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# 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()
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