
            clear = gr.ClearButton(components=[msg, chatbot], value="Borrar resultado.")                

    btn.click(respond, inputs=[msg, questions_guide, chatbot], outputs=[msg, chatbot])
    msg.submit(respond, inputs=[msg, questions_guide, chatbot], outputs=[msg, chatbot]) #Press enter to submit
    clear.click(clear_chat,inputs=[msg, chatbot], outputs=[msg, chatbot])
demo.queue()
demo.launch()