# coding=utf8
from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import gradio as gr
import sys
import os

os.environ["OPENAI_API_KEY"] = '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'

def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600

    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.0, model_name="text-davinci-003", max_tokens=num_outputs))

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTSimpleVectorIndex.from_documents(documents)
    index.save_to_disk('index.json')

    return index


def chatbot(input_text):
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(input_text)
    return response.response

description = """

<center>Olá sou a Zoh, fui treinada para responder perguntas com base das informações do Hippo Supermercados. Pergunte qualquer coisa. Caso eu não saiba, estarei aprendendo.
<img src="https://s3.amazonaws.com/enlizt-resources-prod/companies/10958750-6306-11ea-b31c-2b332181af51_256_avatar?nocache=1588599205314" width=200px></center>
"""

iface = gr.Interface(fn=chatbot,
                     inputs=gr.inputs.Textbox(lines=3, label='O quê gostaria de saber?') ,
                     outputs=gr.inputs.Textbox(lines=3, label="Veja o que encontrei"),
                     description=description,
                     css=".gradio-container-3-23-0 {background-color: #5f0000} .gradio-container-3-23-0 .prose * {color: #ffffff}",
                     title="CD2 IA")

index = construct_index("docs")
iface.launch()