shirley / app.py
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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"]
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="gpt-4", 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, api_token):
if api_token != os.environ["API_TOKEN"]:
return 'API_TOKEN does not match'
index = GPTSimpleVectorIndex.load_from_disk('index.json')
response = index.query(input_text, response_mode="tree_summarize")
return response.response
iface = gr.Interface(fn=chatbot,
inputs=[gr.inputs.Textbox(lines=1, label="Ask Shirley"), gr.inputs.Textbox(lines=1, label="API_TOKEN")],
outputs="text",
title="Ask Shirley Anything")
index = construct_index("docs")
iface.launch()