# 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=0.7, model_name="text-davinci-003", max_tokens=num_outputs)) documents = SimpleDirectoryReader(directory_path).load_data() index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) index.save_to_disk('index.json') return index def chatbot(input_text): index = GPTSimpleVectorIndex.load_from_disk('index.json') response = index.query(input_text, response_mode="compact") return response.response description = """