avelican

t -> the
o -> of
a -> and
i -> in
to -> to
w -> was
is -> is
f -> for
on -> on
as -> as

avelican

import json

# word_frequency.txt contains one word per line, most common words first
with open("word_frequency.txt","r",encoding="utf-8") as f: words = f.read().splitlines()

wrds = set()
wrds_list = []
wrds_learn = []

for word in words:

avelican

from semiauto import search, load, summarize, save

"""
Run Query
Extract links
for each link:
	get text, title etc
	summarize text based on goal (extract desired info)
		NOTE: need to build a recursive summarizer
		      splitting based on paragraphs (+ a few extra before and after) will give superior results.

avelican

# NOTE: Ensure folders 'input' and 'output' exist in script folder.

import os
import openai
import sys

openai.api_key = os.getenv("OPENAI_API_KEY")

OPENAI_MODEL="text-davinci-003"
# OPENAI_MODEL="text-curie-001"

avelican

import os
import openai
import sys
from pdfminer.high_level import extract_text # pip install pdfminer.six

openai.api_key = os.getenv("OPENAI_API_KEY")

OPENAI_MODEL="text-davinci-003"
# OPENAI_MODEL="text-curie-001"
# OPENAI_MODEL="text-babbage-001"

avelican

You are part of an automated system. You must respond only in JSON.
If you need more information about a file, function or class, use one of the "read" actions first.
If you use a read action, do not use any write actions yet, because you will receive a response with the result of the read action.
This will allow you to make better decisions.
If you use one of the "replace" methods, e.g. replace_function_body, you must include the WHOLE function in the content field. Do not omit code: the project will be corrupted!
If you are unable to complete the task with the available actions, use the talk action to explain why. We will use this feedback to improve the system.

Read Actions:
file_read
read_function

avelican

import os
import openai

prompt = "I'm a busy CEO, please summarize the following in 10-20 bullet points. Include only the most important, useful or interesting information.\n\n"
# model = 'gpt-3.5-turbo' # 'gpt-4' # moved to gpt_chat_completion since we use both
CONTEXT_SIZE = 4096 # 2048 for GPT-3 (as half the input.
# CONTEXT_SIZE = 1024 # tiny context for testing
MAX_TOKENS_IN = int(CONTEXT_SIZE*2/3)
MAX_TOKENS_OUT = CONTEXT_SIZE - MAX_TOKENS_IN

avelican

import sys
import subprocess

if len(sys.argv) < 2:
	print("Please provide a YouTube video URL as the first argument.")
	sys.exit(1)

video_url = sys.argv[1]

command = [

avelican

# import openai

import os
import asyncio
import aiohttp

import random

OPENAI_API_KEY = os.environ['OPENAI_API_KEY']

avelican

# credit goes to https://github.com/synesthesiam/magicpy

# Instructions: Texture image MUST be as tall as depth map (taller should work too), 
# and 1/8th of its width (or thereabouts -- any width will work but might not look too good).

from PIL import Image

if __name__ == '__main__':

    depth_file = "depth.jpg"