JupyterJones

#!/usr/bin/python3
import sys
import sqlite3
DATABASE = "notes.db"
conn = sqlite3.connect(DATABASE)
from datetime import datetime
conn.text_factory = str
c = conn.cursor()
if len(sys.argv) < 3:
     print ("\n******* NOTE - Notes Editor ******")

JupyterJones

import subprocess
import numpy as np
import logging
import glob
import random
import uuid
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')

def zoom_pan_nonlinear1(input_path, output_path):

JupyterJones

JupyterJones

import logging
from moviepy.editor import VideoFileClip, VideoClip, concatenate_videoclips
from PIL import Image
import numpy as np
from sklearn.cluster import KMeans
import random
import os

# Configure logging
logging.basicConfig(filename='script_log.log', level=logging.DEBUG)

JupyterJones

from PIL import Image, ImageFilter
import os
import cv2
import random
import time
path = r"/home/jack/Desktop/HDD500/SCRIPTS/UGLY_DOLLS/"
#path = r"crawler4/"
base_image = random.choice([
    x for x in os.listdir(path)

JupyterJones

#!/home/jack/miniconda3/envs/cloned_base/bin/python
import os
import subprocess
import random
from sys import argv
import uuid
import shutil
def diag_trans(DIR,output_file):
    image_dir=DIR
    # Get a list of image file names in the directory

JupyterJones

from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip
import random
import glob

def add_title_image(video_path, title_image_path, output_path):
    # Load the video file and title image
    video_clip = VideoFileClip(video_path)
    print(video_clip.size)
    width, height = video_clip.size

JupyterJones

#!/bin/bash

# Define the paths to your input videos
video1='0001.mp4'
video2='0002.mp4'
video3='0003.mp4'
output='real-fish_123.mp4'

# Create a text file to list the input videos
echo "file '$video1'" > input.txt

JupyterJones

#!/bin/bash
convert -size 512x666 -background black -fill white -font /home/jack/fonts/Arimo-Regular.ttf -pointsize 30 -gravity center caption:@topic.txt -set delay 4 -loop 0 -coalesce -layers Optimize topic.png

JupyterJones

import argparse

from pdfminer.high_level import extract_text
from sentence_transformers import SentenceTransformer, CrossEncoder, util

from text_generation import Client

PREPROMPT = "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n"
PROMPT = """"Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to